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Semantic diversity refers to the degree of semantic variability in the contexts in which a particular word is used. We have previously proposed a method for measuring semantic diversity based on latent semantic analysis (LSA). In a recent paper, Cevoli et al. (2020) attempted to replicate our method and obtained different semantic diversity values. They suggested that this discrepancy occurred because they scaled their LSA vectors by their singular values, while we did not. Using their new results, they argued that semantic diversity is not related to ambiguity in word meaning, as we originally proposed. In this reply, we demonstrate that the use of unscaled vectors provides better fits to human semantic judgements than scaled ones. Thus we argue that our original semantic diversity measure should be preferred over the Cevoli et al. version. We replicate Cevoli et al.’s analysis using the original semantic diversity measure and find (a) our original measure is a better predictor of word recognition latencies than the Cevoli et al. equivalent and (b) that, unlike Cevoli et al.’s measure, our semantic diversity is reliably associated with a measure of polysemy based on dictionary definitions. We conclude that the Hoffman et al. semantic diversity measure is better-suited to capturing the contextual variability among words and that words appearing in a more diverse set of contexts have more variable semantic representations. However, we found that homonyms did not have higher semantic diversity values than non-homonyms, suggesting that the measure does not capture this special case of ambiguity.

The word similarity task is used to calculate the similarity of any pair of words, and is a basic technology of natural language processing (NLP). The existing method is based on word embedding, which fails to capture polysemy and is greatly influenced by the quality of the corpus. In this paper, we propose a multi-prototype Chinese word representation model (MP-CWR) for word similarity based on synonym knowledge base, including knowledge representation module and word similarity module. For the first module, we propose a dual attention to combine semantic information for jointly learning word knowledge representation. The MP-CWR model utilizes the synonyms as prior knowledge to supplement the relationship between words, which is helpful to solve the challenge of semantic expression due to insufficient data. As for the word similarity module, we propose a multi-prototype representation for each word. Then we calculate and fuse the conceptual similarity of two words to obtain the final result. Finally, we verify the effectiveness of our model on three public data sets with other baseline models. In addition, the experiments also prove the stability and scalability of our MP-CWR model under different corpora.

This article proposes that predicate order and coherence relations are the two major determining factors in copredication licensing, resolving a long-standing puzzle over the criteria for construct...

Natural language inference (NLI) task requires an agent to determine the semantic relation between a premise sentence ( ${p}$ ) and a hypothesis sentence ( ${h}$ ), which demands sufficient understanding about sentences semantic. Due to the issues, such as polysemy, ambiguity, as well as fuzziness of sentences, intense sentence understanding is very challenging. To this end, in this article, we introduce the corresponding image of sentences as reference information for enhancing sentence semantic understanding and representing. Specifically, we first propose an image-enhanced multilevel sentence representation net (IEMLRN), that utilizes the image features from pretrained models for enhancing the sentence semantic understanding at different scales, i.e., lexical-level, phrase-level, and sentence-level. The proposed model advances the performance on NLI tasks by leveraging the pretrained global features of images. However, as these pretrained image features are optimized on specific image classification datasets, they may not show the best performance on NLI tasks. Therefore, we further propose to design an adaptive image feature generator that extracts fine-grained image labels from the corresponding sentences. After that, we extend the IEMLRN to multilevel image-enhanced sentence representation net (MIESR) by utilizing not only the coarse-grained pretrained features of images, but also the fine-grained adaptive features of images. Therefore, sentence semantic can be evaluated and enhanced more comprehensively and precisely. Extensive experiments on two benchmark datasets (SNLI, SICK) clearly show our proposed IEMLRN significantly outperform the state-of-the-art baselines, and our proposed MIESR model achieves the best performance by considering not only the text but also images in an adaptive multigranularities way.

The cognitive process and one of its most difficult aspects – translation into different languages - is an obstacle to creating a perfect Artificial Intelligence, because translation is not limited to formulas. The accuracy of the terminology and the lack of ambiguity of the word are the purposes of artificial intelligence while limiting the development of linguistic thinking. The transhumanism involves the improvement of the man, as an unfinished link of evolution, based on his connection with technological elements. From the author`s point of view, the “man-machine” interaction offers a dubious advantage for the human civilization and the planetary ecological system. This is, first of all, anachronistic and its main goal is singularity, i.e., the management of intellectual energy within the strict framework of technology. The article schematically provides the explicit sequence of the translation process. Based on relevant examples, the author demonstrates the difference between mental operations of Artificial Intelligence and those of the man in the translation process. According to the author, translation is a universal feature of human thought, while the process of translating the thought into the speech form is due to biological-genetic, emotional, socio-historical memory and experience, as well as the possibility of the individual translation of the thought into the mother tongue and into any other language. Moreover, translation is one of the strongest survival qualities of homo sapiens.

