Word Polysemy Research Articles

Gamify4LexAmb: a gamification-based approach to address lexical ambiguity in natural language requirements.

Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamif ication for Lex ical Amb iguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Sentiment analysis of tourism review text combined with bert-bilstm and attention mechanism

Word2vec is often used in text sentiment analysis to generate word vector, which maps the same word into the same vector. Although Word2vec plays a very good effect in the initial model training task, it still cannot solve the problems of polysemy and new use of old words, which leads to inaccurate extracted features and affects the final classification results. In this paper, BERT model was used to vectorize the review text of tourist attractions, and fusion attention mechanism and long and short-term memory model were used to extract the emotional features of the text for classification at the feature extraction layer. The emotional accuracy of the model proposed in this paper reached 95.79% in the review text of tourist attractions.

Homonym Detection Using WordNet and Modified Lesk Approach for English Language

Word sense disambiguation (WSD) is a basic and persistent problem that has existed since its inception in the natural language processing (NLP) area. The process of determining the accurate meaning of a word within a specific context is referred to as word sense disambiguation, commonly known as WSD. In NLP, a single word can have two or more meanings, with each meaning being distinguished by its context. This is known as word polysemy. Its applications span a wide range of fields, such as question answering systems, machine translation, information retrieval (IR) etc. Ontology and NLP are still struggling with ambiguity. Homonyms, which are ubiquitous in most languages, are words that have the same spelling but a different meaning. This method's fundamental premise is to select the appropriate sense by comparing a word's context in a sentence to contexts generated from WordNet. The primary goal of this study is to employ WordNet and the Lesk algorithm for WSD. After the algorithm was put into practice and tested on a collection of sentences that included ambiguous words, the synset was able to determine the proper interpretation for most of the sentences. The Lesk algorithm relies on finding the highest number of shared words (maximum overlap) between a word’s context, prepositions and the definitions for its different meanings(glosses). This approach helps in identifying the most accurate interpretation for a given word within a specific context. According to experimental findings, the suggested strategy considerably boosts performance while identifying homonyms.

On the problem of word polysemy in the Khakass language (on the example of the lexicon of expressive verbs)

The subject of the study are polysemantic Khakass verbs of colloquial style, the functional-semantic features of this category of verbs, as well as their semantic modifications, on the basis of which their figurative lexical-semantic variants are formed. Polysemous colloquial verbs, due to their wide situational-pragmatic potential, usually have the most complex, generalising meaning, bordering on desemantisation, and therefore are not always recorded in lexicographic sources. The relevance of the study is due to the fact that there are practically no works devoted to the study of the colloquial and everyday style of the Khakass language. Moreover, this topic is closely related to the concept of expressiveness, which has not been sufficiently studied with the help of Khakass material. The aim of the article is to identify and describe polysemous verbs of the colloquial style from a functional-semantic point of view. In achieving this goal, the following interacting issues were raised: expressive style of language, the phenomenon of polysemy in language, semantic potential of a word, representation of expressive vocabulary in lexicographic sources, good knowledge of the native language as a guarantee of figurative and intuitive perception of the world around us, etc. Methods of distributional analysis and component analysis have been used in the work, which make it possible to identify combination possibilities and semantic features of the described polysemantic verbs, in particular to determine the grammatical, syntactic and semantic methods of forming expressions. In addition, verbs with an expressive frequency not recorded in the Khakass-Russian Dictionary [2006] were identified, as well as lexical-semantic variants of verbs used in everyday speech not listed in the dictionary entries. The material for the analysis were examples collected from the works of Khakass authors. The verbs analysed are, as a rule, a specific part of the colloquial language of the literary characters. Theoretical significance: new knowledge has been obtained in terms of describing polysemantic verbal lexemes of colloquial style, which will serve as theoretical support for further development of the phenomena of polysemy and expressiveness in Khakass and other Turkic languages. Practical significance can be expressed in the use of research materials in the compilation of ideographic dictionaries, textbooks, as well as in teaching activities. In our article, the concepts of "polysemy" and "expressiveness" are discussed within the framework of a summarised understanding of a whole complex of stylistic, emotional, colloquial and other phenomena of language. In this way, an attempt is made to identify the problem for further in-depth and thorough studies of this rich and unique layer of the Khakass lexical system.

Cascaded Semantic Fractionation for identifying a domain in social media.

