Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text

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语言学超精简1

Chapter 1 INTRODUCTIONSession ALanguage is a system of arbitrary vocal symbols used for human communication.Language is a means of verbal communication. It is instrumental in that communicating by speaking or writing is a purposeful act.It is social and conventional in that language is a social semiotic and communication can only take place effectively if all the users share a broad understanding of human interaction including such associated factors as nonverbal cues, motivation, and socio-cultural roles. Language learning and use are determined by the intervention of biological, cognitive, psychosocial, and environmental factors.Other definitions：Language is a symbolic form of communication that involves, on the one hand, the comprehension of words and sentences and, on the other, the expression of feelings, thoughts, and ideas. The basic units of language are phonemes, morphemes, and words.from Encyclopedia BritannicaLanguage is the systematic communication by vocal symbols. It is a universal characteristic of the human species.Nothing is known of its origin, although scientists have identified a gene that clearly contributes to the human ability to use language. Scientists generally hold that it has been so long in use that the length of time writing is known to have existed (7,900 years at most) is short by comparison. Just as languages spoken now by peoples of the simplest cultures are as subtle and as intricate as those of the peoples of more complex civilizations, similarly the forms of languages known (or hypothetically reconstructed) from the earliest records show no trace of being more ―primitive‖ than their modern forms.from The Columbia Electronic Encyclopedia人类特有的一种符号系统。

一起作业八年级上册英语听力材料

一起作业八年级上册英语听力材料全文共3篇示例，供读者参考篇1My 8th Grade English Listening OdysseyI still vividly remember the start of 8th grade English class. Our teacher, Mrs. Thompson, was renowned for being strict but brilliant at helping us improve our listening skills. On the first day, she announced we would be doing listening exercises from a special textbook series every week. I gulped, knowing English listening had always been my biggest struggle."Good listening comprehension is the foundation for mastering any language," Mrs. Thompson declared sternly. "Which is why we will prioritize it this year through focused practice."The listening textbook was thick and imposing, with 30 units spanning topics from everyday conversations to historic speeches. Each unit contained 3-4 recorded passages followed by comprehension questions. The passages were definitely challenging, filled with idioms, advanced vocabulary, and thickaccents I could barely understand at first. Wow, this was going to be tough.To make matters worse, Mrs. Thompson frequently had us do "cold listening" where we faced passages without any preparation on the topic or vocabulary. "Just like in the real world, you can't always know what people will say ahead of time," she lectured. Those cold listening days filled me with dread.However, I had to admit Mrs. Thompson's approach worked wonders. By consistently exposing us to all those diverse passages, slowly but surely, my ears became attuned to understanding accents, idioms, and speech at natural speeds. Vocabulary I initially struggled with, like "take a rain check" or "let the cat out of the bag", became second nature after hearing them repeatedly in context.Some of the listening exercises were gruelingly long, like extracting details from a 20-minute lecture about the Industrial Revolution. Those really tested my stamina and focus. But I found a nice routine - taking meticulous notes as I listened, then rapidly reviewing them at the passage's end before answering the questions.Other exercise types included listening to conversations and identifying the key speakers' intentions, relationships, or emotional states based on tone and wording. Those were my favorite, allowing me to apply reasoningskills beyond just rote comprehension. I started seeing dramatic improvements in my conversational listening abilities too.Another memorable exercise type involved listening to passages with pecise vocabulary omitted, having to determine the missing word from context. For example: "The _____ candidate struggled to convey his political vision effectively." See if you can fill in that blank! These forced me to use linguistic cues and grasp holistic meaning rather than just decodingword-for-word.As the year progressed, I no longer dreaded Mrs. Thompson's listening assignments. In fact, I came to savor the chance to challenge my ears each week with new diverse materials spanning everything from historical documentaries to TED Talks to talk show clips. I'd put on my big headphones, listen intently, and see how much I could comprehend through active focus.Occasionally, we'd get to pick our own supplemental listening materials from approved sources. I personally enjoyedanalyzing song lyrics, movie dialogue, and podcasts this way. For poetry analysis, we'd linger over sound devices and delivery nuances. For movies, we'd dissect how vocal tones conveyed different emotional substrates. Such engaging topics kept me hungering to hone my listening prowess further.By spring, I could perceive my listening abilities having leapfrogged immensely compared to where I started 8th grade. Casual conversations I once found impenetrably fast I now smoothly followed. Subtle sarcasm and humor cues I missed previously became clear through refined listening. When friends spoke Thai around me, I could sometimes comprehend a few words through my sharpened skill at sound discrimination.In the final unit's culminating exercise, Mrs. Thompson had us transcribe verbatim a fascinating but outrageously convoluted lecture about the philosophy of time. Afterwards, in a crowning moment, she revealed the average comprehension score was 94% - a stellar result highlighting how finely-tuned our listening abilities had become in just one school year. I grinned, feeling immensely proud of the progress my own ears had made on this unlikely yet invaluable language journey.Looking ahead to high school, I know my struggles mastering listening will be crucial preparation for advancedEnglish communication, comprehension, and vocabulary acquisition. All thanks to the rigor of Mrs. Thompson's methodical, diverse, and wondrously stretching listening curriculum in 8th grade. My ears were eternally transformed from a hodge-podge of half-comprehension into f230fined listening machines. I foresee fgarming fhose finguistic fuperpowers will allow me to freach fhigher fhastic fheights!篇2The Challenges of 8th Grade English Listening ComprehensionAs an 8th grader, one of the biggest academic hurdles I face is the listening comprehension section of our English course. While reading and writing have their own difficulties, there's something uniquely frustrating about having to understand spoken English at native speeds. The listening materials we cover in class and for homework present a constant uphill battle.To begin with, the accents we encounter are incredibly diverse. Our textbooks and audio files feature speakers from the United States, United Kingdom, Australia, India, and more. Even within those countries and regions, there is tremendous variety in pronunciation, cadence, and speaking styles. One minute Imight be listening to a middle-aged British man with a posh Received Pronunciation accent, and the very next excerpt could be a young American valley girl from California. Keeping up with the rapid shifts in accents and speech patterns is mentally exhausting.Compounding the accent challenges is the sheer speed at which native English speakers talk. Despite my years of English education, those listening to their first language always seem to be going a mile a minute to my ears. I find myself constantly rewinding audio clips, straining to catch every word and idiom. By the time I decipher one sentence, the next has already whizzed by in a blur of unintelligible sounds. English speakers also have a pesky tendency to slur words together and drop consonants and vowels, further muddying my comprehension.The subject matter itself also increases the difficulty level of the 8th grade listening materials. While the readings in our textbooks cover relatively simple narratives and descriptive texts, the listening passages delve into far more complex topics. We've had to comprehend excerpts about historical events, scientific discoveries, current event analyses, and even clips from university lectures. The advanced vocabulary and intricateconcepts in these passages make it twice as hard to simultaneously decode meaning and unfamiliar accents.Even something as seemingly simple as a casual conversation between native speakers becomes an academic obstacle course. The back-and-forth flow of dialogue, constant interjections and interruptions, idioms, slang, and cultural references all coalesce into an impenetrable linguistic jungle. What's meant to be練習の対話は、第二の自然言語のように見えるようにほとんどあまりにも簡単にでき過ぎます。