For past few years, the World Wide Web has turned into an immense wellspring of client produced content and opinionative information. Utilizing web-based life, for example, Instagram, Facebook, twitter, etc., users share their views and feelings in an advantageous manner. Web-based social networking, for example, Instagram, Facebook, twitter, etc., where a huge number of individuals prompt their individual perspectives in their everyday communication, which may be their assumptions & sentiments about specific thing. These consistently developing emotional information are, without a doubt, an incredibly rich wellspring of data for any sort of basic leadership method. To computerize examination of such information, region of Sentiment Analysis has been developed. It goes for recognizing opinionative information in the Web & characterizing it as indicated by their polarities., regardless of whether they convey a positive/negative implication. Assessment Study is an issue of content-based examination, yet there are a few difficulties that make it troublesome when contrasted with conventional content-based investigation This unmistakably expresses there is need of an endeavor to move in the direction of these issues and it has opened up a few open doors for future research for taking care of refutations, concealed assumptions ID, slangs, polysemy. Be that as it may, the developing size of information requests programmed information examination strategies. In this paper, a literature review on various strategies utilized in the Sentiment Analysis is done to comprehend dimension of the work.

Knowledge graph embedding aims to learn the embedded representation of entities and relations in knowledge graphs which is very important for the subsequent link prediction task. However, two key issues are existed for learning knowledge graph embedding: 1) How to take full advantage of the deep learning algorithms to generate expressive embeddings? 2) How to solve the polysemy phenomenon caused by multi-relations knowledge graphs that entities and relations show different semantics after involving different predictions? In this article, to tackle the first problem, the multi-layer convolutional networks are adopted to generate features about entities and relations then used to predict candidate entity. Moreover, the representation power of the networks is strengthened by integrating an effective recalibration mechanism which can accentuate informative features selectively. To tackle the second problem, we propose to learn multiple specific interaction embeddings. Instead of directly learning one general embedding to preserve all information for each entity and relation, their interactions are captured to model the cross-semantic influence from relations to entities and from entities to relations. Compared to traditional embedding models, the proposed model can provide more generalization capabilities and effectively capture potential links between entities and relations. Experimental results have revealed that the proposed model achieves the state-of-the-art performance for general evaluation metrics on link prediction tasks.

Considering that most popular models solving aspect-level sentiment classification problems mainly focus on designing complicated neural networks to scale the importance of each word in the sentence, this paper addresses this problem from the view of semantic space. Motivated by the fact that the senses of a word can be sophisticatedly embedded into the semantic space using a distributed representation, this paper hypothesizes that each sense of a word can be represented by one or more specific dimensions, and thus the target of aspect-level sentiment classification can be simplified into searching the related dimensions for the aspects and sentiments concerned. Particularly, an Attention Vector (ATV) based on attention mechanism is designed for each aspect in terms of a specific task, which involves two sub-vectors, i.e., a Dimension Attention Vector (DATV) and a Sentiment Attention Vector (SATV). The DATV determines the significances of different dimensions based on their correlations with an aspect; and the SATV allocates weights for the attributes of words, which are decided by sentiment polarities and part-of-speech (PoS) tagging. Given a sub-dataset related to an aspect, the ATV will be optimized by an Artificial Bee Colony (ABC) algorithm with a Support Vector Machine (SVM) classifier, the objective of which is to maximize classification accuracy. Intrinsically, the DATV can reduce the ambiguity existing in polysemy, meanwhile, the SATV is an auxiliary means for the optimization of the DATV, which can help eliminate the misunderstandings caused by antonyms. Then, the optimized DATV will be applied on a Convolutional Neural Network (CNN) model via simply scaling the pretrained word embeddings as inputs (named as ATV-CNN model). Experimental results show that the ATV-CNN model can have substantial advantages when compared with state-of-the-art models.

The contemporary unsupervised word representation methods have been successful in capturing semantic statistics on various Natural Language Processing tasks. However, these methods proved to be futile in addressing tasks like polysemy or homonymy, which prevail in such tasks. There has been a rise in the number of state-of-the-art transfer learning techniques bringing into play the language models pre-trained on large inclusive corpus. Motivated by these techniques, the present paper proposes an efficacious transfer learning based ensemble model. This model is inspired by ULMFit and presents results on challenging sentiment analysis tasks such as contextualization and regularization. We have empirically validated the efficiency of our proposed model by applying it to three conventional datasets for sentiment classification task. Our model accomplished the state-of-the-art outcomes remarkably when compared to acknowledged baselines in terms of classification accuracy.