Searching social media to find relevant semantic domains often results in large text files, many of which are irrelevant due to cross-domain content resulting from word polysemy, abstractness, and degree centrality. Through an iterative pruning process, Cascaded Semantic Fractionation (CSF) systematically removes these cross-domain links. The social network procedure performs community detection in semantic networks, locates the semantic groups containing the terms of interest, excludes intergroup links, and repeats community detection on the pruned intragroup network until the domain of interest is clarified. To illustrate CSF, we analyzed public Facebook posts, using the CrowdTangle app for historical data search, from February 3, 2020, to March 13, 2021, about the possible Wuhan lab leak of COVID-19 over a daily interval. The initial search using keywords located six multi-day bursts of posts of more than 500 per day among 95 K posts. These posts were network analyzed to find the domain of interest using the iterative community detection and pruning process. CSF can be applied to capture the evolutions in semantic domains over time. At the outset, the lab leak theory was presented in conspiracy theory terms. Over time, the conspiratorial elements washed out in favor of an accidental release as the issue moved from social to mainstream media and official government views. CSF identified the relevant social media semantic domain and tracked its changes.

Features of polysemy of words and terms of English origin in the Azerbaijani language

Features of polysemy of words and terms of English origin in the Azerbaijani language

Loanwords and polysemy: An investigation of specialized domain lexis in Middle English

This study investigates the polysemy of French loanwords in Middle English, analysing the attested senses of loan lexis in three occupational domains. Senses recorded in the Dictionnaire du moyen français and the Anglo-Norman dictionary were compared with corresponding lexical entries in the Middle English dictionary. Out of over 100 Middle English items borrowed from French, it is found that the great majority were polysemous in ways reflecting the source language semantics. This outcome contrasts with the usual finding that foreign borrowing in modern contexts produces monosemous loanwords. The conclusion is therefore drawn that the process of borrowing from French was of a different nature in the English medieval period. It is argued that bilingual users of French were agents of borrowing who replicated source word polysemy in their English. The likelihood that senses matching those of the French etymon arose in Middle English independently of source language polysemy is considered, but rejected.

The Interplay of Polysemy and Prototypes: A Prototype Theory Approach to the Turkish Polysemic –lIk Morpheme

In contrast to the classical Aristotelian view of categorization, which defines category membership based on compliance with necessary and sufficient conditions, the newly emerging Prototype Theory contends that categories are formed around prototypes, also known as “best examples”. A particularly relevant set of data stems from the observation that speakers do not assign an equal degree of membership to all members within a given category, a phenomenon known in the field as “prototype effects”. On the linguistic side of the discussion, proponents of Prototype Theory argue that the polysemy of linguistic elements, whether morphemes, words, or syntactic structures, should be analyzed in a manner analogous to categories. Just as categories have “prototypical” or “exemplar” members, polysemous linguistic objects have “prototypical” or “exemplar” interpretations, from which non-prototypical, slightly deviant interpretations are derived. This study aims to characterize the polysemous Turkish suffix –lIk through a prototypical analysis, operating on the assumption that language is a dynamic and evolving entity that interacts bidirectionally with cognitive structures. It is proposed that the myriad of interpretations associated with –lIk can be captured around a prototypical core meaning of “property-based relation”, and that other readings can be derived from this core by slight meaning alterations. I also incorporate Langacker’s theory, positing that non-prototypical interpretations may also act as a node from which both prototypical and non-prototypical interpretations branch. Our findings indicate that Prototype Theory offers a more fitting analytical framework for understanding ¬–lIk compared to the classical Aristotelian view, which proves less adequate for this analysis.

Bert-CK: A study of user profile classification based on Bert and CK-means+ fusion

In traditional user portrait construction methods, static word vectors can extract only shallow semantic representations, which cannot manage word polysemy. Moreover, the common clustering algorithm K-means has the problems of initial K values and unstable initial centroid selection. A Bert-CK model based on Bert and CK-means+ is proposed. First, Bert is used to extract semantic and syntactic text features at various levels, and word vectors and sentence vectors are obtained according to the context. Then, the CK-means+ algorithm is improved based on canopy and mean calculation. Next, the K value and initial centroid are determined. The sentence vectors are input to CK-means+ to obtain user classification and topic features. Finally, semantic features and topic features are fused and classified. CK-means+ is evaluated on the Sogou user portrait dataset. The experimental results verify that Bert-CK is better than the baseline model.

