• Research in Mathematical Education
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• v.24 no.2
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• pp.69-82
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• 2021

The purpose of this study is to explore how mathematics educators understand the terms having lexical ambiguity. Five terms with lexical ambiguity, leave, times, high, continuous, and convergent were selected based on literature review and recommendations of college calculus instructors. The participants consisted of four mathematics educators at a large Midwestern university. The qualitative data were collected from open-ended items in the survey. As a result of analysis, I provided participants' sentences with five terms showing their understanding of each term. The data analysis revealed that mathematics educators were not able to separate the meanings of the words such as leave and high when these words are frequently used in daily life, and the meanings in mathematics context are similar with that in daily context. Lexical ambiguity shown by mathematics educators can help mathematics teachers to understand the terms with lexical ambiguity and improve their instructions when those terms should be found in students' conversations.

• Proceedings of the KSPS conference
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• 2003.10a
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• pp.79-82
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• 2003

The purpose of this study is to examine the regions of the cerebrum that handles the lexical and idiomatic ambiguity. The stimuli sets consist of two parts, and each part has 20 sets of sentences. For each part, 10 sets are experimental conditions and the other 10 sets are control conditions. Each set has two sentences, the 'context' and 'target' sentences, and a sentence-verification question for guaranteeing patients' concentration to the task. The results based on 15 patients showed that significant activation is present in the right frontal lobe of the cerebral cortex for both kinds of ambiguity. It means that right hemisphere participates in the resolution of ambiguity, and there are no regions specified for lexical ambiguity or idiomatic ambiguity alone.

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.63-73
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• 2009

The present study was conducted to explore how the processing of lexical ambiguity between homonymy and polysemy differs from each other, and whether the representation of mental lexicon was separated from each lexical ambiguity by a semantic priming paradigm. Homonymy (M1 means the literal meaning of '사과', i.e. apple and M2 means another literal meaning of '사과', i.e. apologize) was used in Experiment I, and polysemy (M2 means the literal meaning of '바람', i.e. wind and M2 means the figurative meaning of '바람', i.e. wanton) was used in Experiment 2. The results of both experiments showed that a significant semantic priming effect occurs regardless of the type of ambiguities (homonymy and polysemy) and the difference of their semantic processes. However, the semantic priming effect for polysemy was larger than that for homonymy. This result supports the hypothesis that the semantic process of homonymy is different from that of polysemy.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.1-24
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• 1991

This paper presents the Lexical Ambiguity Resolution System of Korean Language. This system uses Dependency grammar and Collative Semantics. Dependency grammar is used to analyze Korean syntactic dependency. A robust way to analyze a sentence is to establish links between individual words. Collative Semantics investigates the interplay between lexical ambiguity and semantics relations. Collative Semantics consists of sense-frame, semantic vector, collation, and screening. Our system was implemented by C programming language. This system analyzes sentences, discriminates the kinds of semantic relation between pairs of words senses in those sentences, and resolves lexical ambiguity.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.4
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• pp.783-787
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• 2012

It is not easy to find out syntactic error in a spelling checker systems of Korean, because the spelling checker is generally to correct each phrase and it cannot check the errors of contextual ill-matched words. Spelling checker system tests errors based on a words. Disambiguation of lexical ambiguities is important in natural language processing. Its outputs is used in syntactic analysis. For accurate analysis of a sentence, syntactic analysis system must find out the ambiguity of morphemes in a word. In this paper, we suggest several rules to resolve the ambiguities of morphemes in a word. Using these methods, we can reduce many lexical ambiguities in Korean.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.178-178
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• 1999