写一篇关于表情符号的英语作文

写一篇关于表情符号的英语作文全文共3篇示例，供读者参考篇1The Rise of the Emojis: Exploring the Impact of Digital Pictograms on Modern CommunicationHave you ever found yourself unable to fully express your thoughts and emotions through mere words? In today's digital age, the limitations of traditional text-based communication have become increasingly apparent. Enter the world of emojis –those delightful little pictograms that have taken the virtual realm by storm. From the classic smiley face to the playful unicorn, emojis have become an integral part of our digital vocabulary, transcending language barriers and adding a touch of personality to our conversations.As a student, I can attest to the profound impact emojis have had on my daily interactions. Whether it's exchanging messages with friends, collaborating on group projects, or even communicating with professors, these tiny symbols have proven to be invaluable tools for conveying nuanced emotions and adding a dash of humor to our exchanges.One of the most remarkable aspects of emojis is their universality. In a world where language barriers can often hinder effective communication, emojis have emerged as a universal language of their own. A simple smiley face or a thumbs-up emoji can convey joy, approval, or encouragement, transcending cultural and linguistic boundaries. This has proven particularly useful in academic settings, where students from diverse backgrounds can connect and collaborate more seamlessly.Moreover, emojis have the power to inject personality and emotion into otherwise sterile digital conversations. In the realm of text messaging, where tone and body language are often lost, emojis serve as a visual cue, helping to convey the intended sentiment behind our messages. A winking emoji can add a touch of playfulness, while a crying emoji can express empathy or shared sadness. This emotional depth not only enhances communication but also fosters stronger connections and understanding among peers.However, it's important to note that the use of emojis should be approached with a degree of caution and consideration. While they can add color and personality to our interactions, overusing or misusing emojis can lead to misunderstandings or even come across as unprofessional in certain contexts. Asstudents, it's crucial to strike a balance and ensure that our use of emojis is appropriate for the situation at hand.Furthermore, the ever-expanding library of emojis has opened up new avenues for self-expression and representation. With the introduction of more diverse and inclusive emojis, individuals from various backgrounds can find symbols that resonate with their identities and experiences. This not only fosters a sense of belonging but also promotes greater empathy and understanding within our academic communities.Beyond their communicative value, emojis have also found their way into popular culture, art, and even literature. From emoticon-inspired art installations to novels written entirely in emoji form, these digital pictograms have transcended their original purpose and become a medium for creative expression in their own right.As we look to the future, it's clear that emojis will continue to evolve and shape the way we communicate. With the advent of new technologies such as augmented reality and virtual environments, we may witness the emergence of even more dynamic and interactive forms of emoji communication. Imagine being able to convey complex emotions and ideas throughimmersive, animated emojis that respond to our gestures and facial expressions.In conclusion, the rise of emojis has undoubtedly transformed the landscape of digital communication, offering a unique and expressive way for students like myself to connect, collaborate, and express ourselves in the virtual realm. While they may have started as simple pictograms, emojis have evolved into a powerful tool for bridging cultural divides, fostering emotional intelligence, and adding a touch of personality to our daily interactions. As we navigate theever-changing digital world, let us embrace the power of emojis and continue to explore new and innovative ways to communicate and connect with one another. ✨篇2The Evolution of Expression: Emojis and Their Impact on Modern CommunicationYou know that feeling when you can't quite find the right words to convey your emotions or thoughts? That's where emojis come in handy! These little pictorial icons have become an integral part of our digital communication, adding a touch of personality and expression to our text-based interactions. As astudent navigating the realms of social media, messaging apps, and online discussions, I can attest to the profound impact emojis have had on how we communicate in the modern age.Before we delve into the significance of emojis, let's take a moment to appreciate their humble beginnings. The story of emojis can be traced back to the late 1990s when a Japanese artist named Shigetaka Kurita created a set of 176 simple pictograms for a mobile internet platform. These early emojis were designed to convey basic emotions and concepts in a compact and visually appealing manner. Little did Kurita know that his creation would spark a global phenomenon that would revolutionize how we express ourselves digitally.Fast forward to today, and emojis have become a universal language transcending cultural boundaries. With a diverse array of facial expressions, objects, symbols, and even animated characters, emojis offer a rich tapestry of visual cues that can enhance the context and nuance of our messages. Whetherwe're expressing joy with a smiley face , conveying sadness with a tearful emoji , or simply adding a touch of humor with a playful wink , these tiny icons have become indispensable tools in our digital communication arsenal.One of the most fascinating aspects of emojis is their ability to bridge gaps in communication. In a world where people from diverse backgrounds and cultures interact online, emojis serve as a common ground, helping to convey emotions and ideas that might otherwise be lost in translation. A simple thumbs-up emoji can convey approval and encouragement, while a heart emoji ❤️ can express love and affection – all without the need for complex linguistic explanations.But emojis are more than just cute pictograms; they have also become powerful tools for self-expression and identity representation. From the introduction of diverse skin tones to the inclusion of same-sex couple emojis and gender-inclusive options, the ever-expanding emoji universe reflects the richness and diversity of our global society. By embracing and celebrating these inclusive representations, we foster a more inclusive and accepting digital environment.Moreover, emojis have found their way into various facets of our lives, from advertising and marketing campaigns to educational settings and even legal proceedings. Brands have leveraged the power of emojis to connect with their target audiences in a relatable and engaging manner, while educatorshave explored the use of emojis as a pedagogical tool to enhance student engagement and comprehension.However, as with any form of communication, there are potential pitfalls and misunderstandings associated with emoji usage. The subjective nature of interpreting visual symbols can lead to miscommunication, especially when cultural or contextual nuances are involved. Additionally, the overreliance on emojis in certain situations, such as professional or formal settings, may be perceived as unprofessional or inappropriate.Despite these challenges, the impact of emojis on our communication cannot be understated. They have transformed the way we express ourselves online, adding depth, personality, and emotional resonance to our digital interactions. As we continue to navigate the ever-evolving landscape of technology and communication, it's likely that emojis will continue to evolve and adapt, reflecting the changing needs and preferences of our global society.In conclusion, emojis have become an integral part of our digital vernacular, transcending linguistic and cultural barriers to bring us closer together through shared visual expressions. From conveying complex emotions to fostering inclusivity and representation, these tiny icons have left an indelible mark onhow we communicate in the modern age. As students and digital natives, it's our responsibility to embrace and utilize emojis responsibly, recognizing their power to enhance our interactions while remaining mindful of the potential for misinterpretation. Ultimately, emojis are a testament to the human need forself-expression and connection, reminding us that even in a world dominated by technology, we still crave the warmth and authenticity of personal expression.篇3The Rise of the Emoji: How Pictographs Took Over Digital CommunicationYou know that feeling when words just aren't enough to convey the full depth of your emotions? When you're texting your best friend about that cute guy you saw at the coffee shop, or venting to your roommate about the ridiculous amount of homework you have this week, and regular language starts to feel lacking? That's where emojis come in. Those tiny little pictographs have become integral to how we communicate digitally in the 21st century.At this point, it's hard to imagine a world without emojis. They've infiltrated pretty much every digital messaging platformand social media app out there. But it wasn't too long ago that the idea of using pictures to convey emotions and ideas in our texts and online messages was unheard of. So where did these colorful symbols come from, and how did they become such a ubiquitous part of modern communication?The story of the emoji traces back to late 1990s Japan. The word "emoji" literally means "picture" (e) + "character" (moji) in Japanese. The original emojis were created in 1999 by Shigetaka Kurita, a Japanese artist and designer who was part of the team working on developing i-mode for mobile internet platforms. Kurita envisioned emojis as a way to add more expression and nuance to the limited character allowances that were common in mobile messaging at the time.The first emojis were pretty basic - just 176 simple icons like a smiley face, a heart, an umbrella, and some other random objects. But Kurita's designs filled a need that people didn't even realize they had. Those little pictographs allowed people to enhance their digital messages with an extra layer of emotional context in a way that wasn't possible with just plain text.After their debut on Japanese mobile phones, emojis slowly started catching on in other parts of the world. As internet communication and mobile messaging became more global andwidespread in the early 2000s, emojis facilitated easiercross-cultural communication. While languages differed, the simple symbols had near-universal meanings - a laughing face is a laughing face, whether you're in Japan, the U.S., or anywhere else in the world.Really, though, emojis didn't hit truly mainstream global popularity until 2011 when they were added to iOS. From there, their rise was meteoric. Emojis were embraced by people around the world for their ability to convey tone, emotion, and even comedy with just a few pictures. By enhancing messaging apps like iMessage and WhatsApp, emojis made digital communication feel more personal and expressive.These days, most of us use emojis without even thinking about them. We naturally pepper our texts and social media posts with the appropriate symbols to make our point or set the tone. An means we're being playful and silly. and let people know we're annoyed or upset. adds some visual excitement to share big news. And the endless variety of new emojis added each year lets us get even more creative with our digital expression.In fact, emojis have become so ingrained in modern communication that they've even started influencing actuallanguage. New slang terms and idioms have emerged based on combining different emojis in novel ways. And anyone who's ever used to get out of an awkward conversation knows the power of emoji communication.Beyond just individual communication, emojis have also permeated marketing, popular culture, and entertainment. Companies and brands use emojis to establish a particular look and youthful persona. Movies and TV shows use the familiar symbols for promotional materials and merchandise. Even the Unicode Consortium, the non-profit organization that oversees emoji standards, adds new emojis each year based on demand from the public. When Apple released its avocado emoji in 2016, people lost their minds. These days, "avocado emoji" is even a widely understood slang term for a millennial obsession. Wild.Whether you love them or hate them, it's hard to deny the immense cultural impact of emojis. They've undeniably shaped how we communicate and express ourselves online. While they may seem silly and trivial on the surface, these little pictographs actually represent a fascinating new evolution of written language. As the world gets smaller through technology and global communication, emojis provide a wordless way to bridge cultural gaps and enhance digital expression. They allow us totranscend differences and find common ground through universal symbols and meanings.So the next time you laugh-cry at a meme, slide into someone's DMs with a pickup line only Shakespearean emojis can convey, or rally a group chat with a string of celebration symbols, remember that you're taking part in a revolutionary new chapter of written expression. Those little emojis are doing a lot of heavy lifting, conveying subtle emotional shades that plain text simply cannot. Who knows where this phenomenon will go next - it seems the possibilities are endless in the world of digital pictographs!。

