Divergent word usages reflect differences among people. In this paper, we present a novel angle for studying word usage divergence -- word interpretations. We propose an approach that quantifies semantic differences in interpretations among different groups of people. The effectiveness of our approach is validated by quantitative evaluations. Experiment results indicate that divergences in word interpretations exist. We further apply the approach to two well studied types of differences between people -- gender and region. The detected words with divergent interpretations reveal the unique features of specific groups of people. For gender, we discover that certain different interests, social attitudes, and characters between males and females are reflected in their divergent interpretations of many words. For region, we find that specific interpretations of certain words reveal the geographical and cultural features of different regions.
Much work has focused on divergent word usages among people in numerous aspects, such as gender, age, occupation, and region. It is reported that typical male language uses more judgmental adjectives, elliptical sentences, directives, and I'' reference, while typical female language contains more intensive adverbs, references to emotions, uncertainty verbs @cite_9 . People in different age groups appear to choose words differently. reported that, comparing with younger people, older people talk more about family and work, and use less swear words in their language @cite_0 . Jobs affect the language patterns of people as well @cite_10 . In the paper, the authors listed the most used words of several occupations, and their results indicate clear divergences in word usages between people of different jobs. Similarly, different cities may have different preferences in words @cite_13 , which reflect regional differences between people.
@cite_0 @cite_9 @cite_10 @cite_13
2119595472 1979753320 1979230891 6274171 2023736093 2955868760 2109816866 1914856875 2250559305 2022792389 2500392116 2950211925 2065130322 1973871257 1982374456 2166700048 2888167352 2073443734 2114030807 147964346
We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase ‘sick of’ and the word ‘depressed’), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive ‘my’ when mentioning their ‘wife’ or ‘girlfriend’ more often than females use ‘my’ with ‘husband’ or 'boyfriend’). To date, this represents the largest study, by an order of magnitude, of language and personality. Abstract Groups of 32 children in each of three grades (kindergarten, second, and fourth) listened to a man's voice and a woman's voice, speaking words simultaneously. On one run through the 23 pairs of words, Ss were instructed to report what the man's voice was saying; on another, the words spoken by the woman. The stimulus words were systematically varied with respect to the number of syllables they contained. For cach S, the stimulus words were presented on one occasion binaurally (both words in both cars), on another occasion, dichotically (two different words in each ear). The number of correct reports of the word spoken by the desired voice increased with age; the number of intrusive errors (reports of words spoken by the unwanted voice) declined with age. At all age levels, scores were higher under dichotic presentation, but there was no change with age in the advantage derived from this presentation. Children at each age level made more errors on single-syllable than on multi-syllable words, but this difference was greater for older children. An analysis of errors revealed systematic differences among age groups in the nature of wrong guesses. Performanec improved through blocks of trials, at all age levels. Two projects explored the links between language use and aging. In the first project, written or spoken text samples from disclosure studies from over 3,000 research participants from 45 different studies representing 21 laboratories in 3 countries were analyzed to determine how people change in their use of 14 text dimensions as a function of age. A separate project analyzed the collected works of 10 well-known novelists, playwrights, and poets who lived over the last 500 years. Both projects found that with increasing age, individuals use more positive and fewer negative affect words, use fewer self-references, use more future-tense and fewer past-tense verbs, arid demonstrate a general pattern of increasing cognitive complexity. Implications for using language as a marker of personality among current and historical texts are discussed. The language used in tweets from 1,300 different US counties was found to be predictive of the subjective well-being of people living in those counties as measured by representative surveys. Topics, sets of co-occurring words derived from the tweets using LDA, improved accuracy in predicting life satisfaction over and above standard demographic and socio-economic controls (age, gender, ethnicity, income, and education). The LDA topics provide a greater behavioural and conceptual resolution into life satisfaction than the broad socio-economic and demographic variables. For example, tied in with the psychological literature, words relating to outdoor activities, spiritual meaning, exercise, and