Most stochastic taggers can not resolve some morphological ambiguities that can be resolved only by referring to lexical contexts because they use only contextual probabilities based ontag n-grams and lexical probabilities. Existing lexical rules are effective for resolving such ambiguitiesbecause they can refer to lexical contexts. However, they have two limitations. One is that humanexperts tend to make erroneous rules because they are deterministic rules. Another is that it is hardand time-consuming to acquire rules because they should be manually acquired. In this paper, wepropose context-dependent lexical rules, which are lexical rules based on the statistics of a taggedcorpus, and an ambiguity-driven teaming method, which is the method of automatically acquiring theproposed rules from a tagged corpus. By using the proposed rules, the proposed tagger can partiallyannotate an unseen corpus with high accuracy because it is a kind of memorizing tagger that canannotate a training corpus with 100% accuracy. So, the proposed tagger is useful to improve theaccuracy of a stochastic tagger. And also, it is effectively used for detecting and correcting taggingerrors in a manually tagged corpus. Moreover, the experimental results show that the proposed methodis also effective for English part-of-speech tagging.

• The Journal of Bigdata
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• v.5 no.1
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• pp.197-208
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• 2020

Lexical ambiguity means that a word can be interpreted as two or more meanings, such as homonym and polysemy, and there are many cases of word sense ambiguation in words expressing emotions. In terms of projecting human psychology, these words convey specific and rich contexts, resulting in lexical ambiguity. In this study, we propose an emotional classification model that disambiguate word sense using bidirectional LSTM. It is based on the assumption that if the information of the surrounding context is fully reflected, the problem of lexical ambiguity can be solved and the emotions that the sentence wants to express can be expressed as one. Bidirectional LSTM is an algorithm that is frequently used in the field of natural language processing research requiring contextual information and is also intended to be used in this study to learn context. GloVe embedding is used as the embedding layer of this research model, and the performance of this model was verified compared to the model applied with LSTM and RNN algorithms. Such a framework could contribute to various fields, including marketing, which could connect the emotions of SNS users to their desire for consumption.

• Journal of Korean language education
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• v.29 no.3
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• pp.25-53
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• 2018

This study examined the influence of lexical factors on verbal Eojeol recognition. To meet the goal, forty-five L2 Korean learners and twenty-two Korean native speakers took Eojeol decision tasks measured with the lexical factors such as 'number of strokes', 'number of consonants and vowels', 'number of syllables', 'number of morphemes', 'whole Eojeol frequency', 'root frequency', 'first-syllable-sharing frequency', and 'number of dictionary meanings.' As a result, 'whole Eojeol frequency' was the most effective factor to predict Eojeol recognition reaction time for native speakers and L2 learners, which supports the full-list model. Other lexical factors influencing Eojeol recognition reaction time in L2 learners were different following their proficiency level.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.358-380
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• 2016

In order to considerably reduce the ambiguity rate, we propose in this article a disambiguation approach that is based on the selection of the right diacritics at different analysis levels. This hybrid approach combines a linguistic approach with a multi-criteria decision one and could be considered as an alternative choice to solve the morpho-lexical ambiguity problem regardless of the diacritics rate of the processed text. As to its evaluation, we tried the disambiguation on the online Alkhalil morphological analyzer (the proposed approach can be used on any morphological analyzer of the Arabic language) and obtained encouraging results with an F-measure of more than 80%.

• Proceedings of the KSPS conference
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• 2007.05a
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• pp.59-63
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• 2007

Neuromagnetic fields were recorded from normal 10 subjects to investigate the time course of cerebral neural activation during the resolution of lexical ambiguity. All recordings were made using a whole-head 306-channel MEG (Elekta Neuromag TM Inc., $Vectorview^{TM}$). The observed activity was described by sLORETA (standardized low resolution brain electromagnetic tomography) techniques implemented in CURRY software (Neuroscan). In the results, bilaterally occipito-temporal lobe was activated at 170ms. At 250ms was associated with bilateral temporal lobe during ambiguous condition, whereas in left parietal, temporal lobe on unambiguous condition. The left frontal lobe, temporal lobe were activated at 350ms for all condition. At approximately 430ms, was activated in right frontal, temporal lobe on the resolving ambiguous condition, in left parietal lobe, right temporal lobe on the preserving ambiguous condition. In conclusion, the cerebral activations related to the resolving lexical ambiguity were right frontal lobe and the areas of mountainous ambiguity were left parietal lobe.