两种情感英语

两种情感英语In the realm of the English language, expressing emotions is a nuanced art that can significantly enrich communication. Two common emotions that are frequently encountered and expressed in English are happiness and sadness. Let's explore how these emotions are conveyed through various linguistic means.Happiness1. Vocabulary: Words like "joy," "delight," "elation," "contentment," and "bliss" can be used to express varying degrees of happiness.2. Phrases: Idiomatic expressions such as "on cloud nine," "walking on sunshine," and "tickled pink" are also used to convey happiness.3. Sentences: "She was over the moon about her new job," or "He was thrilled to bits with the surprise party."Sadness1. Vocabulary: Terms like "sad," "unhappy," "depressed," "gloomy," and "miserable" are used to express differentlevels of sadness.2. Phrases: Common phrases include "down in the dumps," "feeling blue," and "heartbroken."3. Sentences: "He felt a deep sense of sorrow after the loss," or "She was despondent over the failed exam."Expressing Emotions in Context- Tone: The tone of voice can greatly affect how emotions are perceived. A cheerful tone can make the expression of happiness more convincing, while a somber tone can emphasize sadness.- Body Language: Non-verbal cues such as smiling (for happiness) or a furrowed brow (for sadness) can support the verbal expression of emotions.- Context: The situation or event being discussed can also influence how emotions are expressed. For example, "I was ecstatic when I heard the good news," versus "The news of the accident left me devastated."Cultural NuancesIt's important to note that the expression of emotions can vary significantly across cultures. What is considered an appropriate display of happiness or sadness in one culture might be seen as over-the-top or understated in another.ConclusionUnderstanding and effectively using emotional expressions in English can enhance interpersonal communication and help convey a more accurate representation of one's feelings. Whether it's sharing the joy of a personal achievement or expressing the sorrow of a loss, the ability to articulate emotions in English is a valuable skill in both personal and professional settings.。