Sentiment Analysis of Social Network Comment Text Based on LSTM and Bert

This paper has the aim of solving problems in research studies on the analysis tasks of text emotion; the problems are the low utilization of text, the difficulty of effective information extraction, the failure of recognizing word polysemy with effectiveness. Thus, based on LSTM and Bert, the method of sentiment analysis on text is adopted. To be precise, word embedding of dataset in view of the skip-gram model is used for training course. In each sample, the word embeddings combine matric with the two-dimensional feature to be neural network input. Next, construction of analysis model for text sentiment combines Bert pre-training language model and long short-term memory (LSTM) network, using the word vector pre-trained by Bert instead of that trained in the traditional way to dynamically generate the semantic vector according to the word context. Finally, the semantic representation of words from text is improved by effectively identifying the polysemy of words, and the semantic vector is input into the LSTM to capture the semantic dependencies, thereby enhancing the ability to extract valid information. The Accuracy, Precision, Recall and F-Measure for the method of Bert–LSTM based on analysis of text sentiment are 0.89, 0.9, 0.84 and 0.87, indicating high value than the compared ones. Thus, the proposed method significantly outperforms the comparison methods in text sentiment analysis.

Comparison of algorithms that use deep learning to classify spam

Although the application of network security protocols and cryptography provides a certain security guarantee for Internet surfing, it is difficult to cure the persistent security problems. Driven by the promotion of e-mail technology and benefits, bad businesses will also issue promotional emails indiscriminately to a large number of mailboxes, and even drive the underground industry of private mailbox information trading. The existing spam filters use black and whitelists, sensitive word matching and other technologies, but they can not effectively filter all forms of spam, and non-spam is often filtered, which brings more trouble to users. With the rise of artificial intelligence, machine learning algorithms have been applied to spam recognition, such as decision tree algorithm, Boosting algorithm, K nearest neighbour algorithm, SVM support vector machine algorithm, Bayesian principle related algorithms, etc. These methods based on traditional statistics can intelligently classify data sets with large differences and are often used together with expert systems with certain rules to classify spam. However, with the diversification of spam types, the old classification rules are relatively rigid, and new types of mail will be misjudged. In addition, statistics based natural language processing method is based on pre trained fixed dictionaries. For new words and polysemy words, it is impossible to give word vectors with accurate semantics, which brings difficulties to classification. This paper mainly studies the application of five machine learning algorithms in spam detection: improved naive Bayes algorithm, A Lite Bidirectional Encoder Representations from Transformer (ALBERT) dynamic word vector algorithm, Bidirectional Gating Recurrent Unit (BiGRU) algorithm, the Inverted Multi-Index with Weighted Naive Bayes (IMI-WNB) algorithm and clustering analysis algorithm.

Desiring the Sweet Perfume of Closeness in the Oscillating Tawajjuh of the Letter Rāʾ

This article delves into the concept of tawajjuh through a poem and a prayer ascribed to the Arabic letter rāʾ, which expresses key themes in the Akbarian tradition. Using the hermeneutical approach of Ibn ʿArabī to interpret word polysemy in the texts, the article sheds light on the science of letters and key metaphysical ideas cultivated in this tradition. The letter rāʾ represents various aspects of cosmic duality and hence a strong desire for intimacy. The Arabic word tawajjuh, meaning the projection of spiritual energy, orientation, or attentiveness, refers to turning to face God. There is contemplation and continuous turning, like the phases of the moon facing the sun.

A Hybrid Model Based on Deep Convolutional Network for Medical Named Entity Recognition

The typical pretrained model’s feature extraction capabilities are insufficient for medical named entity identification, and it is challenging to express word polysemy, resulting in a low recognition accuracy for electronic medical records. In order to solve this problem, this paper proposes a new model that combines the BERT pretraining model and the BilSTM-CRF model. First, word embedding with semantic information is obtained by pretraining the corpus input to the BERT model. Then, the BiLSTM module is utilized to extract further features from the encoded outputs of BERT in order to account for context information and improve the accuracy of semantic coding. Then, CRF is used to modify the results of BiLSTM to screen out the annotation sequence with the largest score. Finally, extensive experimental results show that the performance of the proposed model is effectively improved compared with other models.