The topic model has been widely applied to various research domains such as information retrieval, data mining, and so on. It can discover topics of texts in an unsupervised way. In the early years, most researches mainly focused on long texts. With the emergence of the Internet, the number of short texts is growing rapidly. Most existing schemes to solve the sparsity problems of short texts, are mainly based on data aggregation or model improvements. Among them, the Biterm Topic Model is one of the most representative models. It proposed a new way to model topics based on document-level word pairs and has shown creativity and effectiveness. However, this strategy ignores those semantically similar and rarely co-occurrent word pairs. What’s more, most researches ignore the multi-sense phenomenon in natural languages. In this paper, we utilize multi-sense word vectors to extract similar word pairs from the whole corpus by considering multiple senses. Based on this idea, we introduce a novel short-text topic model, which disambiguates multiple senses of words and generates more reasonable global biterms. Experimental results on two open-source English datasets have shown superiority to state-of-the-art topic models.

Determining the similarity between short texts plays an important role in natural language processing applications such as search, query suggestion and automatic summary, which has attracted widespread attention. Unlike traditional long texts, short texts present the characteristics of short length, weak signal, and high ambiguity. Researchers have proposed many methods, from simple vector space models to more sophisticated distributed semantics. However, these methods only consider the literal meaning of words, ignoring the impact of word ambiguity and the semantic information contained in the structure of the short text. Additionally, words on their own are often insufficient for expressing semantics, as many terms are composed of multiple words. In this paper, we propose a method based on semantic and syntactic information for short text similarity calculations by using knowledge and corpora to express the meaning of the term to solve polysemy, and using a constituency parse tree to capture the syntactic structure of short texts. Additionally, the proposed method uses terms as semantic units. Experimental results on ground-truth datasets demonstrate that the proposed method outperforms baseline methods.

OBJECTIVE The aim: The paper is aimed at creation of the procedure and criteria for determining a health disorder associated with permanent disability as a sign of serious bodily harm. PATIENTS AND METHODS Materials and methods: To identify the problems faced by forensic medical and judicial practice in determining a health disorder associated with permanent disability, we studied more than 100 criminal proceedings from 2007 to the present time. RESULTS Results: Ways to further improvement of the procedure for conducting expert studies on health disorders, associated with persistent loss of general ability to work as a characteristic feature of the bodily harm have been found to avoid errors in forensic medical and judicial practice. The issues of conducting forensic medical examinations to determine the degree of loss of general ability to work remain unresolved. The lack of joint research projects conducted by both medical and legal scientists leads to the polysemy and different approaches in the stating of certain concepts that are the subject of study of both medical and law sciences. Currently, the definition of the offence against health is debatable and the issues of criteria for determining such damage are not completely settled to date. CONCLUSION Conclusions: We consider the development of the Procedure and Criteria for determining the degree (in percentage) of the permanent loss of general ability to work of victims of criminal offences, established by forensic medical experts, is crucial.

Along with the emergence of the Internet, the rapid development of handheld devices has democratized content creation due to the extensive use of social media and has resulted in an explosion of short informal texts. Although a sentiment analysis of these texts is valuable for many reasons, this task is often perceived as a challenge given that these texts are often short, informal, noisy, and rich in language ambiguities, such as polysemy. Moreover, most of the existing sentiment analysis methods are based on clean data. In this paper, we present D I C E T , a transformer-based method for sentiment analysis that encodes representation from a transformer and applies deep intelligent contextual embedding to enhance the quality of tweets by removing noise while taking word sentiments, polysemy, syntax, and semantic knowledge into account. We also use the bidirectional long- and short-term memory network to determine the sentiment of a tweet. To validate the performance of the proposed framework, we perform extensive experiments on three benchmark datasets, and results show that D I C E T considerably outperforms the state of the art in sentiment classification.