linguistics studies particular language

linguistics studies particularlanguageLinguistics is the scientific study of language. It involves the analysis of language structure, language use, and language acquisition. Linguistics studies particular language by examining its sounds, grammar, vocabulary, and pragmatics.One of the main areas of linguistics is phonetics, which studies the sounds of language. Phoneticians analyze the physical properties of sounds, such as their pitch, loudness, and duration, and how they are produced and perceived. Another area of linguistics is morphology, which studies the structure of words. Morphologists examine how words are formed from smaller units, such as roots and affixes, and how these units combine to create different meanings.Syntax is the study of the structure of sentences. Syntaxians analyze the rules that govern the combination of words into phrases and clauses and how these combinations convey meaning. Lexicography is the study of vocabulary. Lexicographers编纂dictionaries and other lexical resources to document the meanings, pronunciations, and uses of words in a particular language.Pragmatics is the study of language use in context. Pragmaticians examine how speakers use language to convey meaning beyond the literal meaning of words and how listeners interpret this meaning based on the context and their knowledge of the world. Linguistics also studies language acquisition, which is the process by which humans learn to speak and understand a language.Overall, linguistics provides a framework for understanding how particular languages work and how they are used in different contexts. By studying the sounds, grammar, vocabulary, and pragmatics of a language, linguists can gain insight into the structure and meaning of that language and how it is acquired and used by speakers.。

中英文分离公式

中英文分离公式English: In natural language processing (NLP), particularly when dealing with mixed-language text, separating Chinese and English text can be challenging but is achievable using various techniques. One common approach is to leverage the stark visual differences between the two scripts; Chinese characters are typically square-shaped, while English letters are more linear. By analyzing the shape and layout of the characters, it's possible to develop algorithms that can automatically detect and separate Chinese and English text. Another method involves utilizing linguistic features, such as the presence of spaces and punctuation, which are more prevalent in English text. These features can act as cues for segmentation, helping algorithms determine where one language ends and the other begins. Furthermore, machine learning models, particularly those based on deep learning, can be trained on large datasets containing mixed-language text to learn the patterns and structures of both languages, enabling them to effectively separate Chinese and English text. Additionally, rule-based approaches can be effective, especially when dealing with well-formatted text where language switches are clear and consistent. By combining these approaches, it's possible todevelop robust systems for automatically separating Chinese and English text in NLP applications.中文翻译：在自然语言处理（NLP）中，特别是在处理混合语言文本时，将中文和英文文本分开可能会很具有挑战性，但可以通过各种技术来实现。

英语作文隐形衔接

英语作文隐形衔接In the realm of written expression, the art of seamless transitions holds the power to transform a mere collection of sentences into a captivating narrative. It is a delicate dance, where words intertwine like threads, guiding the reader effortlessly through the tapestry of ideas. As an avid wordsmith, I have spent countless hours honing my craft, striving to master the elusive art of invisible transitions.My journey began with an insatiable curiosity, a desire to unravel the secrets that lay beneath the surface of language. I devoured books on writing, studying the techniques employed by literary giants. I pored over passages, analyzing how they crafted seamless connections between paragraphs, ideas, and even sentences. It was an arduous endeavor, but with each passing day, I felt my understanding deepen.The key to invisible transitions lies in recognizing the inherent relationships between ideas. Each sentence should not stand alone but rather serve as a stepping stone towards the next. By identifying common threads, shared themes, or logical progressions, I learned to weave a cohesive narrative that flowed effortlessly.Moreover, I discovered the importance of using transitional words and phrases. These linguistic signposts serve as subtle cues, guiding the reader through the twists and turns of my prose. Words like "however," "moreover," and "in addition" help to establish relationships between contrasting or complementary ideas. By carefully selecting and placing these transitions, I could create a sense of coherence and direction.As I delved deeper into the art of invisible transitions, I realized that it was not merely a technical skill but also an expression of empathy. By anticipating the reader's expectations and guiding them smoothly through my writing,I could forge a connection that transcended the written word. I could invite them into my world, sharing my thoughts and ideas with clarity and grace.In the end, the true measure of my success lies not in the absence of visible transitions but in the seamless flow of my prose. When a reader can glide effortlessly through my writing, unaware of the subtle connections that guide their journey, I know that I have achieved my goal. It is then that I feel a sense of deep satisfaction, knowing that I have honored the power of language and created a truly immersive experience for my readers.As I continue my literary journey, I am constantly seeking to refine my skills in the art of invisible transitions. It is a lifelong pursuit, one that requires patience, practice, and an unwavering dedication to the craft of writing. But I am confident that with each passing day, I will grow closer to my goal of creating prose that flows with the elegance and grace of a gentle stream, carrying my readers on an unforgettable journey through theworld of words.。

2021考研英语二七选五数字题

2021考研英语二七选五数字题In the 2021 National Entrance Examination for Postgraduate (NEEP) in China, the English II section included a challenging task known as the "7选5" (Seven Choices Five). This section required candidates to select five sentences from a total of seven, with the aim of constructing a coherent and cohesive passage. Let's delve into the nature of this task and discuss effective strategies for tackling it.The 7选5 task is designed to assess candidates' ability to comprehend and organize information in English. It demands not only a solid grasp of vocabulary and grammar but also keen analytical skills to determine the logical sequence and thematic coherence of the provided sentences.To succeed in this task, candidates must first carefully read through all seven sentences. Each sentence presents a unique perspective or idea, often relating to a common theme or topic. Understanding the underlying theme is crucial as it forms the backbone of the coherent passage that candidates need to construct.After identifying the theme, candidates should evaluate each sentence based on its relevance and logical flow within the context of the other sentences. This process involves considering how each sentence contributes to the overall development of ideas and whether any sentences can serve as introductory, supporting, or concluding statements.Furthermore, paying attention to transitional phrases or linking words within the sentences can aid in establishing a smooth progression from one idea to the next. These linguistic cues help maintain coherence and clarity throughout the constructed passage.In terms of language use, candidates are expected to demonstrate proficiency in academic English. This includes using appropriate vocabulary and grammatical structures to convey ideas accurately and effectively. Clarity of expression is paramount, as it ensures that the intended meaning of the passage is communicated clearly to the reader.Moreover, candidates should be mindful of sentence structure variety to enhance readability and engagement. By incorporating a mix of simple, compound, and complex sentences, candidates can create a rhythm that keeps the reader engaged while also showcasing their language proficiency.In conclusion, the 7选5 task in the 2021 NEEP English II section presents a unique challenge that assesses candidates' comprehension, analytical, and language skills. Success in this task hinges on the ability to discern thematic coherence, organize ideas logically, and express them clearly and concisely in academic English. By approaching the task systematically and leveraging effective strategies, candidates can navigate this challenge with confidence and maximize their performance in the examination.。

