scholarly journals Cognitive Metaphor and Discourse: Research Methods and Paradigms

2022 ◽  
pp. 108-121
Author(s):  
Oleg Kalinin
Keyword(s):  
Word Sense Disambiguation ◽  
The Other ◽  
Computer Assisted ◽  
Word Sense ◽  
Research Papers ◽  
Cognitive Metaphor ◽  
Sense Disambiguation ◽  
Points Of View ◽  
Basic Approaches ◽  
The One

The article dwells on a modern cognitive and discourse study of metaphors. Taking the advantage of the analysis and fusion of information in foreign and domestic papers, the researcher delves into their classification from the ontological, axiological and epistemological points of view. The ontological level breaks down into two basic approaches, namely metaphorical nature of discourse and discursive nature of metaphors. The former analyses metaphors to fathom characteristics of discourse, while the other provides for the study of metaphorical features in the context of discursive communication. The axiological aspect covers critical and descriptive studies and the epistemological angle comprises quantitive and qualitative methods in metaphorical studies. Other issues covered in the paper incorporate a thorough review of methods for identification of metaphors to include computer-assisted solutions (Word Sense Disambiguation, Categorisation, Metaphor Clusters) and numerical analysis of the metaphorical nature of discourse – descriptor analysis, metaphor power index, cluster analysis, and complex metaphor power analysis. On the one hand, the conceptualization of research papers boils down to major features of the discursive approach to metaphors and on the other, multiple studies of metaphors in the context of discourse pave the way for a discursive trend in cognitive metaphorology.

DISTRIBUTIONAL ANALYSIS OF RELATED SYNSETS IN WordNet FOR A WORD SENSE DISAMBIGUATION TASK

2005 ◽  
Vol 14 (06) ◽  
pp. 919-934 ◽  
Author(s):  
KOSTAS FRAGOS ◽  
YANIS MAISTROS
Keyword(s):  
Target Word ◽  
Goodness Of Fit ◽  
Hypothesis Test ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Reasonable Assumption ◽  
P Value ◽  
Word Sense ◽  
Chi Square ◽  
Sense Disambiguation

This work presents a new method for an unsupervised word sense disambiguation task using WordNet semantic relations. In this method we expand the context of a word being disambiguated with related synsets from the available WordNet relations and study within this set the distribution of the related synset that correspond to each sense of the target word. A single sample Pearson-Chi-Square goodness-of-fit hypothesis test is used to determine whether the null hypothesis of a composite normality PDF is a reasonable assumption for a set of related synsets corresponding to a sense. The calculated p-value from this test is a critical value for deciding the correct sense. The target word is assigned the sense, the related synsets of which are distributed more "abnormally" relative to the other sets of the other senses. Our algorithm is evaluated on English lexical sample data from the Senseval-2 word sense disambiguation competition. Three WordNet relations, antonymy, hyponymy and hypernymy give a distributional set of related synsets for the context that was proved quite a good word sense discriminator, achieving comparable results with the system obtained the better results among the other competing participants.

scholarly journals The diversity of lexical functions in Bulgarian and Russian: an approach to compatible digital comparative lexicography

2015 ◽  
pp. 53-70
Author(s):  
Svetlana Timoshenko ◽  
Olga Shemanaeva
Keyword(s):  
Language Learning ◽  
Word Sense Disambiguation ◽  
Important Application ◽  
Computer Assisted ◽  
Computer Assisted Language Learning ◽  
Word Sense ◽  
Slavic Languages ◽  
Transmission Problems ◽  
Sense Disambiguation ◽  
Academy Of Sciences

The diversity of lexical functions in Bulgarian and Russian: an approach to compatible digital comparative lexicographyThis paper presents an approach to the creation of Russian-Bulgarian digital dictionary of collocations using the apparatus of lexical functions. The project is aimed not only at the high-quality translation and word sense disambiguation but also at the cross-linguistic analysis and at comparing the semantics and compatibility of the words in Slavic languages (here: Russian and Bulgarian) by means of digital lexicographical data. Another important application is computer-assisted language learning: Bulgarian data can be incorporated in the educational project being developed for Russian and English at the Institute for Information Transmission Problems of the Russian Academy of Sciences.

scholarly journals Incremental Sense Weight Training for In-Depth Interpretation of Contextualized Word Embeddings (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13823-13824
Author(s):  
Xinyi Jiang ◽  
Zhengzhe Yang ◽  
Jinho D. Choi
Keyword(s):  
Online Algorithm ◽  
Weight Training ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Word Embeddings ◽  
Sense Disambiguation ◽  
The One

We present a novel online algorithm that learns the essence of each dimension in word embeddings. We first mask dimensions determined unessential by our algorithm, apply the masked word embeddings to a word sense disambiguation task (WSD), and compare its performance against the one achieved by the original embeddings. Our results show that the masked word embeddings do not hurt the performance and can improve it by 3%.