The article explores homonymous and tautological rhymes in K. Simonovs poetry as a form of language game. The study of rhymes in the authors poetry, as well as the appeal to the categories of play-element poetics in connection with his work, is realized for the first time. The term polysemantic rhyme is introduced to denote the transitional case between the two types of rhymes which is often found in Simonovs poetry. The dynamics of rhyme preferences and their functions throughout Simonovs work is revealed. In the early poems, he actively uses homonymous rhymes, that have a characterizing and world-modeling function. Rhyme becomes part of a more complex game and is reinforced by repetitions of similar-root and similar-sounding words, that create a dense semantic grid of important to Simonov concepts. In mature creativity, the poet often turns to polysemantic rhyme, and if he puts homonyms at the end of the line, they does not always rhyme with each other. While maintaining the previous functions, traditional humorous and compositional ones are added to them, the latter is associated with the separation of lines as independent aphorisms.

This ethnography examines how teachers interpret and enact language-in-education policies in an adult ESL classroom in the United States, where students simultaneously received job training as Cert...

This research paper aims to approach verbal techniques of humor creation in comic TV shows. Comedians usually play with words and expressions to create various readings. The various readings are the basis of humorous incongruity. Following Raskin’s (1985) Semantic Script Theory of Humour (SSTH), the research paper’s approach is semantic, aiming to analyze linguistically how comedians joke verbally in front of an audience in comic TV shows. The research paper also sheds light on whether or not semantic sense relations, especially homonymy and polysemy, are the basic techniques of verbal incongruity. Besides, figurative language and parody are approached to show to what extent they are creating elements of humor in comic TV shows. Cultural barriers are put into consideration throughout the analysis via the role of cultural literacy between the comedians and the audience. Consequently, the contribution of the research paper is the semantic approach via a qualitative framework to analyze techniques of verbal humor in addition to the role of culture in comic TV shows.

Cross-lingual information retrieval (CLIR) allows user query in a different language from the language of target resources. Thus, translation is the key element in the query processing. There are three translation approaches: query, document, or hybrid query-document. However, query translation is very challenging due to the polysemy problem. Different linguistic nature of the languages will lead to ambiguity of meaning subsequently user’s true intention could be misinterpreted. This paper presents a semantic technique on query translation for a multilingual knowledge repository to improve the query processing. Offline translated documents or parallel corpora in English, Arabic, and Malay language including Jawi text was used as the data. Set of keywords were constructed preidentified by expert related to prophetic food. These keywords were annotated with the relevant Quranic verses, Hadith texts, Manuscript text images and scientific article determined by expert. The synonym and context-based translation was annotated together with the specific keyword. A query will do a three-way pattern match based on the keyword indexing list that link to the relevant documents. A one-stop knowledge repository on prophetic food was developed as a proof of concept using sources are from al-Quran, Hadith, classical manuscript, and scientific articles verified by experts to ensure the content authenticity and integrity.

In natural language, the phenomenon of polysemy is widespread, which makes it very difficult for machines to process natural language. Word sense disambiguation is a key issue in the field of natural language processing. This paper introduces the more common statistical learning methods used in the field of word sense disambiguation. Using the naive Bayesian machine learning method and the feature vector set extracted and constructed by the Dice coefficient method, a semantic word disambiguation model based on semantics is realized. The results of comparative experiments show that the proposed method is better compared with known systems. This paper proposes a method for disambiguation of word segmentation in professional fields based on unsupervised learning. This method does not rely on professional domain knowledge and training corpus and only uses the frequency, mutual information, and boundary entropy information of the string in the test corpus to solve the problem of word segmentation ambiguity. The experimental results show that these three evaluation standards can solve the problem of word segmentation ambiguity in professional fields and improve the effect of word segmentation. Among them, the segmentation result using mutual information is the best, and the performance is stable.

As a written language used for thousands of years, Ancient Chinese has some special characteristics like complex semantics as polysemy and the one-to-many alignment with Modern Chinese. Thus it may be translated in a large number of fully different but equally correct ways. In the absence of multiple references, reference-dependent evaluations like Bilingual Evaluation Understudy (BLEU) cannot identify potentially correct translation results. The explore on automatic evaluation of Ancient-Modern Chinese Translation is completely lacking. In this paper, we proposed an automatic evaluation metric for Ancient-Modern Chinese Translation called DTE (Dual-based Translation Evaluation), which can be used to evaluate one-to-many alignment in the absence of multiple references. When using DTE to evaluate, we found that the proper nouns often could not be correctly translated. Hence, we designed a new word segmentation method to improve the translation of proper nouns without increasing the size of the model vocabulary. Experiments show that DTE outperforms several general evaluations in terms of similarity to the evaluation of human experts. Meanwhile, the new word segmentation method promotes the Ancient-Modern Chinese translation models perform better on proper nouns’ translation, and get higher scores on both BLEU and DTE.