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Journal of Artiﬁcial Intelligence Research30(2007)457-500Submitted05/07;published11/07 Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and TextFran¸c ois Mairesse f.mairesse@ Department of Computer Science,University of Sheﬃeld211Portobello Street,Sheﬃeld S14DP,United KingdomMarilyn A.Walker m.a.walker@ Department of Computer Science,University of Sheﬃeld211Portobello Street,Sheﬃeld S14DP,United KingdomMatthias R.Mehl mehl@ Department of Psychology,University of Arizona1503E University Blvd.Building68,Tucson,AZ85721,USARoger K.Moore r.k.moore@ Department of Computer Science,University of Sheﬃeld211Portobello Street,Sheﬃeld S14DP,United KingdomAbstractIt is well known that utterances convey a great deal of information about the speaker in addition to their semantic content.One such type of information consists of cues to the speaker’s personality traits,the most fundamental dimension of variation between humans.Recent work explores the automatic detection of other types of pragmatic variation in text and conversation,such as emotion,deception,speaker charisma,dominance,point of view,subjectivity,opinion and sentiment.Personality aﬀects these other aspects of linguistic production,and thus personality recognition may be useful for these tasks,in addition to many other potential applications.However,to date,there is little work on the automatic recognition of personality traits.This article reports experimental results for recognition of all Big Five personality traits,in both conversation and text,utilising both self and observer ratings of personality.While other work reports classiﬁcation results,we experiment with classiﬁcation,regression and ranking models.For each model,we analyse the eﬀect of diﬀerent feature sets on accuracy.Results show that for some traits,any type of statistical model performs signiﬁcantly better than the baseline,but ranking models perform best overall.We also present an experiment suggesting that ranking models are more accurate than multi-class classiﬁers for modelling personality.In addition,recognition models trained on observed personality perform better than models trained using self-reports,and the optimal feature set depends on the personality trait.A qualitative analysis of the learned models conﬁrms previousﬁndings linking language and personality,while revealing many new linguistic markers.1.IntroductionPersonality is the complex of all the attributes—behavioural,temperamental,emotionaland mental—that characterise a unique individual.It is well known that utterances convey a great deal of information about the speaker in addition to their semantic content.One such type of information consists of cues to theMairesse,Walker,Mehl&Moorespeaker’s personality traits,the most fundamental dimension of variation between humans. Personality is typically assessed alongﬁve dimensions known as the Big Five:•Extraversion vs.Introversion(sociable,assertive,playful vs.aloof,reserved,shy)•Emotional stability vs.Neuroticism(calm,unemotional vs.insecure,anxious)•Agreeableness vs.Disagreeable(friendly,cooperative vs.antagonistic,faultﬁnding)•Conscientiousness vs.Unconscientious(self-disciplined,organised vs.ineﬃcient,care-less)•Openness to experience(intellectual,insightful vs.shallow,unimaginative)Theseﬁve personality traits have been repeatedly obtained by applying factor analyses to various lists of trait adjectives used in personality description questionnaires(sample adjectives above)(Norman,1963;Peabody&Goldberg,1989;Goldberg,1990).The basis for such factor analyses is the Lexical Hypothesis(Allport&Odbert,1936),i.e.that the most relevant individual diﬀerences are encoded into the language,and the more important the diﬀerence,the more likely it is to be expressed as a single word.Despite some known limits(Eysenck,1991;Paunonen&Jackson,2000),over the last50years the Big Five model has become a standard in psychology and experiments using the Big Five have shown that personality traits inﬂuence many aspects of task-related individual behaviour.For example, the success of most interpersonal tasks depends on the personalities of the participants,and personality traits inﬂuence leadership ability(Hogan,Curphy,&Hogan,1994),general job performance(Furnham,Jackson,&Miller,1999),attitude toward machines(Sigurdsson, 1991),sales ability(Furnham et al.,1999),teacher eﬀectiveness(Rushton,Murray,&Erdle, 1987),and academic ability and motivation(Furnham&Mitchell,1991;Komarraju& Karau,2005).However,to date there has been little work on the automatic recognition of personality traits(Argamon,Dhawle,Koppel,&Pennebaker,2005;Mairesse&Walker, 2006a,2006b;Oberlander&Nowson,2006).Recent work in AI explores methods for the automatic detection of other types of prag-matic variation in text and conversation,such as emotion(Oudeyer,2002;Liscombe,Ven-ditti,&Hirschberg,2003),deception(Newman,Pennebaker,Berry,&Richards,2003; Enos,Benus,Cautin,Graciarena,Hirschberg,&Shriberg,2006;Graciarena,Shriberg, Stolcke,Enos,Hirschberg,&Kajarekar,2006;Hirschberg,Benus,Brenier,Enos,Fried-man,Gilman,Girand,Graciarena,Kathol,Michaelis,Pellom,Shriberg,&Stolcke,2005), speaker charisma(Rosenberg&Hirschberg,2005),mood(Mishne,2005),dominance in meetings(Rienks&Heylen,2006),point of view or subjectivity(Wilson,Wiebe,&Hwa, 2004;Wiebe,Wilson,Bruce,Bell,&Martin,2004;Wiebe&Riloﬀ,2005;Stoyanov,Cardie, &Wiebe,2005;Somasundaran,Ruppenhofer,&Wiebe,2007),and sentiment or opinion (Turney,2002;Pang&Lee,2005;Popescu&Etzioni,2005;Breck,Choi,&Cardie,2007). In contrast with these pragmatic phenomena,which may be relatively contextualised or short-lived,personality is usually considered to be a longer term,more stable,aspect of individuals(Scherer,2003).However,there is evidence that personality interacts with,and aﬀects,these other aspects of linguistic production.For example,there are strong relations between the extraversion and conscientiousness traits and the positive aﬀects,and betweenRecognising Personality in Conversation and Textneuroticism and disagreeableness and various negative aﬀects(Watson&Clark,1992).Ly-ing leads to inconsistencies in impressions of the agreeableness personality trait across modes (visual vs.acoustic),and these inconsistencies are used as cues for deception detection by human judges(Heinrich&Borkenau,1998).Outgoing and energetic people(i.e.extravert) are more successful at deception,while apprehensive(i.e.neurotic)individuals are not as successful(Riggio,Salinas,&Tucker,1988),and individuals who score highly on the agree-ableness and openness to experience traits are also better at detecting deception(Enos et al.,2006).Features used to automatically recognise introversion and extraversion in our studies are also important for automatically identifying deception(Newman et al.,2003). Speaker charisma has been shown to correlate strongly with extraversion(Bono&Judge, 2004),and individuals who dominate meetings have similar characteristics to extraverts, such as verbosity(Rienks&Heylen,2006).Oberlander and Nowson(2006)suggest that opinion mining could beneﬁt from personality information.Thus this evidence suggests that incorporating personality models into these other tasks may improve accuracy.We also hypothesise that computational recognition of user personality could be use-ful in