good jobs correlate with increased life satisfaction, while words signifying disengagement like ’bored’ and ’tired’ show a negative association. Information about the affective meanings of words is used by researchers working on emotions and moods, word recognition and memory, and text-based sentiment analysis. Three components of emotions are traditionally distinguished: valence (the pleasantness of a stimulus), arousal (the intensity of emotion provoked by a stimulus), and dominance (the degree of control exerted by a stimulus). Thus far, nearly all research has been based on the ANEW norms collected by Bradley and Lang (1999) for 1,034 words. We extended that database to nearly 14,000 English lemmas, providing researchers with a much richer source of information, including gender, age, and educational differences in emotion norms. As an example of the new possibilities, we included stimuli from nearly all of the category norms (e.g., types of diseases, occupations, and taboo words) collected by Van Overschelde, Rawson, and Dunlosky (Journal of Memory and Language 50:289-335, 2004), making it possible to include affect in studies of semantic memory. Speakers of different languages must attend to and encode strikingly different aspects of the world in order to use their language correctly (Sapir, 1921; Slobin, 1996). One such difference is related to the way gender is expressed in a language. Saying “I am happy” in English, does not encode any additional knowledge of the speaker that uttered the sentence. However, many other languages do have grammatical gender systems and so such knowledge would be encoded. In order to correctly translate such a sentence into, say, French, the inherent gender information needs to be retained recovered. The same sentence would become either “Je suis heureux”, for a male speaker or “Je suis heureuse” for a female one. Apart from morphological agreement, demographic factors (gender, age, etc.) also influence our use of language in terms of word choices or even on the level of syntactic constructions (Tannen, 1991; , 2003). We integrate gender information into NMT systems. Our contribution is twofold: (1) the compilation of large datasets with speaker information for 20 language pairs, and (2) a simple set of experiments that incorporate gender information into NMT for multiple language pairs. Our experiments show that adding a gender feature to an NMT system significantly improves the translation quality for some language pairs. Word usage is influenced by diverse factors, including topic, genre and various speaker author characteristics. To characterize these aspects of language, we introduce the “Multi-Factor Sparse Plus Low Rank” exponential language model, which allows supervised joint training of arbitrary overlapping factor-specific model components. This flexible architecture has the advantage of being highly interpretable. The elements of sparse parameter matrices can be viewed as factor-dependent corrections (e.g. topic- or speaker-dependent phenomena). In topic modeling experiments on conversational telephone speech, we obtain modest perplexity reductions over an n-gram baseline and demonstrate topic-dependent keyword extraction that leads to a 13 (absolute) improvement in precision over TFIDF. We also show how keywords can be jointly learned for speakers, roles and topics in a study of Supreme Court oral arguments. While the study of the connection between discourse patterns and personal identification is decades old, the study of these patterns using language technologies is relatively recent. In that more recent tradition we frame author age prediction from text as a regression problem. We explore the same task using three very different genres of data simultaneously: blogs, telephone conversations, and online forum posts. We employ a technique from domain adaptation that allows us to train a joint model involving all three corpora together as well as separately and analyze differences in predictive features across joint and corpus-specific aspects of the model. Effective features include both stylistic ones (such as POS patterns) as well as content oriented ones. Using a linear regression model based on shallow text features, we obtain correlations up to 0.74 and mean absolute errors between 4.1 and 6.8 years. Many NLP tools for English and German are based on manually annotated articles from the Wall Street Journal and Frankfurter Rundschau. The average readers of these two newspapers are middle-aged (55 and 47 years old, respectively), and the annotated articles are more than 20 years old by now. This leads us to speculate whether tools induced from these resources (such as part-of-speech taggers) put older language users at an advantage. We show that this is actually the case in both languages, and that the cause goes beyond simple vocabulary differences. In our experiments, we control for gender and region. Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts. The authors examine the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach by identifying differences between genders in the ways they use the same words. They present several experiments using data from a large collection of blogs authored by men and women, and they report results for a new task of "gender-based word disambiguation" for a set of over 350 words. Many aspects of people's lives are proven to be deeply connected to their jobs. In this paper, we first investigate the distinct characteristics of major occupation categories based on tweets. From multiple social media platforms, we gather several types of user information. From users' LinkedIn webpages, we learn their proficiencies. To overcome the ambiguity of self-reported information, a soft clustering approach is applied to extract occupations from crowd-sourced data. Eight job categories are extracted, including Marketing, Administrator, Start-up, Editor, Software Engineer, Public Relation, Office Clerk, and Designer. Meanwhile, users' posts on Twitter provide cues for understanding their linguistic styles, interests, and personalities. Our results suggest that people of different jobs have unique tendencies in certain language styles and interests. Our results also clearly reveal distinctive levels in terms of Big Five Traits for different jobs. Finally, a classifier is built to predict job types based on the features extracted from tweets. A high accuracy indicates a strong discrimination power of language features for job prediction task. Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people. We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10 compared with several alternative methods. Abstract Previous studies have found systematic associations between personality and individual differences in word use. Such studies have typically focused on broad associations between major personality domains and aggregate word categories, potentially masking more specific associations. Here I report the results of a large-scale analysis of personality and word use in a large sample of blogs ( N = 694). The size of the dataset enabled pervasive correlations with personality to be identified for a broad range of lexical variables, including both aggregate word categories and individual English words. The results replicated category-level findings from previous off-line studies, identified numerous novel associations at both a categorical and single-word level, and underscored the value of complementary approaches to the study of personality and word use. Abstract We ask if certain dimensions of perceptual similarity are weighted more heavily than others in determining word extension. The specific dimensions examined were shape, size, and texture. In four experiments, subjects were asked either to extend a novel count noun to new instances or, in a nonword classification task, to put together objects that go together. The subjects were 2-year-olds, 3-year-olds, and adults. The results of all four experiments indicate that 2- and 3-year-olds and adults all weight shape more heavily than they do size or texture. This observed emphasis on shape, however, depends on the age of the subject and the task. First, there is a developmental trend. The shape bias increases in strength and generality from 2 to 3 years of age and more markedly from early childhood to adulthood. Second, in young children, the shape bias is much stronger in word extension than in nonword classification tasks. These results suggest that the development of the shape bias originates in language learning—it reflects a fact about language—and does not stem from general perceptual processes. An elementary way of using language is to refer to objects. Often, these objects are physically present in the shared environment and reference is done via mention of perceivable properties of the objects. This is a type of language use that is modelled well neither by logical semantics nor by distributional semantics, the former focusing on inferential relations between expressed propositions, the latter on similarity relations between words or phrases. We present an account of word and phrase meaning that is perceptually grounded, trainable, compositional, and ‘dialogueplausible’ in that it computes meanings word-by-word. We show that the approach performs well (with an accuracy of 65 on a 1-out-of-32 reference resolution task) on direct descriptions and target landmark descriptions, even when trained with less than 800 training examples and automatically transcribed utterances. Abusive language detection models tend to have a problem of being biased toward identity words of a certain group of people because of imbalanced training datasets. For example, "You are a good woman" was considered "sexist" when trained on an existing dataset. Such model bias is an obstacle for models to be robust enough for practical use. In this work, we measure gender biases on models trained with different abusive language datasets, while analyzing the effect of different pre-trained word embeddings and model architectures. We also experiment with three bias mitigation methods: (1) debiased word embeddings, (2) gender swap data augmentation, and (3) fine-tuning with a larger corpus. These methods can effectively reduce gender bias by 90-98 and can be extended to correct model bias in other scenarios. We present a study of the relationship between gender, linguistic style, and social networks, using a novel corpus of 14,000 Twitter users. Prior quantitative work on gender often