Polysemy, when a single term has multiple meanings, and synonymy, when multiple terms have the same meaning, are common phenomena in linguistics as well as in scientific knowledge. In ontology engineering, it is vital to detect the synonyms annotations and the multiple inheritances because of polysemy. The persistence of these issues in the semantic description of a knowledge domain causes problematic interoperability and data processing. The disambiguation of the entities, properties and relationships sense in a semantic web ontology significantly improves linked data generation and information retrieval. We explore the synonymy and polysemy in the setting of a cardiology terminology generated from textbooks on the basis of field coverage, professionals’ associations’ recommendations and bibliometrics, for the building of a cardiologic ontology. From 56,134 terms collected we found that 67.7% were unique. The indexed terms included single words, compound words and multi-word expressions. The frequency of their appearances in the combined master index was calculated and used as a marker of their significance. To cope with the linguistic polysemy and synonymy of terms, we examined them in WordNet, MeSH and BioPortal, as well as by latent semantic analysis (LSA) through singular value decomposition (SVD). Through these approaches we managed to identify and decipher semantic associations and relationships between the terms. We proposed a roadmap for ontology building from scratch by utilizing intrinsic and extrinsic knowledge resources and reuse of metadata. We anticipate that this approach is applicable in ontology engineering of different knowledge domains for relationships setting and linked data contextualization

The article analyzes one of the early poems of D. S. Merezhkovsky, which refers to the so-called landscape lyrics. Sustained consistently in the third person and drawing a very realistic picture of nature before a thunderstorm, this poem becomes a kind of «landscape of the soul» of the lyrical hero from the very beginning. This is created primarily with the help of lexical-semantic means, in particular, the polysemy of most words in the text. At the same time, the parallelism «nature – a human being», traditional for Russian poetry, receives a peculiar embodiment here. Based on the analysis of the dictionary of the poem, the author determines the main images and motives, identifies the style-forming dominants of the text, and characterizes the features of the functioning of units of the lexical-semantic level.

This study investigates the concurrent and longitudinal relationships between polysemous word knowledge and reading comprehension among bilingual students (n = 107) followed from seventh to eighth grade. Standardized tests were used to measure decoding skills, broad vocabulary knowledge, and reading comprehension. Data about students’ polysemous word knowledge were collected using the Polysemous Word Test, an assessment designed to investigate students’ abilities to recognize the academic senses of words that also have casual, everyday meanings. The study provides longitudinal evidence that students’ knowledge of the academic senses of familiar words contributes to their future reading comprehension. Results from hierarchical multiple regression analyses indicated that students with better polysemous word knowledge in seventh grade show higher levels of grade eight reading comprehension, controlling for grade seven reading comprehension and other skills, compared to students with weaker polysemous word knowledge. Reading comprehension was found to predict polysemous knowledge in both seventh and eighth grade as well as between seventh and eighth grade.

The problem of recognition of the manipulative text in the news on the example of Kazakhstani media is posed. One of the most important tasks of text research in the context of manipulativeness is to find something that, although it has a linguistic representation, is obviously pragmatically “obscured”, hidden from the addressee. It is emphasized that in such cases, the danger of manipulation increases at times, since it is addressed both to large social groups and to society as a whole. The relevance of the work is that the destructive nature of manipulation can lead to large-scale social upheaval. The study is based on the hypothesis of a uniform structure of the manipulative text in the context of big data research for further machine processing. Based on the formed working definition of manipulability, an algorithm for analyzing media texts has been developed, which includes a complex of speech and language indicators. The work was carried out on a corpus of 1,000 media texts, selected from 10,000 fragments of manipulative discourses. Both unambiguous and mixed components are distinguished, that is, linguistic units capable of performing dual functions due to their semantic polysemy. A 4-component universal structure of manipulative texts is compiled, including language parameters extracted from manipulative texts.

The article reveals specific features of the translation from Spanish to Bulgarian of some comic word games/ puns in the American television series Silvana sin lana, especially those based on polysemy and paronymy, neologisms and certain proper names. Based on the requirements presented in the audio-visual translation, several translation proposals are analysed, including different options for translation compensations aimed at generating the same comic impact on the Bulgarian public as the original version exerts on Spanish-speaking viewers. The results show that this objective, however complicated it may seem, in many cases is achievable, although with certain inevitable losses. Nevertheless, on other occasions the word games have practically disappeared from the target text both for purely linguistic reasons, and because of the limitations of audio-visual translation caused by the interpenetration of several codes of different kinds.