many other computational applications.Identiﬁcation of leaders using personality dimensions could be useful in analysing meetings and the conversations of suspected ter-rorists(Hogan et al.,1994;Tucker&Whittaker,2004;Nunn,2005).Dating websites could analyse text messages to try to match personalities and increase the chances of a successful relationship(Donnellan,Conger,&Bryant,2004).Tutoring systems might be more eﬀective if they could adapt to the learner’s personality(Komarraju&Karau,2005).Automatically identifying the author’s personality in a corpus could also improve language generation, as individual diﬀerences in language aﬀect the way that concepts are expressed(Reiter& Sripada,2004).Studies have also shown that users’evaluation of conversational agents depends on their own personality(Reeves&Nass,1996;Cassell&Bickmore,2003),which suggests a requirement for such systems to adapt to the user’s personality,like humans do (Funder&Sneed,1993;McLarney-Vesotski,Bernieri,&Rempala,2006).While in some applications it would be possible to acquire personality information by asking the user or author directly(John,Donahue,&Kentle,1991;Costa&McCrae, 1992),here we explore whether it is possible to acquire personality models for the Big Five personality traits by observation of individual linguistic outputs in text and conversation. To date,we know of only two studies besides our own on automatic recognition of user personality(Argamon et al.,2005;Mairesse&Walker,2006a,2006b;Oberlander&Nowson, 2006).Other work has applied classiﬁcation models to the recognition of personality in texts and blog postings.To our knowledge,the results presented here are theﬁrst to examine the recognition of personality in dialogue(Mairesse&Walker,2006a,2006b),and to apply regression and ranking models that allow us to model personality recognition using the continuous scales traditional in psychology.We also systematically examine the use of diﬀerent feature sets,suggested by psycholinguistic research,and report statistically signiﬁcant results.We start in Section2by reviewing the psychologyﬁndings linking personality and language;theseﬁndings motivate the features used in the learning experiments described in Section3.Section3overviews the methods we use to automatically train personality models,using both conversation and written language samples,and both self-ratings and observer ratings of personality traits.We explore the use of classiﬁcation models(Section4),Mairesse,Walker,Mehl&Mooreregression models(Section5),and ranking models(Section6),and the eﬀect of diﬀerent feature sets on model accuracy.The results show that for some traits,any type of statistical model performs signiﬁcantly better than the baseline,but ranking models perform best overall.In addition,models trained on observed personality scores perform better than models trained using self-reports,and the optimal feature set is dependent on the personality trait.The rules derived and features used in the learned models conﬁrm previousﬁndings linking language and personality,while revealing many new linguistic markers.We delay the review of Argamon et al.(2005)and Oberlander and Nowson(2006)to Section7,when we can better compare their results with our own,and sum up and discuss future work in Section8.2.Personality Markers in LanguageWhy do we believe it might be possible to automatically recognise personality from linguistic cues?Psychologists have documented the existence of such cues by discovering correlations between a range of linguistic variables and personality traits,across a wide range of linguistic levels,including acoustic parameters(Smith,Brown,Strong,&Rencher,1975;Scherer, 1979),lexical categories(Pennebaker&King,1999;Pennebaker,Mehl,&Niederhoﬀer, 2003;Mehl,Gosling,&Pennebaker,2006;Fast&Funder,2007),n-grams(Oberlander& Gill,2006),and speech-act type(Vogel&Vogel,1986).As the correlations reported in the literature are generally weak(see Section3.3),it is not clear whether these features will improve accuracies of statistical models on unseen subjects.Of all Big Five traits, extraversion has received the most attention from researchers.However,studies focusing systematically on all Big Five traits are becoming more common.2.1Markers of ExtraversionWe summarise variousﬁndings linking extraversion and language cues in Table1,for diﬀerent levels of language production such as speech,syntax and content selection.A re-view by Furnham(1990)describes linguistic features linked to extraversion and other traits, and Dewaele and Furnham(1999)review studies focusing on the link between extraversion and both language learning and speech production.Findings include that there is a higher correlation between extraversion and oral lan-guage,especially when the study involves a complex task.Extraverts talk more,louder and more repetitively,with fewer pauses and hesitations,they have higher speech rates, shorter silences,a higher verbal output,a lower type/token ratio and a less formal lan-guage,while introverts use a broader vocabulary(Scherer,1979;Furnham,1990;Gill& Oberlander,2002).Extraverts also use more positive emotion words,and show more agree-ments and compliments than introverts(Pennebaker&King,1999).Extravert students learning French as a second language produce more back-channels,and have a more im-plicit style and a lower lexical richness in formal situations.It seems that the more complex the task and the higher the level of anxiety,the easier it is to diﬀerentiate between introverts and extraverts(Dewaele&Furnham,1999).Heylighen and Dewaele(2002)also note that extraversion is signiﬁcantly correlated with contextuality,as opposed to formality.Contextuality can be seen a high reliance on shared knowledge between conversational partners,leading to the use of many deicticRecognising Personality in Conversation and TextLevel Introvert ExtravertConversational Listen Initiate conversation behaviour Less back-channel behaviour More back-channel behaviourTopic Self-focused Not self-focused*selection Problem talk,dissatisfaction Pleasure talk,agreement,compliment Strict selection Think out loud*Single topic Many topicsFew semantic errors Many semantic errorsFew self-references Many self-referencesStyle Formal InformalMany hedges(tentative words)Few hedges(tentative words) Syntax Many nouns,adjectives,prepositions(explicit)Many verbs,adverbs,pronouns(implicit) Elaborated constructions Simple constructions*Many words per sentence Few words per sentenceMany articles Few articlesMany negations Few negationsLexicon Correct Loose*Rich PoorHigh diversity Low diversityMany exclusive and inclusive words Few exclusive and inclusive wordsFew social words Many social wordsFew positive emotion words Many positive emotion wordsMany negative emotion words Few negative emotion words Speech Received accent Local accent*Slow speech rate High speech rateFew disﬂuencies Many disﬂuencies*Many unﬁlled pauses Few unﬁlled pausesLong response latency Short response latencyQuiet LoudLow voice quality High voice qualityNon-nasal voice Nasal voiceLow frequency variability High frequency variabilityTable1:Summary of identiﬁed language cues for extraversion and various production lev-els,based on previous studies by Scherer(1979),Furnham(1990),Pennebakerand King(1999),Dewaele and Furnham(1999),Gill(2003),Mehl et al.