DictPrompt: Comprehensive dictionary-integrated prompt tuning for pre-trained language model

The textual semantics contained in the PLM (Pre-trained Language Model) is constrained by the text distribution in the original training corpus. Due to the lack of sufficient contextual training corpus, the low-frequency word representations in the PLM often have difficulty capturing their actual semantics. Previous research has shown that using semantic information from dictionaries can alleviate this problem. Unfortunately, these works neglected the infinite potential of example sentences from different target words with various meanings. To re-explore methods for enhancing PLM using the dictionary, we propose a novel Comprehensive Dictionary-based tuning approach integrating the latest Prompt learning (DictPrompt). We first collect a dataset based on the Oxford Advanced Learner’s English Dictionary. Then, we designed a set of comprehensive prompt templates with the corpus combining the word, the definition, and its example sentence. Finally, we insert a word game training task between pre-training and fine-tuning using these templates, allowing the model to inject more semantic information into PLM. We test our Dictprompt tuning method on three commonly used PLMs. The testing results on five fine-grained semantic tasks show that our dictionary-based secondary tuning can bring additional gains to the model’s performance. The best accuracy improves 3.09% on average with our tuning on the WiC task and 7.93% on the WSC task. We also plot the sentence embedding scatters of polysemy words. Our method can smooth the decision boundary and help the model output more distinguishable embedding. The code is available at https://github.com/xbdxwyh/Dictprompt.

The Polysemous Lexemes in the Kazakh Dictionary Written in Cyrillic of the XIX Century

In modern Turkological science, it is relevant to study the language of Turkic written legacies written by various graphic systems as Runic, Uighur, Arabic, Latin, Cyrillic. In this context, Kazakh legacies written in Cyrillic at the end of the XIX – early XX century have not been sufficiently studied to date. The article examines how the Turkic peoples, including Kazakhs, adopted Cyrillic script around the turn of the nineteenth and twentieth centuries, when the first Cyrillic Kazakh ABC books, self-help books, dictionaries, and other landmarks were published by P.M. Melioransky, N.I. Ilminsky, M.A. Terentyev, V.V. Katarinsky, L. Budagov, and others. I. Altynsarin, A. Baitursynov, T. Bokin, Zh. Kusherbayev, M. Mashanov, and others were among the Kazakh intellectuals who actively started using the Cyrillic alphabet at that time as the enlighteners of the nation. Among Cyrillic publications bilingual dictionaries held a unique position, providing a glimpse into the Kazakhs' way of life during a particular historical period. Due to the fact that they contained both linguistic and ethno-cultural knowledge. The article is devoted to one such dictionary, in particular the «Kyrgyz-Russian Dictionary», which was released in 1897 in Orenburg by B.A. Breslin's publishing company. The unnamed dictionary was created using N.I. Ilminsky's dictionary, «Materials for the study of the Kyrgyz dialect» (Kazan, 1861) that was expanded upon and amended with the help of the Kazakh intellectuals. The article examines the polysemy of these dictionary's words. The polysemous meanings of this dictionary are exposed, together with their lexical and semantic variations, on the basis of the notions of polysemy. This also reveals the continuity of this dictionary polysemous meanings with the lexemes of the contemporary Kazakh language.

MAKNA KATA AULIYA’ DALAM PERSPEKTIF PENDIDIKAN ISLAM

This article is an offer to understand the meaning of Auliya and its derivation in the Qur'an. Namely as a way to understand the meaning of auliya' according to Mufassir M. Quraish Shihab who is the main reference in interpretation in Indonesia. In this article, the author uses a qualitative research method, which is a method used to explore the meaning contained in it. In the research, the writer refers to the interpretation of Prof. M. Quraish Shihab to explore the meaning of auliya 'in QS. al-Maidah verse 51 and its derivation. The purpose of this paper is to reveal the meaning of the word auliya 'in QS. al-Maidah verse 51 which can be understood that auliya 'is a polysemy word which has the meaning of leader and lover. From the results of this study that M. Quraish Shihab's interpretation of the word auliya 'in the sense of love (mawaddah). Islamic education has an important position in human life, shaping humanity in accordance with the laws and values of Islamic teachings. The meaning of the word auliya which means thefabric of love (Mawaddahh) is very important in developing students so that they can understand and practice the results of Islamic education as a whole so that they have maturity in faith and piety, in accordance with the provisions contained in the Qur'an and al-Hadith

Enhancing Text Classification by Graph Neural Networks With Multi-Granular Topic-Aware Graph

