The Ambiguity in the Verb Words in the Epic

This article describes the historical and etymological aspects, lexical and semantic nature of the words used in the epos “Rustamkhan” and their ambiguous features. Polysemy plays a significant role in any language. Linguistic richness is measured not only by words and phrases, but also by the lexical meanings of words. The ambiguity of words is that the phenomenon of polysemy has its place in the richness of language [1, p. 3]. Based on the same principles, we tried to study the polysemous words used in the text of the epic “Rustamkhan” from a semantic, lexical-grammatical and genealogical point of view.

Polysemous Words in English Movies, Learning Obstacles or Gifted Talent?

The purpose of this paper is twofold: (1) to investigate how L2 learners deal with polysemous words or phrases without instruction about polysemy and (2) to observe what alternatives L2 learners have instead of instruction. In this paper, a case study was administered with three advanced college students. The material was an American movie, Café Society (Allen, 2016). All participants were tested three times with mostly polysemous words and phrases. It was found in the first test that the participants depended heavily on context to decide which senses of polysemous words were appropriate. The second test showed that the participants failed to handle the context because it was too difficult for them. In the third test, participants often misjudged the context and consequently made wrong choices among several senses. The results of the tests indicate that context is key to dealing with polysemous words or phrases. The pedagogical implication is that L2 students need context to deal with polysemous words or phrases. Teachers must instruct about the context. Image schemas and conceptual metaphors are only products of the interaction between context and polysemous words.

Representation of Different Types of Adjectival Polysemy in the Mental Lexicon

We studied mental representations of literal, metonymically different, and metaphorical senses in Russian adjectives. Previous studies suggested that in polysemous words, metonymic senses, being more sense-related, were stored together with literal senses, whereas more distant metaphorical senses had separate representations. We hypothesized that metonymy may be heterogeneous with respect to its mental storage. “Whole-part” metonymy (“sad person” – “sad eyes”), which is cognitively closer to the literal sense and more regular, should be stored differently from temporal, causal or resultative metonymy (“sad person” – “sad time;” “sad person” – “sad news;” “lead.ADJ ball” – “lead.ADJ poisoning”), which is irregular and semantically distant from the literal sense. We conducted an online experiment with semantic clustering task in which the participants were asked to classify sentences with a literal, proximal metonymic, distal metonymic, or metaphorical sense of an adjective into virtual baskets so that sentences with the same perceived sense were put in the same basket. Our results showed that proximal metonymies were grouped together with the literal sense and with each other more often than with distal metonymies and metaphors. Distal metonymies, in turn, were grouped with literal senses more often than with metaphors. Overall, we concluded that literal senses and proximal metonymies were stored in single representations, distal metonymies formed hybrid representations with literal senses, and metaphors were stored separately from literal senses. Additionally, we discovered that perception of semantic differences is affected by the surrounding senses: distal metonymies were more discernible from literal senses when presented with proximal metonymies, and less so when presented with metaphors.

The status of conventional metaphorical meaning in the L2 lexicon

This paper investigates how L2 speakers of English process conventional metaphorical expressions. While much of the literature on L2 processing of figurative expressions focuses on idioms only, the aim of this paper is to investigate how L2 speakers process conventional metaphorical expressions. The results of a cross-modal semantic priming task show that conventional metaphors have a special status in comparison to literal language in the L2 lexicon. The differences in reaction times show that L2 speakers are aware of the connections between literal primes and targets, resulting in slower reaction times, while this effect is not found in the metaphorical condition. This demonstrates that even when metaphorical language is very conventional, it can cause difficulties for L2 speakers. Furthermore, these results show that conventional metaphorical expressions can pose a semantic and pragmatic challenge for language learners, thus creating a need for explicit teaching of metaphorical meanings of polysemous words.

