Lexical polysemy and homonymy in the Middle High German language in light of the problematic of lexical synonymy and lexical-semantic analysis

Based on the compositions of pre-courteous epic poetry and chivalric romance written in the Middle High German language, this article reviews the problem of lexical polysemy in relation to the phenomena of homonymy and synonymy, as well as the problem of structural description of lexis. The need for comprehensive examination of polysemous lexemes in the Middle High German language, which includes structural analysis of the meaning of polysemous word and the lexical-thematic group and/or synonymic row it belongs to, well as the study of contextual implementation of the meanings of polysemous word, is substantiated by the principle of diffusivity of meanings of polysemous word that complicates comprising dictionary definitions and creates difficulties for the researcher in distinguishing the meanings of a polysemous word and separating polysemy from homonymy. Based on the example of lexical-thematic group for denomination of edged weapon in the Middle High German Language, the author demonstrates the appropriateness of using lexical-semantic analysis for establishing systemic relations between the analyzed lexemes, as well as postulates the importance of the context in determination of the structure of polysemous word. Discussion of the given examples from the compositions of pre-courteous epic poetry and chivalric romance written in the Middle High German language is accompanied by the author’s clarifications to the dictionary definitions of the lexemes under review. The conclusion is made on feasibility of the authorial approach towards detection of the discrepancies between lexicographic data and use of the lexeme in the texts written in the Middle High German language. The author also believes that this research is valuable from the perspective of lexicographic practice.

Intelligent Recognition and Teaching of English Fuzzy Texts Based on Fuzzy Computing and Big Data

In this paper, we conduct in-depth research and analysis on the intelligent recognition and teaching of English fuzzy text through parallel projection and region expansion. Multisense Soft Cluster Vector (MSCVec), a multisense word vector model based on nonnegative matrix decomposition and sparse soft clustering, is constructed. The MSCVec model is a monolingual word vector model, which uses nonnegative matrix decomposition of positive point mutual information between words and contexts to extract low-rank expressions of mixed semantics of multisense words and then uses sparse. It uses the nonnegative matrix decomposition of the positive pointwise mutual information between words and contexts to extract the low-rank expressions of the mixed semantics of the polysemous words and then uses the sparse soft clustering algorithm to partition the multiple word senses of the polysemous words and also obtains the global sense of the polysemous word affiliation distribution; the specific polysemous word cluster classes are determined based on the negative mean log-likelihood of the global affiliation between the contextual semantics and the polysemous words, and finally, the polysemous word vectors are learned using the Fast text model under the extended dictionary word set. The advantage of the MSCVec model is that it is an unsupervised learning process without any knowledge base, and the substring representation in the model ensures the generation of unregistered word vectors; in addition, the global affiliation of the MSCVec model can also expect polysemantic word vectors to single word vectors. Compared with the traditional static word vectors, MSCVec shows excellent results in both word similarity and downstream text classification task experiments. The two sets of features are then fused and extended into new semantic features, and similarity classification experiments and stack generalization experiments are designed for comparison. In the cross-lingual sentence-level similarity detection task, SCLVec cross-lingual word vector lexical-level features outperform MSCVec multisense word vector features as the input embedding layer; deep semantic sentence-level features trained by twin recurrent neural networks outperform the semantic features of twin convolutional neural networks; extensions of traditional statistical features can effectively improve cross-lingual similarity detection performance, especially cross-lingual topic model (BL-LDA); the stack generalization integration approach maximizes the error rate of the underlying classifier and improves the detection accuracy.

Problems of the vocabulary description of the lexics of traditional material culture (on the example of the vocabulary of clothing in Russian dialects of the Perm region)

The article discusses the issues that the authors solved when describing the vocabulary of traditional peasant clothes and shoes in a thematic dictionary: highlighting thematic groups, including words in various groups, interpreting linguistic units, highlighting the meanings of a polysemous word, submitting phraseological and nominative combinations.

To smoke means ‘to drink’: about the obsolescence of some words meanings and its representation in the explanatory dictionary of the middle size

When compiling a dictionary of an explanatory dictionary of the middle size, lexicographers deal with the problem of the expediency of description of different vocabulary groups in the dictionary. The paper deals with some problems of the lexicographic description of obsolete words and meanings, special attention is paid to developing dictionary entries describing a polysemous word, a separate meaning of which is outdated.

MULTI-LEVEL STRUCTURE OF POLISEMOUS WORD: INVARIANT-CLUSTER ANALYSIS

Within the framework of the article, we have come up with a hypothesis of a polysemous word integrity as a multilevel structure, which is ensured by the dominant lexical invariant meaning. The analysis of the English polysemous word “key” is presented with the use of the empirical invariant-component method. Metaphorical clusters of meanings of this word and its lexical invariant are defined as a set of basic dominant components that form the semantic core of the word. The reasons for the ambiguity of the boundaries of the lexical meaning are substantiated, the patterns of schematization of the meanings of the word “key” are revealed. The use of lexical invariants can be useful for didactic purposes when using them as an alternative to memorizing lists of polysemous word meanings. The proposed method can be useful when compiling educational dictionaries. The representation of the semantic structure of words in the form of lexical invariants has a number of advantages over the “list theory”, it can significantly complement the theory of “general meaning”.

Temporal Conjunctions and Their Semantic Extensions: The Case of in Biblical Hebrew

Past analyses of have tended toward descriptive taxonomies or proposals of a highly abstract semantic core. Taxonomic approaches have the strength of descriptive rigour while proposals of an abstract core have the strength of offering a coherent analysis of its various uses. However, the former offer little or no explanation for the semantic variation of , and the latter simply attribute such variation to context. This paper argues that the best analysis of (or any such polysemous word) will both account for real variation in meaning without simply attributing it to context, and will also explain the principled connection between seemingly unrelated uses. Utilizing insights from cognitive semantics and grammaticalization theory, this paper will argue that temporal spans an internally complex semantic category, the various points of which served as the source for semantic extensions into its causal and conditional uses.

Cluster Analysis of Polysemous Word Semantic Structure in the Light of Invariant Theory

The aim of the article is to disclose specific features of the cluster method of material organization to the semantic structure analysis of polysemous words. The object of study includes the most frequent polysemous words of Russian and English languages. The words for study should have a developed semantic structure thanks to which intra-word connections and a picture of the semantic component selection in the process of decoding figurative meaning can be visually presented. The main method of analysis is component analysis and consideration of non-trivial semantic components. In the process of word analysis, we intend to discover what underlies the retention of meanings within a single structure. Initially, the idea bases on the existence of some invariant content, which is stored in the folded form in the mind of a native speaker. In the present study, the application of cluster theory to the organization of the polysemous word semantic structure not only confirms the invariant theory, but also brings it to a new level of understanding what the meaning of the word is and how we understand the figurative meaning. Representation of the semantic structure in the form of interconnected semantic components, united in clusters on the basis of a criterion, represents a new approach to understanding the phenomenon of polysemy and explaining the diffusivity of figurative meanings. The configuration of invariant and differential components in the decoding process provides an understanding of figurative meanings. We find confirmation of this theory in the works on neurolinguistics and cognitive psychology.

Techniques for Lexical Semantics in Hindi Language

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is constrained by non-availability of the standard corpora. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the available sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favourable results are achieved