Design of a corpus of stimuli for a psycholinguistic study of lexical ambiguity

Lexical ambiguity takes place when a word has more than one meaning. This phenomenon could therefore lead to multiple difficulties in the processing of information; however, speakers deal almost effortlessly with ambiguous units on a daily basis. In order to understand how ambiguous items are processed by speakers, a clear synchronic definition of homonymy and polysemy is needed. In this paper a methodology to gather subjective information about ambiguous words and the relation within their meanings is proposed. Based on this methodology, a corpus of Spanish stimuli is being developed: this corpus consists of words classified as monosemic, homonymous and polysemous via the subjective interpretation of Spanish speakers. This corpus could be used to conduct experimental tasks to determine the behaviour in on-line processing of items with more than one meaning, in order to later design appropriate methods of approaching this complex phenomenon from the point of view of Psycholinguistics

Representation and processing of semantic ambiguity

One of the established findings in the psycholinguistic literature is that semantic ambiguity (e.g., “dog/tree bark”) slows word comprehension in neutral/ minimal context, though it is not entirely clear why this happens. Under the “semantic competition” account, this ambiguity disadvantage effect is due to competition between multiple semantic representations in the race for activation. Under the alternative “decision-making” account, it is due to decision-making difficulties in response selection. This thesis tests the two accounts by investigating in detail the ambiguity disadvantage in semantic relatedness decisions. Chapters 2-4 concentrate on homonyms, words with multiple unrelated meanings. The findings show that the ambiguity disadvantage effect arises only when the different meanings of homonyms are of comparable frequency (e.g., “football/electric fan”), and are therefore initially activated in parallel. Critically, homonymy has this effect during semantic activation of the ambiguous word, not during response selection. This finding, in particular, refutes any idea that the ambiguity disadvantage is due to decision making in response selection. Chapters 5 and 6 concentrate on polysemes, words with multiple related senses. The findings show that the ambiguity disadvantage effect arises for polysemes with irregular sense extension (e.g., “restaurant/website menu”), but not for polysemes with regular (e.g., “fluffy/marinated rabbit”) or figurative sense extension (e.g., “wooden/authoritative chair”). The latter two escape competition because they have only one semantic representation for the dominant sense, with rules of sense extension to derive the alternative sense on-line. Taken together, this thesis establishes that the ambiguity disadvantage is due to semantic competition but is restricted to some forms of ambiguity only. This is because ambiguous words differ in how their meanings are represented and processed, as delineated in this work

The problem of polyfunctionality in the traditional and computational linguistics

This article describes in detail the study of multifunctional words in traditional linguistics by world and Uzbek scientists. Its relation to concomitant phenomena – homonymy and polysemy is expressed. It is noted that in computational linguistics, the question about the functional vocabulary is not understood, not solved the question of its purpose in the housing, on the agenda posed the need for prompt decision of this question by establishing a theoretical framework for the differentiation of polyfunctional vocabulary in information retrieval system

A comparison of homonym meaning frequency estimates derived from movie and television subtitles, free association, and explicit ratings

First Online: 10 September 2018Most words are ambiguous, with interpretation dependent on context. Advancing theories of ambiguity resolution is important for any general theory of language processing, and for resolving inconsistencies in observed ambiguity effects across experimental tasks. Focusing on homonyms (words such as bank with unrelated meanings EDGE OF A RIVER vs. FINANCIAL INSTITUTION), the present work advances theories and methods for estimating the relative frequency of their meanings, a factor that shapes observed ambiguity effects. We develop a new method for estimating meaning frequency based on the meaning of a homonym evoked in lines of movie and television subtitles according to human raters. We also replicate and extend a measure of meaning frequency derived from the classification of free associates. We evaluate the internal consistency of these measures, compare them to published estimates based on explicit ratings of each meaning’s frequency, and compare each set of norms in predicting performance in lexical and semantic decision mega-studies. All measures have high internal consistency and show agreement, but each is also associated with unique variance, which may be explained by integrating cognitive theories of memory with the demands of different experimental methodologies. To derive frequency estimates, we collected manual classifications of 533 homonyms over 50,000 lines of subtitles, and of 357 homonyms across over 5000 homonym–associate pairs. This database—publicly available at: www.blairarmstrong.net/homonymnorms/—constitutes a novel resource for computational cognitive modeling and computational linguistics, and we offer suggestions around good practices for its use in training and testing models on labeled data

SEMANTIC ANALYSIS ON LEXICAL RELATIONS IN PUJAKESUMA LANGUAGE

Human beings need language as their communication because language as the branch of linguistic. When all people speak in a communication is a process and then, there must be both sender and receiver. Sometimes, all people only talk interaction not only directly interact and something more meaningless. In the branch of linguistics, semantic is the study about all the aspects of meaning outside the grammatical of language which different with morphology and syntax that concern with grammatical of language. In this paper, the writer interest to discuss about semantic analysis on lexical in Pujakesuma because semantics is one of the important aspect in linguistics and lexical of Javanese society nomads in Sumatera Utara (Pujakesuma)  is also have different meaning to be analyzed

The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing

We present the Bayesian Information-Theoretical (BIT) model of lexical processing: A mathematical model illustrating a novel approach to the modelling of language processes. The model shows how a neurophysiological theory of lexical processing relying on Hebbian association and neural assemblies can directly account for a variety of effects previously observed in behavioural experiments. We develop two information-theoretical measures of the distribution of usages of a morpheme or word, and use them to predict responses in three visual lexical decision datasets investigating inflectional morphology and polysemy. Our model offers a neurophysiological basis for the effects of morpho-semantic neighbourhoods. These results demonstrate how distributed patterns of activation naturally result in the arisal of symbolic structures. We conclude by arguing that the modelling framework exemplified here, is a powerful tool for integrating behavioural and neurophysiological results

Modeling the Acquisition of Words with Multiple Meanings

Learning vocabulary is essential to successful communication. Complicating this task is the underappreciated fact that most common words are associated with multiple senses (are polysemous) (e.g., baseball cap vs. cap of a bottle), while other words are homonymous, evoking meanings that are unrelated to one another (e.g., baseball bat vs. flying bat). Models of human word learning have thus far failed to represent this level of naturalistic complexity. We extend a feature-based computational model to allow for multiple meanings, while capturing the gradient distinction between polysemy and homonymy by using structured sets of features. Results confirm that the present model correlates better with human data on novel word learning tasks than the existing feature-based model