Translanguaging patterns in everyday urban conversations in Cameroon

This study analyses the translanguaging patterns of urban Cameroonians’ linguistic choices (e.g. lexical or phonological) in everyday conversations in Cameroon. Using observation and audio-recordings of 20 naturally occurring conversations as data, a descriptive corpus-based methodology was adopted for analysis. The quantitative approach utilises AntConc (Version 3.5.8) with descriptive analytical tools to identify the speakers’ idiolectal choices in meaning-making translanguaging patterns. The results revealed salient patterns of the speakers’ deployed lexical, grammatical, morphological, phonological and syntactical forms as an integrated system of language. It revealed the speakers preference for polysemous words (e.g. repe) over less polysemous words (e.g. father); choice for shorter lexical words (e.g. man) over longer words (e.g. manpikin); a preference for specialised gender-neutral markers (e.g. ih, which refers to both male and female) over gender-specific forms (e.g. he/she); a preference for voiceless interdental fricatives (e.g. dem, dey) over voiced interdental fricatives (e.g. them, they) and where the choice of inflectional morpheme expressing tense (e.g. ed) is one that can either be omitted or added to a word, the presence of this inflectional morpheme is sometimes fairly used. Such results have practical implications for understanding peoples’ language use as a translanguaging act in bi/multilingual contexts.

Using corpus methods to identify subject specific uses of polysemous words in English secondary school science materials

Many education professionals in Britain believe that school pupils have difficulty accessing academic texts because of inadequate knowledge of vocabulary. Previous research has suggested that some high frequency words used in non-specialised contexts have academic meanings that can cause problems for school pupils. We take corpus techniques used in the study of higher education texts and apply them to a corpus of texts designed for school pupils aged 11 to 14, attempting to identify such words automatically. We use the Spoken BNC2014 as a reference corpus. We identify a list of semi-technical words ( Baker, 1988 ), many of which are polysemous, having everyday meanings and related school subject meanings that may not be familiar to pupils. We investigate how semi-technical vocabulary can be identified and distinguished from both specialised and general vocabulary. Some supplementary qualitative analysis was needed, using collocation and concordance analysis. While time-consuming, the potential benefits for pupils struggling with school language make this a worthwhile exercise.

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.

Constraints of Attaining Pragmatic Relevance from English into Selected Luhya Varieties in Interpreter-mediated Sermons

Interpretation is an ultimate bridge among people who speak more than one language. In the case where the audience fails to understand the source language (SL), it is necessary to get the message to communicate with the target language (TL) speaker through an interpreter. This paper aims to evaluate the possible constraints of attaining pragmatic relevance during the delivery of interpreter-mediated sermons from English into selected Luhya varieties. The rationale for this position is that since English and Luhya belong to different language families, rendering information between these two languages can be very challenging. The Relevance Theory by Sperber and Wilson (1986) provided the background for discussing the data. Data was collected through Key-Informant Interviews for the interpreters, Focus Group Discussions for the congregants and the researcher’s non-participant observation during church services. The audio recording was used to collect corpus for analysis. The following constraints were revealed; grammatic and structural constraints, time lag, idiomatic expressions in the SL, lack of compatible hyponyms, phonological and prosodic constraints, semantic constraints, lack of lexicalized TL versions, culture-specific words in the SL and polysemous words. The findings also revealed that interpreters need to be aware of the constraints they face in interpreting sermons to determine the appropriate strategies to counteract the constraints.

The implications of polysemy for theories of word learning

Word learning is typically studied as a problem in which children need to learn a single meaning for a new word. According to most theories, children’s learning is itself guided by the assumption that a new word has only one meaning. However, most words in languages are polysemous, having many related and distinct meanings. In this article, we consider the implications of this disjuncture. As we review, current theories predict that children should struggle to learn polysemous words. Yet recent research shows that young children readily learn multiple meanings for words and represent them in ways that are qualitatively similar to adults. Moreover, polysemy may facilitate word learning by allowing children to use their knowledge of familiar meanings of a word to learn its other meanings. These findings motivate a new perspective on word learning that recognizes polysemy as a fundamental feature of language instead of treating it as an outlying case.