scholarly journals A Knowledge Based Word Sense Disambiguation in Telugu Language

2020 ◽  
Vol 10 (1) ◽  
pp. 440-445
Keyword(s):  
Computational Linguistics ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Word Sense ◽  
Knowledge Based ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
The Senses ◽  
Definition Of ◽  
Polysemous Words

Telugu (తెలుగు) is one of the Dravidian languages which are morphologically rich. As within the other languages, it too consists of ambiguous words/phrases which have one-of-a-kind meanings in special contexts. Such words are referred as polysemous words i.e. words having a couple of experiences. A Knowledge based approach is proposed for disambiguating Telugu polysemous phrases using the computational linguistics tool, IndoWordNet. The task of WSD (Word sense disambiguation) requires finding out the similarity among the target phrase and the nearby phrase. In this approach, the similarity is calculated either by means of locating out the range of similar phrases (intersection) between the glosses (definition) of the target and nearby words or by way of finding out the exact occurrence of the nearby phrase's sense in the hierarchy (hypernyms/hyponyms) of the target phrase's senses. The above parameters are changed by using the intersection use of not simplest the glosses but also by using which include the related words. Additionally, it is a third parameter 'distance' which measures the distance among the target and nearby phrases. The proposed method makes use of greater parameters for calculating similarity. It scores the senses based on the general impact of parameters i.e. intersection, hierarchy and distance, after which chooses the sense with the best score. The correct meaning of Telugu polysemous phrase could be identified with this technique.

scholarly journals Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences

2003 ◽  
Vol 29 (4) ◽  
pp. 639-654 ◽  
Author(s):  
Diana McCarthy ◽  
John Carroll
Keyword(s):  
Word Sense Disambiguation ◽  
Standard Test ◽  
Direct Object ◽  
Word Sense ◽  
Knowledge Sources ◽  
Test Corpus ◽  
Sense Disambiguation ◽  
Word Forms ◽  
Selectional Preferences ◽  
The One

Selectional preferences have been used by word sense disambiguation (WSD) systems as one source of disambiguating information. We evaluate WSD using selectional preferences acquired for English adjective—noun, subject, and direct object grammatical relationships with respect to a standard test corpus. The selectional preferences are specific to verb or adjective classes, rather than individual word forms, so they can be used to disambiguate the co-occurring adjectives and verbs, rather than just the nominal argument heads. We also investigate use of the one-senseper-discourse heuristic to propagate a sense tag for a word to other occurrences of the same word within the current document in order to increase coverage. Although the preferences perform well in comparison with other unsupervised WSD systems on the same corpus, the results show that for many applications, further knowledge sources would be required to achieve an adequate level of accuracy and coverage. In addition to quantifying performance, we analyze the results to investigate the situations in which the selectional preferences achieve the best precision and in which the one-sense-per-discourse heuristic increases performance.

scholarly journals What’s the Matter? Knowledge Acquisition by Unsupervised Multi-Topic Labeling for Spoken Utterances

2020 ◽  
pp. 43-66
Author(s):  
Sebastian Weigelt
Keyword(s):  
Knowledge Acquisition ◽  
User Study ◽  
Word Sense Disambiguation ◽  
Spoken Language ◽  
The Other ◽  
Word Sense ◽  
Bayes Classifier ◽  
Sense Disambiguation ◽  
Topic Labeling ◽  
Word Senses

Systems such as Alexa, Cortana, and Siri appear rather smart. However, they only react to predefined wordings and do not actually grasp the user’s intent. To overcome this limitation, a system must understand the topics the user is talking about. Therefore, we apply unsupervised multi-topic labeling to spoken utterances. Although topic labeling is a well-studied task on textual documents, its potential for spoken input is almost unexplored. Our approach for topic labeling is tailored to spoken utterances; it copes with short and ungrammatical input. The approach is two-tiered. First, we disambiguate word senses. We utilize Wikipedia as pre-labeled corpus to train a naïve-bayes classifier. Second, we build topic graphs based on DBpedia relations. We use two strategies to determine central terms in the graphs, i.e. the shared topics. One focuses on the dominant senses in the utterance and the other covers as many distinct senses as possible. Our approach creates multiple distinct topics per utterance and ranks results. The evaluation shows that the approach is feasible; the word sense disambiguation achieves a recall of 0.799. Concerning topic labeling, in a user study subjects assessed that in 90.9% of the cases at least one proposed topic label among the first four is a good fit. With regard to precision, the subjects judged that 77.2% of the top ranked labels are a good fit or good but somewhat too broad (Fleiss’ kappa κ = 0.27). We illustrate areas of application of topic labeling in the field of programming in spoken language. With topic labeling applied to the spoken input as well as ontologies that model the situational context we are able to select the most appropriate ontologies with an F1-score of 0.907.

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