The literature on higher education student success offers a considerable set of definitions of this concept, reflecting its semantic polysemy. This is understandable, inasmuch that the highly compe...

Well is an exemplary multi-functional word performing pragmatic and syntactic functions. That multi-functionality poses a potential problem: How do hearers in conversation determine which function is actualized and how do speakers project the function actualized? We address both questions examining factors hearers rely on to disambiguate well and the resources speakers deploy to designate well’s function. The study is based on 8-, 9-, and 10-word turns containing well extracted from the British National Corpus for which audio files from the Audio BNC are available. We include duration, measuring well’s durations in Praat. The workflow comprised both qualitative and quantitative methods. Qualitatively, all turns were manually inspected and the functions and subfunctions of well were identified. Due to data paucity the quantitative analysis was based only on a broad distinction between syntactic and pragmatic functions. The analysis involved two logistic regression model selection processes, one adopting a hearer, one a speaker perspective. Based on the factors position in the turn, duration and lexical context, our final models indicate that hearers disambiguate the two main functions of well drawing on lexical context and position in the turn while speakers project well’s functions by modulating duration. We propose that Hoey’s (2005) 6th priming hypothesis, concerned with polysemy, can be extended to also include polyfunctionality. Position also suggests a reading in terms of Hoey’s ‘textual colligation’ hypothesis related to a word’s position: particularly in its incarnation as a marker of dispreferreds, pragmatic well is heavily primed to occur turn-initially.

In the last century, the theme of an objectified mesorah (tradition) has been operative in, and at times central to, discussions relating to modernity within rabbinic discourse. This article traces...

The complexity of Lucian Blaga’s poetry is a matter of common knowledge. Part of this complexity is related to the elements of prosody that Blaga skilfully employs, to say nothing of the philosophical vein which infuses his writings, and which derives, understandably, from his philosophical work. Mention should also be made of the lyrical character of Blaga’s dramatic works, which adds significantly to the effort of translating his writings into English, or any other language for that matter. In what follows, we intend to offer a bird’s eye view of the volumes that have been translated into English and to analyse a selection of poems comparatively, in order to signal challenges and discrepancies, born in the process of transferring literary material from Romanian to English, and to point out what has been lost, and, if that be the case, what has been gained in the translation process.

This paper aims to investigate the effectiveness of teaching polysemous nouns through an innovative method that the researcher calls Similarity-Based Approach (SBA) and to compare it to the translation-based vocabulary instruction method (TBM). The objective of the study is to show how guiding students’ comprehension of a polysemous word through semantic relations improves their understanding. At the same time, it is the aim of the study to demonstrate the effectiveness of Similarity-Based instruction by teaching vocabulary through showing semantic relation of word meaning to young learners compared to the old, traditional way of memorizing pairs of words. The experiment is conducted with two groups of elementary students with a pre-test, a lesson and a post-test. The results in the post-test revealed that through SBA students remember the vocabulary better than they do through the traditional method of memorization. SBA might represent a new method in teaching polysemous vocabulary to young learners, while TBM implies a traditional, well rooted way of teaching polysemous words in ESL classrooms.

To objectively and quantitatively study transcribed protocols of design conversations, we apply a semantic analysis approach based on dynamic semantic networks of nouns. We examined the applicability of the approach focused on a dynamic evaluation of the design problem solving process in engineering design educational settings. Using a case of real-world case, we show that the approach is able to determine the time dynamics of semantic factors such as level of abstraction, polysemy, information content, and quantify convergence/divergence in engineering design conversations.

Introduction Communication in the biomedical sciences and clinical practice would be clearer if everyone used the same set of technical terms. Technical vocabularies, such as international standard terminologies, are attempts to avoid common linguistic problems, such as synonymy (many names for a single entity) and polysemy (many meanings for a single term). Materials and methods Efforts made in human anatomical nomenclature since the late 19th century to deal with these issues were reviewed. Results The new designations official term, equivalent term, synonym and related term are defined, and current challenges (e.g., eponyms) are identified. Discussion The addition of synonyms and related terms to the international standard anatomical terminology allows indexing of these terms to the official terms and evaluation of the relationships between terms.

