Burnout and Depression Detection Using Affective Word List Ratings

Burnout syndrome and depression are prevalent mental health problems in many societies today. Most existing methods used in clinical intervention and research are based on inventories. Natural Language Processing (NLP) enables new possibilities to automatically evaluate text in the context of clinical Psychology. In this paper, we show how affective word list ratings can be used to differentiate between texts indicating depression or burnout, and a control group. In particular, we show that depression and burnout show statistically significantly higher arousal than the control group

Concreteness and imageability differentially predict judgments of manual and visual similarity

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Is Gender Reference Gender-specific? Studies in a Polar Domain

We investigate how gender authorship influences polar, i.e. positive and negative gender reference. Given German-language newspaper texts where the full names of the authors are known and their gender can be inferred from the first names. And given that nouns in the text have gender reference, i.e. are labeled by a gender classifier as female or male denoting nouns. If these nouns carry a polar load, they count towards the gender-specific statistics we are interested in. A polar load is given either via phrase-level sentiment composition, or by a verb-based analysis of the polar role a noun (phrase) plays: is it framed by the verb as a positive or negative actor, or as receiving a positive or negative effect? Also, reported gender-gender relations ({in favor, against}) might be gender-specific. Statistical hypothesis testing is carried out in order to find out whether significant gender-wise correlations exist. We found that, in fact, gender reference is gender-specific: each gender significantly more often focuses on their own gender than the other one and e.g. positive actorship supremacy is claimed (intra-) gender-wise

Aging affects steaks more than knives: Evidence that the processing of words related to motor skills is relatively spared in aging

Lexical-processing declines are a hallmark of aging. However, the extent of these declines may vary as a function of different factors. Motivated by findings from neurodegenerative diseases and healthy aging, we tested whether ‘motor-relatedness’ (the degree to which words are associated with particular human body movements) might moderate such declines. We investigated this question by examining data from three experiments. The experiments were carried out in different languages (Dutch, German, English) using different tasks (lexical decision, picture naming), and probed verbs and nouns, in all cases controlling for potentially confounding variables (e.g., frequency, age-of-acquisition, imageability). Whereas ‘non-motor words’ (e.g., steak) showed age-related performance decreases in all three experiments, ‘motor words’ (e.g., knife) yielded either smaller decreases (in one experiment) or no decreases (in two experiments). The findings suggest that motor-relatedness can attenuate or even prevent age-related lexical declines, perhaps due to the relative sparing of neural circuitry underlying such words

Variety, flexibility, and use of abstract concepts. A multiple grounded perspective.

The nature of concepts is a matter of intense debate in cognitive sciences. While traditional views claim that conceptual knowledge is represented in a unitary symbolic system, recent Embodied and Grounded Cognition theories (EGC) submit the idea that conceptual system is couched in our body and influenced by the environment (Barsalou, 2008). One of the major challenges for EGC is constituted by abstract concepts (ACs), like fantasy. Recently, some EGC proposals addressed this criticism, arguing that the ACs comprise multifaced exemplars that rely on different grounding sources beyond sensorimotor one, including interoception, emotions, language, and sociality (Borghi et al., 2018). However, little is known about how ACs representation varies as a function of life experiences and their use in communication. The theoretical arguments and empirical studies comprised in this dissertation aim to provide evidence on multiple grounding of ACs taking into account their varieties and flexibility. Study I analyzed multiple ratings on a large sample of ACs and identified four distinct subclusters. Study II validated this classification with an interference paradigm involving motor/manual, interoceptive, and linguistic systems during a difficulty rating task. Results confirm that different grounding sources are activated depending on ACs kind. Study III-IV investigate the variability of institutional concepts, showing that the higher the law expertise level, the stronger the concrete/emotional determinants in their representation. Study V introduced a novel interactive task in which abstract and concrete sentences serve as cues to simulate conversation. Analysis of language production revealed that the uncertainty and interactive exchanges increase with abstractness, leading to generating more questions/requests for clarifications with abstract than concrete sentences. Overall, results confirm that ACs are multidimensional, heterogeneous, and flexible constructs and that social and linguistic interactions are crucial to shaping their meanings. Investigating ACs in real-time dialogues may be a promising direction for future research

Eesti keele ühendverbide automaattuvastus lingvistiliste ja statistiliste meetoditega