(2006).Asterisks indicate that the cue is only based on a hypothesis,as opposed to studyresults.expressions such as pronouns,verbs,adverbs and interjections,whereas formal language is less ambiguous and assumes less common knowledge.In order to measure this variation, Heylighen and Dewaele suggest the use of a metric called formality,deﬁned as:F=(noun freq+adjective freq+preposition freq+article freq-pronoun freq-verbfreq-adverb freq-interjection freq+100)/2They argue that this measure is the most important dimension of variation between linguistic expressions,as shown in Biber’s factor analysis of various genres(Biber,1988). In addition to introversion,the authors alsoﬁnd that formality correlates positively with the level of education and the femininity of the speaker.Situational variables related to the use of formal language are the audience size,the time span between dialogues,the unavailability of feedback,diﬀerence of backgrounds and spatial location between speakers, as well as the preceding amount of conversation.Mairesse,Walker,Mehl&MooreScherer(1979)shows that extraverts are perceived as talking louder and with a more nasal voice,and that American extraverts tend to make fewer pauses,while German ex-traverts produce more pauses than introverts.Thus personality markers are culture-dependent, even among western societies.Oberlander and Gill(2006)use content analysis tools and n-gram language models to identify markers in extravert and introvert emails.They replicate previousﬁndings and identify new personality markers such asﬁrst person singular pronouns(e.g.,I don’t)and formal greetings(e.g.,Hello)for introversion,while less formal phrases such as Take care and Hi characterise extraverts.2.2Markers of Other Big Five TraitsPennebaker and King(1999)identify many linguistic features associated with each of the Big Five personality traits.They use their Linguistic Inquiry and Word Count(LIWC) tool to count word categories of essays written by students whose personality has been assessed using a questionnaire.The authorsﬁnd small but signiﬁcant correlations between their linguistic dimensions and personality traits.Neurotics use more1st person singular pronouns,more negative emotion words and less positive emotion words.On the other hand, agreeable people express more positive and fewer negative emotions.They also use fewer articles.Conscientious people avoid negations,negative emotion words and words reﬂecting discrepancies(e.g.,should and would).Finally,openness to experience is characterised by a preference for longer words and words expressing tentativity(e.g.,perhaps and maybe),as well as the avoidance of1st person singular pronouns and present tense forms.Additionally,Mehl et al.(2006)study markers of personality as perceived by observers. Theyﬁnd that the use of words related to insight and the avoidance of past tense indicates openness to experience,and swearing marks disagreeableness.The same authors also show that some linguistic cues vary greatly across gender.For example,males perceived as conscientious produce moreﬁller words,while females don’t.Gender diﬀerences are also found in markers of self-assessed personality:the use of2nd person pronouns indicates a conscientious male,but an unconscientious female.Gill and Oberlander(2003)study correlates of emotional stability:theyﬁnd that neu-rotics use more concrete and frequent words.However,they also show that observers don’t use those cues correctly,as observer reports of neuroticism correlate negatively with self-reports.Concerning prosody,Smith et al.(1975)also show that speech rate is positively corre-lated with perceived competence(conscientiousness),and that speech rate has an inverted-U relationship with benevolence(agreeableness),suggesting a need for non-linear models.Some traits have produced moreﬁndings than others.A reason for this might be that some are more reﬂected through language,like extraversion.However,it is possible that this focus is a consequence of extraversion being correlated with linguistic cues that can be analysed more easily(e.g.,verbosity).Recognising Personality in Conversation and Text3.Experimental MethodWe conduct a set of experiments to examine whether automatically trained models can be used to recognise the personality of unseen subjects.Our approach can be summarised in ﬁve steps:1.Collect individual corpora;2.Collect associated personality ratings for each participant;3.Extract relevant features from the texts;4.Build statistical models of the personality ratings based on the features;5.Test the learned models on the linguistic outputs of unseen individuals.The following sections describe each of these steps in more detail.3.1Sources of Language and PersonalityIntrovert ExtravertI’ve been waking up on time so far.What I have some really random thoughts.Ihas it been,5days?Dear me,I’ll never want the best things out of life.keep it up,being such not a morning But I fear that I want too much!person and all.But maybe I’ll adjust,What if I fallﬂat on my face andor not.I want internet access in my don’t amount to anything.But Iroom,I don’t have it yet,but I will feel like I was born to do BIG thingson Wed???I think.But that ain’t soon on this earth.But who knows...There enough,cause I got calculus homework[...]is this Persian party today.Neurotic Emotionally stableOne of my friends just barged in,and I I should excel in this sport because Ijumped in my seat.This is crazy.I know how to push my body harder than should tell him not to do that again.anyone I know,no matter what the test II’m not that fastidious actually.But always push my body harder than everyone certain things annoy me.The things else.I want to be the best no matterthat would annoy me would actually what the sport or event.I should alsoannoy any normal human being,so I be good at this because I love to rideknow I’m not a freak.my bike.Table2:Extracts from the essays corpus,for participants rated as extremely introvert, extravert,neurotic,and emotionally stable.We use the data from Pennebaker and King(1999)and Mehl et al.(2006)in our ex-periments.Theﬁrst corpus contains2,479essays from psychology students(1.9million words),who were told to write whatever comes into their mind for20minutes.The data was collected and analysed by Pennebaker and King(1999);a sample is shown in Table2.Mairesse,Walker,Mehl&MooreIntrovert Extravert-Yeah you would do kilograms.Yeah I see-That’s myﬁrst yogurt experience here.what you’re saying.Really watery.Why?-On Tuesday I have class.I don’t know.-Damn.New game.-I don’t know.A16.Yeah,that is kind of cool.-Oh.-I don’t know.I just can’t wait to be with-That’s so rude.That.you and not have to do this every night,-Yeah,but he,they like each other.you know?He likes her.-Yeah.You don’t know.Is there a bed in-They are going to end up breaking upthere?Well ok just...and he’s going to be like.Unconscientious Conscientious-With the Chinese.Get it together.