This paper reports on a quasi-experimental study investigating the effects of a teaching methodology that combines Cognitive Linguistics (CL) and Corpus Linguistics (CrpL) insights on the acquisition of the two sets of English spatial prepositions of verticality, over/above and under/below. In addition, it is also concerned with learners’ attitudes towards the method of instruction. A total of 55 Moroccan first-year university students participated in this study, 22 of which were assigned to the experimental group, and 26 to the control group. The experimental group received what we termed the IPDDL instruction, which involves cognitive representations of the prepositions based on Image-schemas, the Principled Polysemy model, and Data-Driven Learning. The control group, in contrast, received a traditional instruction based on definitions and example sentences provided in dictionaries. Participants’ performance was measured with pre-, post-, and delayed post-tests in the form of a semantic judgment task and a gap-filling task. The findings demonstrate that the IPDDL method of instruction was significantly more effective in helping students acquire vertical prepositions, and its advantages proved durable posterior to two weeks of the instruction period. Thus, this study lends support to the applicability of a CL-CrpL-inspired methodology in second language instruction.

In the history of natural language processing (NLP) development, the representation of words has always been a significant research topic. In this survey, we provide a comprehensive typology of word representation models from a novel perspective that the development from static to dynamic embeddings can effectively address the polysemy problem, which has been a great challenge in this field. Then the survey covers the main evaluation metrics and applications of these word embeddings. And, we further discuss the development of word embeddings from static to dynamic in cross-lingual scenario. Finally, we point out some open issues and future works.

In this paper, we present a formalism for handling polysemy in spatial expressions based on supervaluation semantics called standpoint semantics for polysemy (SSP). The goal of this formalism is, given a prepositional phrase, to define its possible spatial interpretations. For this, we propose to characterize spatial prepositions by means of a triplet $\langle $image schema, semantic feature, spatial axis$\rangle $. The core of SSP is predicate grounding theories, which are formulas of a first-order language that define a spatial preposition through the semantic features of its trajector and landmark. Precisifications are also established, which are a set of formulae of a qualitative spatial reasoning formalism that aims to provide the spatial characterization of the trajector with respect to the landmark. In addition to the theoretical model, we also present results of a computational implementation of SSP for the preposition ‘in’.

Event extraction is a challenging problem in information extraction, designed to extract structured information from unstructured text. The existing event extraction methods are mostly based on the pipeline model and use the traditional language representation model for word embedding. It is impossible to model the polysemy and the features is very simple, besides, the pipeline model is also easy to generate cascading errors. Aiming at the above problems, we propose a joint event extraction model, which combines part-of-speech features, entity features and word representation of BERT model. Because of the BERT can’t capture local features, we use multi-CNN to capture the local features of different scales. and further use BiLSTM to capture context features to assist joint event extraction. Experimental results show that the precision, recall and F1 of the method on the CEC dataset are 88.83%, 90.91% and 89.86%, respectively, which are 1.61%, 3.6% and 2.6% higher than the PLMEE model.

In the comedies of Aristophanes, mythological paradigms undergo the usual mechanisms of comic distortion, based on polysemy and linguistic conventions. This paper analyzes the exempla that articula...

Most current automatic summarization methods are for English texts. The distinction between words in Chinese text is large, the types of parts of speech are many and complex, and polysemy or ambiguous words appear frequently. Therefore, compared with English text, Chinese text is more difficult to extract useful feature words. Due to the complex syntax of Chinese, there are currently relatively few automatic summarization methods for Chinese text. In the past, only the important sentences in the original text can be selected and simply arranged to obtain a summary with chaotic sentences and insufficient coherence. Meanwhile, because Chinese short text usually contains more redundant information and the sentence structure is not neat, we propose a topic-based automatic summary method for Chinese short text. Firstly, a key sentence selection method is proposed combining topic words and TF-IDF to obtain the score of each text corresponding to the topic in the original text data. Then the sentence with the highest score as the topic sentence of the topic is selected. Considering that the short text of Weibo may contain a lot of irrelevant information and sometimes even lack some important components of topic, three retouching mechanisms are proposed to improve the conciseness, richness and readability of topic sentence extraction results. We validate our approach on natural disaster and social hot event datasets from Sina Weibo. The experimental results show that the polished topic summary not only reflects the exact relationship between topic sentences and natural disasters or social hot events, but also has rich semantic information. More importantly, we can almost grasp the basic elements of natural disaster or social hot event from the topic sentence, so as to help the government guide disaster relief or meet the needs of users for quickly obtaining information of social hot events.