Tänapäeval on inimkeeli (kaasa arvatud eesti keelt) töötlevad tehnoloogiaseadmed igapäevaelu osa, kuid arvutite „keeleoskus“ pole kaugeltki täiuslik. Keele automaattöötluse kõige rohkem kasutust leidev rakendus on ilmselt masintõlge. Ikka ja jälle jagatakse sotsiaalmeedias, kuidas tuntud süsteemid (näiteks Google Translate) midagi valesti tõlgivad. Enamasti tekitavad absurdse olukorra mitmest sõnast koosnevad fraasid või laused. Näiteks ei suuda tõlkesüsteemid tabada lauses „Ta läks lepinguga alt“ ühendi alt minema tähendust petta saama, sest õige tähenduse edastamiseks ei saa selle ühendi komponente sõna-sõnalt tõlkida ja seetõttu satubki arvuti hätta. Selleks et nii masintõlkesüsteemide kui ka teiste kasulike rakenduste nagu libauudiste tuvastuse või küsimus-vastus süsteemide kvaliteet paraneks, on oluline, et arvuti oskaks tuvastada mitmesõnalisi üksuseid ja nende eri tähendusi, mida inimesed konteksti põhjal üpriski lihtalt teha suudavad. Püsiühendite (tähenduse) automaattuvastus on oluline kõikides keeltes ja on seetõttu pälvinud arvutilingvistikas rohkelt tähelepanu. Seega on eriti inglise keele põhjal välja pakutud terve hulk meetodeid, mida pole siiamaani eesti keele püsiühendite tuvastamiseks rakendatud. Doktoritöös kasutataksegi masinõppe meetodeid, mis on teiste keelte püsiühendite tuvastamisel edukad olnud, üht liiki eesti keele püsiühendi – ühendverbi – automaatseks tuvastamiseks. Töös demonstreeritakse suurte tekstiandmete põhjal, et seni eesti keele traditsioonilises käsitluses esitatud eesti keele ühendverbide jaotus ainukordseteks (ühendi komponentide koosesinemisel tekib uus tähendus) ja korrapärasteks (ühendi tähendus on tema komponentide summa) ei ole piisavalt põhjalik. Nimelt kinnitab töö arvutilingvistilistes uurimustes laialt levinud arusaama, et püsiühendid (k.a ühendverbid) jaotuvad skaalale, mille ühes otsas on ühendid, mille tähendus on selgelt komponentide tähenduste summa. ja teises need ühendid, mis saavad uue tähenduse. Uurimus näitab, et lisaks kontekstile aitavad arvutil tuvastada ühendverbi õiget tähendust mitmed teised tunnuseid, näiteks subjekti ja objekti elusus ja käänded. Doktoritöö raames valminud andmestikud ja vektoresitused on vajalikud uued ressursid, mis on avalikud edaspidisteks uurimusteks.Nowadays, applications that process human languages (including Estonian) are part of everyday life. However, computers are not yet able to understand every nuance of language. Machine translation is probably the most well-known application of natural language processing. Occasionally, the worst failures of machine translation systems (e.g. Google Translate) are shared on social media. Most of such cases happen when sequences longer than words are translated. For example, translation systems are not able to catch the correct meaning of the particle verb alt (‘from under’) minema (‘to go’) (‘to get deceived’) in the sentence Ta läks lepinguga alt because the literal translation of the components of the expression is not correct. In order to improve the quality of machine translation systems and other useful applications, e.g. spam detection or question answering systems, such (idiomatic) multi-word expressions and their meanings must be well detected. The detection of multi-word expressions and their meaning is important in all languages and therefore much research has been done in the field, especially in English. However, the suggested methods have not been applied to the detection of Estonian multi-word expressions before. The dissertation fills that gap and applies well-known machine learning methods to detect one type of Estonian multi-word expressions – the particle verbs. Based on large textual data, the thesis demonstrates that the traditional binary division of Estonian particle verbs to non-compositional (ainukordne, meaning is not predictable from the meaning of its components) and compositional (korrapärane, meaning is predictable from the meaning of its components) is not comprehensive enough. The research confirms the widely adopted view in computational linguistics that the multi-word expressions form a continuum between the compositional and non-compositional units. Moreover, it is shown that in addition to context, there are some linguistic features, e.g. the animacy and cases of subject and object that help computers to predict whether the meaning of a particle verb in a sentence is compositional or non-compositional. In addition, the research introduces novel resources for Estonian language – trained embeddings and created compositionality datasets are available for the future research.https://www.ester.ee/record=b5252157~S

Behavioral Aspects of Corporate Decision - Making and Employment Restructuring

Employment restructuring represents a core strategic decision with far-reaching impact on a firm’s course of action (e.g., Cascio, Chatrath and Christie-David, 2021). Although having high practical relevance, prior research remains inconsistent regarding the antecendents (i.e., why firms restructure) and consequences (i.e., evaluative judgment by external stakeholders) of employment restructuring. To provide new nuances and insights to the antecedents and consequences of employment restructuring, this cumulative dissertation draws on the emerging socio-cognitive perspective in strategic management. Grounded in social psychology and socio-cognitive research, this perspective focuses on a) how the strategists’ socially construct perceptions influence their decision-making; and b) how stakeholders’ socio-cognitively perceive, interpret, and make sense of firms at the collective level (Rindova, Reger and Dalpiaz, 2012; Barnett, 2014; Pfarrer et al., 2019). Chapter 1 of this dissertations draws on the socio-cognitive perspective to examine how the socially influenced decision-maker inside an organization shapes employment restructuring (i.e., Study 1). Based on moral foundations theory (Haidt and Graham, 2007), the study argues and finds that CEOs moral stances impact the decision to restructure a firm’s workforce. Methodologically, the large-scale archival approval in chapter 1 leverages recent advances in digital technology and uses a novel psycholinguistic approach to operationalize the CEOs moral stances to understand their impact on employment restructuring (n = 218 observations). Chapter 2 and 3 incorporate socio-cognitive theories to understand how the mass media socio-cognitively perceives and makes sense of employment restructuring. For chapter 2 (i.e., Study 2), expectancy violation theory (Burgoon, 1993) and construal-level theory (Liberman and Trope, 2008) are employed to understand the impact of firms’ issuing employment restructuring on the tenor of media coverage as well as socio-cognitive framing tools to influence their behavior (n = 267 observations). Chapter 3, on the other hand (i.e, Study 3), examines media agents’ socio-cognitive construction processes to understand their sensemaking about employment restructuring (downsizing n = 527; upsizing n = 389). Dependent on the social approval of a firm, the underlying argument here is that media agents draft their stories about employment restructuring differently, as prior social approval act as a ‘cognitive shorthand’ to help them make sense of an organization’s action (Bitektine, 2011; Mishina, Block and Mannoer, 2012; Pfarrer et al., 2019). The two studies in chapter 2 and 3 employ computer-aided content-analysis to measure the media tenor about employment restructuring, finding strong support for the hypotheses

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)