Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userQuantifying Cognitive Factors in Lexical Decline
تحديد العوامل المعرفية في التراجع المعجمي
312
0 0
0.0
(
0
)
Created by
Shamra Editor
Ask ChatGPT about the research
مجردة أننا نعتمد وجهة نظر تطورية حول تغيير اللغة التي تؤثر العوامل المعرفية (بالإضافة إلى الخدمات الاجتماعية) على لياقة الكلمات ونجاحها في النظام الإيكولوجي اللغوي.على وجه التحديد، نقترح مجموعة متنوعة من العوامل النفسية --- الدلالية، التوزيع، والفونولوجي --- أننا نفترض أنها تنبؤية للانخفاض المعجمي، والكلمات تنخفض كثيرا في التردد مع مرور الوقت.باستخدام البيانات التاريخية عبر ثلاث لغات (الإنجليزية والفرنسية والألمانية)، نجد أن معظم عواملنا المقترحة تظهر فرقا كبيرا في الاتجاه المتوقع بين كل مجموعة من جميع الكلمات المنوية وكلماتها المستقرة المتطابقة.علاوة على ذلك، تظهر تحليل الانحدار اللوجستي أن العوامل الدلالية والتوزيع مهمة في التنبؤ بالكلمات المتنوعة.يكشف المزيد من التحليل DIACHRONIC أن انخفاض الكلمات يميل إلى الانخفاض في تنوع سياقاتهم المعجمية بمرور الوقت، وتضييق منافذهم البيئية تدريجيا.
Abstract We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors---semantic, distributional, and phonological---that we hypothesize are predictive of lexical decline, in which words greatly decrease in frequency over time. Using historical data across three languages (English, French, and German), we find that most of our proposed factors show a significant difference in the expected direction between each curated set of declining words and their matched stable words. Moreover, logistic regression analyses show that semantic and distributional factors are significant in predicting declining words. Further diachronic analysis reveals that declining words tend to decrease in the diversity of their lexical contexts over time, gradually narrowing their ecological niches'.
References used
https://aclanthology.org/
rate research
Read More
Lab studies in cognition and the psychology of morality have proposed some thematic and linguistic factors that influence moral reasoning. This paper assesses how well the findings of these studies generalize to a large corpus of over 22,000 descript
ions of fraught situations posted to a dedicated forum. At this social-media site, users judge whether or not an author is in the wrong with respect to the event that the author described. We find that, consistent with lab studies, there are statistically significant differences in uses of first-person passive voice, as well as first-person agents and patients, between descriptions of situations that receive different blame judgments. These features also aid performance in the task of predicting the eventual collective verdicts.
In lexical semantics, full-sentence segmentation and segment labeling of various phenomena are generally treated separately, despite their interdependence. We hypothesize that a unified lexical semantic recognition task is an effective way to encapsu
late previously disparate styles of annotation, including multiword expression identification / classification and supersense tagging. Using the STREUSLE corpus, we train a neural CRF sequence tagger and evaluate its performance along various axes of annotation. As the label set generalizes that of previous tasks (PARSEME, DiMSUM), we additionally evaluate how well the model generalizes to those test sets, finding that it approaches or surpasses existing models despite training only on STREUSLE. Our work also establishes baseline models and evaluation metrics for integrated and accurate modeling of lexical semantics, facilitating future work in this area.
This study recruited 51 elders aged 53-74 to discuss their daily activities in focus groups. The transcribed discourse was analyzed using the Chinese version of LIWC (Lin et al., 2020; Pennebaker et al., 2015) for cognitive complexity and dynamic lan
guage as well as content words related to elders' daily activities. The interruption behavior during the conversation was also coded and analyzed. After controlling for education, gender and age, the results showed that cognitive flexibility performance was accompanied by the increasing adoption of dynamic language, insight words and family words. These findings serve as the basis for predicting elders' cognitive flexibility through their daily language use.
The impact of the "crisis of comparative literature" (Renee Wilk) in the critical
center of the fire spread in the wild, although the vocabulary did not bear new added to the
efforts of critics and comparison in his time and before. From a research
er dealing with the
issues of comparative literature only pointed out a statement or hint To what Wilk
discussed in his crisis.
The pages of this research may be able to capture the knowledge and methodology of
WELC in formulating the points presented in its crisis, relying on extrapolation and
devising a means, and a tool for the fairness of the previous efforts objectively and
impartially, And its objectives.
Combining a pretrained language model (PLM) with textual patterns has been shown to help in both zero- and few-shot settings. For zero-shot performance, it makes sense to design patterns that closely resemble the text seen during self-supervised pret
raining because the model has never seen anything else. Supervised training allows for more flexibility. If we allow for tokens outside the PLM's vocabulary, patterns can be adapted more flexibly to a PLM's idiosyncrasies. Contrasting patterns where a token'' can be any continuous vector from those where a discrete choice between vocabulary elements has to be made, we call our method CONtinous pAtterNs (CONAN). We evaluate CONAN on two established benchmarks for lexical inference in context (LIiC) a.k.a. predicate entailment, a challenging natural language understanding task with relatively small training data. In a direct comparison with discrete patterns, CONAN consistently leads to improved performance, setting a new state of the art. Our experiments give valuable insights on the kind of pattern that enhances a PLM's performance on LIiC and raise important questions regarding our understanding of PLMs using text patterns.
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