-I don’t,I don’t know for a fact but-I tried to yell at you through the window.I would imagine that historically women Oh.xxxx’s fucking a dumb ass.Look at who have entered prostitution have done him.Look at him,dude.Look at him.I so,not everyone,but for the majority out wish we had a camera.He’s fucking brushing of extreme desperation and I think.I don’t his t-shirt with a tooth brush.Get a kick know,i think people understand thatof it.Don’t steal nothing.desperation and they don’t don’t see[...] Table3:Extracts from the EAR corpus,for participants rated as extremely introvert,ex-travert,unconscientious,and conscientious.Only the participants’utterances areshown.Personality was assessed by asking each student toﬁll in the Big Five Inventory question-naire(John et al.,1991),which asks participants to evaluate on a5point scale how well their personality matches a series of descriptions.The second source of data consists of conversation extracts recorded using an Electroni-cally Activated Recorder(EAR)(Mehl,Pennebaker,Crow,Dabbs,&Price,2001),collected by Mehl et al.(2006).To preserve the participants’privacy,only random snippets of conver-sation were recorded.This corpus is much smaller than the essays corpus(96participants for a total of97,468words and15,269utterances).While the essays corpus consists only of texts,the EAR corpus contains both sound extracts and transcripts.This corpus therefore allows us to build models of personality recognition from speech.Only the participants’ut-terances were transcribed(not those of their conversational partners),making it impossible to reconstruct whole conversations.Nevertheless,the conversation extracts are less formal than the essays,and personality may be best observed in the absence of behavioural con-straints.Table4shows that while the essays corpus is much larger than the EAR corpus, the amount of data per subject is comparable,i.e.766words per subject for the essays and 1,015for the EAR corpus.Table3shows examples of conversations from the EAR corpus for diﬀerent personality traits.For personality ratings,the EAR corpus contains both self-reports and ratings from18 independent observers.Psychologists use self-reports to facilitate evaluating the personal-ity of a large number of participants,and there are a large number of standard self-report tests.Observers were asked to make their judgments by rating descriptions of the Big Five Inventory(John&Srivastava,1999)on a7point scale(from strongly disagree to stronglyRecognising Personality in Conversation and TextDataset Essays EARSource of language Written SpokenPersonality reports Self reports Self and observerNumber of words 1.9million97,468Subjects2,47996Words per subject766.41,015.3Table4:Comparison of the essays and EAR corpora.agree),without knowing the participants.Observers were divided into three groups,each rating one third of the participants,after listening to each participant’s entire set of sound ﬁles(130ﬁles on average).The personality assessment was based on the audio recordings, which contain more information than the transcripts(e.g.,ambient sounds,including cap-tured conversations).Mehl et al.(2006)report strong inter-observer reliabilities across all Big Five dimensions(intraclass correlations based on one-way random eﬀect models:mean r=0.84,p<.01).The observers’ratings were averaged for each participant,to produce theﬁnal scores used in our experiments.Interestingly,the average correlations between frequency counts from psycholinguistic word categories and the Big Five personality dimensions were considerably larger in the EAR corpus than with the student essays studied by Pennebaker and King.Moreover, the correlations reported by Mehl et al.seem to be higher for observer reports than for self-reports.Based on this observation,we hypothesise that models of observed personality will outperform models of self-assessed personality.3.2FeaturesThe features used in the experiments are motivated by previous psychologicalﬁndings about correlations between measurable linguistic factors and personality traits.Features are divided into subsets depending on their source and described in the subsections below. The total feature set is summarised in Table6.The experimental results given in Sections4, 5,and6examine the eﬀect of each feature subset on model accuracy.3.2.1Content and SyntaxWe extracted a set of linguistic features from each essay and conversation transcript, starting with frequency counts of88word categories from the Linguistic Inquiry and Word Count(LIWC)utility(Pennebaker et al.,2001).These features include both syntactic(e.g., ratio of pronouns)and semantic information(e.g.,positive emotion words),which were validated by expert judges.Some LIWC features are illustrated in Table5.Pennebaker and King(1999)previously found signiﬁcant correlations between these features and each of the Big Five personality traits.Relevant word categories for extraversion include social words,emotion words,ﬁrst person pronouns,and present tense verbs.Mehl et al.(2006) showed that LIWC features extracted from the EAR corpus were signiﬁcantly correlated with both self and observer reports of personality.We also added14additional features from the MRC Psycholinguistic database(Colt-heart,1981),which contains statistics for over150,000words,such as estimates of the ageMairesse,Walker,Mehl&MooreFeature Type ExampleAnger words LIWC hate,kill,pissedMetaphysical issues LIWC God,heaven,coﬃnPhysical state/function LIWC ache,breast,sleepInclusive words LIWC with,and,includeSocial processes LIWC talk,us,friendFamily members LIWC mom,brother,cousinPast tense verbs LIWC walked,were,hadReferences to friends LIWC pal,buddy,coworkerImagery of words MRC Low:future,peace-High:table,carSyllables per word MRC Low:a-High:uncompromisinglyConcreteness MRC Low:patience,candor-High:shipFrequency of use MRC Low:duly,nudity-High:he,theTable5:Examples of LIWC word categories and MRC psycholinguistic features(Pen-nebaker et al.,2001;Coltheart,1981).MRC features associate each word to anumerical value.of acquisition,frequency of use,and familiarity.As introverts take longer to reﬂect on their utterances,Heylighen and Dewaele(2002)suggest that their vocabulary is richer and more precise,implying a lower frequency of use.The MRC feature set was previously used by Gill and Oberlander(2002),who showed that extraversion is negatively correlated with concreteness.Concreteness also indicates neuroticism,as well as the use of more frequent words(Gill&Oberlander,2003).Table5shows examples of MRC scales.Each MRC feature is computed by averaging the feature value of all the words in the essay or con-versational extract.Part-of-Speech tags are computed to identify the correct entry in the database among a set of homonyms.3.2.2Utterance TypeVarious facets of personality traits seem to depend on the level of initiative of the speaker and the type of utterance used(e.g.,assertiveness,argumentativeness,inquisitiveness,etc.). For example,extraverts are more assertive in their emails(Gill&Oberlander,2002),while extravert second language learners were shown to produce more back-channel behaviour (Vogel&Vogel,1986).We therefore introduced features characterising the types of utter-ance produced.We automatically tagged each utterance of the EAR corpus with speech act categories from Walker and Whittaker(1990),using heuristic rules based on each ut-terance’s parse tree:•Command:utterance using the imperative form,a command verb(e.g.,must and have to)ora yes/no second person question with a modal auxiliary like can;•Prompt:single word utterance used for back-channelling(e.g.,Yeah,OK,Huh,etc.);•Question:interrogative utterance which isn’t a command;•Assertion:any other utterance.。