treats this social variable as a female male binary; we argue for a more nuanced approach. By clustering Twitter users, we find a natural decomposition of the dataset into various styles and topical interests. Many clusters have strong gender orientations, but their use of linguistic resources sometimes directly conflicts with the population-level language statistics. We view these clusters as a more accurate reflection of the multifaceted nature of gendered language styles. Previous corpus-based work has also had little to say about individuals whose linguistic styles defy population-level gender patterns. To identify such individuals, we train a statistical classifier, and measure the classifier confidence for each individual in the dataset. Examining individuals whose language does not match the classifier's model for their gender, we find that they have social networks that include significantly fewer same-gender social connections and that, in general, social network homophily is correlated with the use of same-gender language markers. Pairing computational methods and social theory thus offers a new perspective on how gender emerges as individuals position themselves relative to audiences, topics, and mainstream gender norms. Three experiments were conducted to determine the psychometric properties of language in dyadic interactions. Using text-analysis, it was possible to assess the degree to which people coordinate their word use in natural conversations. In Experiments 1 (n = 130) and 2( n = 32), college students interacted in dyadic conversations in laboratory-based private Internet chat rooms. Experiment 3 analyzed the official transcripts of the Watergate tapes involving the dyadic interactions between President Richard Nixon and his aids H. R. Haldeman, John Erlichman, and John Dean. The results of the three studies offer sub- stantial evidence that individuals in dyadic interactions exhibit linguistic style matching (LSM) on both the conversation level as well as on a turn-by-turn level. Furthermore, LSM is unrelated to ratings of the quality of the interaction by both participants and judges. We propose that a coordination-engagement hypothesis is a better description of linguistic behaviors than the coordination-rapport hypothesis that has been proposed in the nonver- bal literature. For years social psychologists have exalted the power of the situa- tion. We comfortably acknowledge that across different situations, with different people, we may act in a range of ways, or even talk using a variety of styles. Aware of this tendency, Gergen (1972) began his explorations of our shifting masks of identity. In writing letters to close friends, he realized that he came across as a "completely different per- son" in each letter. "In one, I was morose, pouring out a philosophy of existential sorrow; in another I was a lusty realist: in a third I was a lighthearted jokester" (p. 32). Based merely on his word choices, Gergen inadvertently varied his style to adapt to the recipients of his letters. This is a prime demonstration of our inherent knowledge of the mutability of our language with respect to varying social contexts. Abstract Most paralinguistic analysis tasks are lacking agreed-uponevaluation procedures and comparability, in contrast to more‘traditional’ disciplines in speech analysis. The INTERSPEECH2010 Paralinguistic Challenge shall help overcome the usuallylow compatibility of results, by addressing three selected sub-challenges. In the Age Sub-Challenge, the age of speakers hasto be determined in four groups. In the Gender Sub-Challenge,a three-class classiﬁcation task has to be solved and ﬁnally, theAffect Sub-Challenge asks for speakers’ interest in ordinal rep-resentation. This paper introduces the conditions, the Challengecorpora “aGender” and “TUM AVIC” and standard feature setsthat may be used. Further, baseline results are given.Index Terms: Paralinguistic Challenge, Age, Gender, Affect 1. Introduction Most paralinguistic analysis tasks resemble each other not onlyby means of processing and ever-present data sparseness, but bylacking agreed-upon evaluation procedures and comparability,in contrast to more traditional disciplines in speech analysis. Atthe same time, this is a rapidly emerging ﬁeld of research, dueto the constantly growing interest on applications in the ﬁeldsof Human-Machine Communication, Human-Robot Communi-cation, and Multimedia Retrieval. In these respects, the INTER-SPEECH 2010 Paralinguistic Challenge shall help bridging thegap between excellent research on paralinguistic information inspoken language and low compatibility of results, by address-ing three selected tasks. The “aGender” and the “TUM AVIC”corpora are provided by the organizers. The ﬁrst consists of 46hours of telephone speech, stemming from 954 speakers, andserves to evaluate features and algorithms for the detection ofspeaker age and gender. The second features 2 hours of humanconversational speech recording (21 subjects), annotated in 5differentlevelsofinterest. Thecorpusfurtherfeaturesauniquelydetailed transcription of spoken content with word boundaries byforced alignment, non-linguistic vocalizations, single annotatortracks, and the sequence of (sub-)speaker-turns. Both are given