The task of relation recognition identifies semantic relationships between two named entities in a sentence. In neural network based models, a convolutional layer is often conducted to extract representative local features of a sentence. The convolution operation is implemented through a whole sentence, without considering the structure of a sentence. Because the task to recognize entity relation is processed in sentence level, many ambiguous phenomena (e.g., polysemy) are influential rather than in a document. Capturing structural information of a sentence is helpful to solve this problem. In this paper, a multi-channel framework is presented, which uses two named entities to divide a sentence into several channels. Each channel is stacked with layered neural networks. These channels do not interact during recurrent propagation, which enables a neural network to learn different representations. In our experiments, it outperforms the widely used position embedding approach. Comparing with the state-of-the-art approaches, its performance shows a meaningful improvement.

A pun is always humorous and has strong interactive value in people's daily communication. It creates a humorous effect in a certain context, in which a word implies two or more meanings by using polysemy (homographic pun) or phonological similarity to another word (heterographic pun). Pun location is a task to identify the pun word in a given text, which is of great significance to understand humorous texts. Existing methods generally adopt single long sequence structure but cannot well capture the rich semantics of pun words in sentences. We present an approach that considers long-distance and short-distance semantic relations between words simultaneously. For the long-distance semantic relation, we introduce multi-level embeddings to represent the most relevant aspects of the data. For the short-distance semantic relation, we exploit the complex-valued model with a self-adaptive selection mechanism based on multi-scale of input information. Meanwhile, we propose a new classification task to distinguish the homographic pun and heterographic pun. We introduce it as an auxiliary to jointly train the original pun location task, which first learns the location of different types of puns together. Experiment results show that the latest state-of-the-art results can be achieved through our model.

The article deals with the analysis of the main semantic processes in the English terminological system of biotechnology. The importance of our investigation lies in the fact that biotechnology is still a rather new sphere of human activity that is rapidly developing at present, thus the special language of biotechnology reflecting these rapid changes demands great attention on the part of terminologists as the new concepts should be nominated and different semantic processes described. The aim of the research is to define and describe terminological meanings of the English terms of biotechnology in comparison with other words of general language and terms of other spheres of human activity and analyze such semantic processes as polysemy and synonymy inside this terminology. In order to achieve the aim of the work it is necessary to study English biotechnology terms and their meanings on the basis of explanatory and terminological dictionaries and to analyze different semantic processes in the 113 English biotechnological terminology. The language material of our investigation is 1000 English biotechnology terms with the explanation of their terminological and general meanings. The words are taken from terminological and explanatory dictionaries. The analysis showed that many English biotechnology terms were formed by means of terminologization of common language words. The correlation of special and common language lexis is studied by means of selection of common semantic features (semes). Special emphasis in the articles is also laid on the study of polysemy and synonymy in English terminology of biotechnology

In Natural Language Processing, determining the semantic likeness between sentences is an important research area. For example, there exists many possible semantics for a word (polysemy), and the synonym of the word differs. Double LSTM (Long Short Term Memory) working at same time on double phrase sequences model is projected to overcome the solitary sequence problem. Furthermore, with the goal of overcoming the second issue, as indicated by the qualities of English dialect, we utilized the British corpus semantic similarity datasets structured by specialists to prepare, and validate the technique. During the training process the stopwords were reserved for use. Convolution Neural Network and Semantic Likeness model based on grammar are used to compare the results of our projected representation. The outcomes demonstrate that the proposed methodology is more prominent than the previous approaches by means of precision, recall rate, accuracy etc., along with the enhanced generalization potential of the neural network.

Technological determinism has been driving conceptions of technology enhanced learning for the last two decades at least. The abrupt shift to the emergency delivery of online courses during COVID-19 has accelerated big tech's coup d'etat of higher education, perhaps irrevocably. Yet, commercial technologies are not necessarily aligned with dialogic conceptions of learning while a technological transmission model negates learners' input and interactions. Mikhail Bakhtin viewed words as the multivocal bridge to social thought. His theory of the polysemy of language, that has subsequently been termed dialogism, has strong correlations with the semiotic philosophy of American pragmatist Charles Sanders Peirce. Peirce's semiotic philosophy of signs extends far beyond words, speech acts, linguistics, literary genres, and/or indeed human activity. This study traces links between Bakhtin's dialogism with Peirce's semiotics. Conceptual synthesis develops the semiotic-dialogic framework. Taking augmented reality as a theoretical case, inquiry illustrates that while technologies are subsuming traditional pedagogies, teachers and learners, this does not necessarily open dialogic learning. This is because technologies are never dialogic, in and of themselves, although semiotic learning always involves social actors' interpretations of signs. Crucially, semiotic-dialogism generates theorising of the visual literacies required by learners to optimise technologies for dialogic learning. © 2021 University Library System, University of Pittsburgh. All rights reserved.