Text classification based on graph neural networks (GNNs) has been widely studied by virtue of its potential to capture complex and across-granularity relations among texts of different types from learning on a text graph. Existing methods typically construct text graphs based on words-documents to capture relevant intra-class document representations among the same documents via words-words and words-documents propagation. However, a natural problem is that polysemy words in documents may become an information medium between documents of different categories, promoting heterophily information propagation. The performance of text classification will be somewhat constrained by this issue. This paper proposes a novel text classification method based on GNN from multi-granular topic-aware perspective, referred to as Text-MGNN. Specifically, topic nodes are introduced to build a triple node set of “word, document, topic,” and multi-granularity relations are modeled on a text graph for this triple node set. The introduction of topic nodes has three significant advantages. The first is to strengthen the propagation of topics, words, and documents. The second is to enhance class-aware representation learning. The final is to mitigate the effect of heterophily information caused by polysemy words. Extensive experiments are conducted on three real-world datasets. Results validate that our proposed method outperforms 11 baselines methods.

Терпкий вкус спасения (к вопросу использования Климентом Александрийским корня -στυφ-)

For describing the opposite (punishing and encouraging) methods of divine pedagogy, Clement of Alexandria used metaphors related to taste and medicine. God appears to him to be not a strict judge, punishing sinners, but a caring and wise doctor, prescribing bitter/tart medicine for them. Clement compares harsh but salutary experiences (mental suffering, fear) with astringents. He interprets physical punishment, which is written about in the Book of Proverbs (Prov. 23:13-14), as moral. In his opinion, Hellenic philosophy formed and strengthened morals in advance (προ-τυποῦσα καὶ προ-στύφουσα) (Strom. I, 16, 80, 6), and he intends to reinforce those with the help of the divine Scriptures, using the latter as an astringent (ἐπι-στύψω) (Paed. III, 2, 9, 1). Wateriness (ὑδαρότης, ὑδαρές, ὑδατῶδες) in some cases is associated with depravity, and reproof is regarded as an astringent medicine, relieving of excessive liquid and designed to stop the spread of sinful ulcers. The Alexandrian theologian draws the readers’ attention to points that are important to him, using both verbal play (for example, ἐπιστῦφον… σταφυλῆς) and the polysemy of words (for example, the verb ἐπι-στύφειν has the meanings ‘to exert an astringent effect’ and ‘to reprove’. Clement was not the only one who wrote about the usefulness of censure and astringents. In this he followed in the footsteps of authors such as Plato and Plutarch. The perception of God as a Physician became characteristic of the Orthodox Church and influenced the understanding of the essence of penance, considered as the healing of the penitent.

СЕМАНТИКО-ПАРАДИГМАТИЧНІ ВІДНОШЕННЯ ПОЛІСЕМІЇ ТА ОМОНІМІЇ

The article is devoted to the study of the phenomena of homonymy and polysemy, namely their demarcation. Among the numerous issues related to the problem of homonymy, the issue of distinguishing the phenomena of polysemy and homonymy is currently the most difficult, namely, the identification of lexical homonyms that arose as a result of disintegration of polysemy. Modern lexicographical practice points to the difficulty of distinguishing the studied phenomena and the difficulty of their clear, consistent differentiation. Many words that are presented as polysemous in one dictionary are considered in another as different words, homonymous with each other. The problem of distinguishing homonymy and polysemy may arise when homonyms appear as a result of semantic splitting of a polysemous word. At the same time, other words are formed based on different meanings of one word. On the basis of the analysis of existing linguistic concepts, the definition of polysemy is presented, the similarities and differences of individual fragments of related languages are systematized, and the main regularities of the occurrence of polysemy are studied. In the linguistic literature, there is no unity of opinion on the phenomenon of homonymy and on distinguishing it from polysemy. Some scientists recognize as homonyms only such lexical units that have the same sound, which initially differed in form and only in the process of historical development acquired the same sound due to various phonetic or other reasons. All other cases, when the same material, sound shell acquires a different meaning, are called the phenomenon of polysemy, word polysemy, others – words that, due to historical reasons, coincide in sound, are called homonyms, as well as those cases when different meanings of a polysemous lexical unit diverge so much that new (two or more) words arise. Whether it is possible to consider polysemy as a source of homonymy, or not to recognize the existence of homonyms, which are the result of the historical development of multiple meanings of the word, and how to distinguish between these two phenomena are the main questions that are considered in the article.