Influence of Context on the Activation and Selection of Ambiguous Word Senses

1983 ◽  
Vol 35 (1) ◽  
pp. 51-64 ◽  
Author(s):  
Gregg C. Oden ◽  
James L. Spira
Keyword(s):  
Language Processing ◽  
Accurate Determination ◽  
Processing System ◽  
Ambiguous Word ◽  
Word Sense ◽  
Semantic Constraints ◽  
Ambiguous Words ◽  
Word Senses ◽  
Selection Of

The activation of ambiguous word senses was investigated by measuring the amount of interference in naming the ink color of a word that was either related or unrelated to one of the meanings of a preceding ambiguous word. In agreement with previous results obtained using this procedure (Conrad, 1974), evidence was obtained that both meanings of the ambiguous words are activated even in the presence of biasing context. However, contrary to previous findings, the degree of activation of each word sense depended on its degree of compatibility with the context. These results are consistent with a language processing system in which all interpretations of an ambiguity are accessed and then processed until an accurate determination has been made of which interpretation best satisfies the syntactic and semantic constraints that govern it.

Word Sense Disambiguation for Improving the Quality of Machine Translation

2014 ◽  
Vol 981 ◽  
pp. 153-156
Author(s):  
Chun Xiang Zhang ◽  
Long Deng ◽  
Xue Yao Gao ◽  
Li Li Guo
Keyword(s):  
Machine Translation ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Translation System ◽  
Word Sense ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Machine Translation System

Word sense disambiguation is key to many application problems in natural language processing. In this paper, a specific classifier of word sense disambiguation is introduced into machine translation system in order to improve the quality of the output translation. Firstly, translation of ambiguous word is deleted from machine translation of Chinese sentence. Secondly, ambiguous word is disambiguated and the classification labels are translations of ambiguous word. Thirdly, these two translations are combined. 50 Chinese sentences including ambiguous words are collected for test experiments. Experimental results show that the translation quality is improved after the proposed method is applied.

scholarly journals Global Word Sense Disambiguation of Polysemous Words in Telugu Language

2020 ◽  
Vol 10 (1) ◽  
pp. 420-425
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Text Summarization ◽  
Word Sense ◽  
Significant Issue ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Sequencing Technique ◽  
Polysemous Words ◽  
Word Senses

Word Sense Disambiguation (WSD) is a significant issue in Natural Language Processing (NLP). WSD refers to the capacity of recognizing the correct sense of a word in a given context. It can improve numerous NLP applications such as machine translation, text summarization, information retrieval, or sentiment analysis. This paper proposes an approach named ShotgunWSD. Shotgun WSD is an unsupervised and knowledgebased algorithm for global word sense disambiguation. The algorithm is motivated by the Shotgun sequencing technique. Shotgun WSD is proposed to disambiguate the word senses of Telugu document with three functional phases. The Shotgun WSD achieves the better performance than other approaches of WSD in the disambiguating sense of ambiguous words in Telugu documents. The dataset is used in the Indo-WordNet.

Semantic ambiguity norms for 530 Spanish words

2016 ◽  
Vol 38 (2) ◽  
pp. 457-475 ◽  
Author(s):  
JUAN HARO ◽  
PILAR FERRÉ ◽  
ROGER BOADA ◽  
JOSEP DEMESTRE
Keyword(s):  
Language Processing ◽  
Ambiguous Word ◽  
Age Of Acquisition ◽  
Emotional Valence ◽  
Objective Measures ◽  
Semantic Ambiguity ◽  
Subjective Ratings ◽  
Subjective Measures ◽  
Word Meanings ◽  
Ambiguous Words

ABSTRACTThis study presents semantic ambiguity norms for 530 Spanish words. Two subjective measures of semantic ambiguity and two subjective measures of relatedness of ambiguous word meanings were collected. In addition, two objective measures of semantic ambiguity were included. Furthermore, subjective ratings were obtained for some relevant lexicosemantic variables, such as concreteness, familiarity, emotional valence, arousal, and age of acquisition. In sum, the database overcomes some of the limitations of the published databases of Spanish ambiguous words; in particular, the scarcity of measures of ambiguity, the lack of relatedness of ambiguous word meanings measures, and the absence of a set of unambiguous words. Thus, it will be very helpful for researchers interested in exploring semantic ambiguity as well as for those using semantic ambiguous words to study language processing in clinical populations.

Ontology Matching using BabelNet Dictionary and Word Sense Disambiguation Algorithms

2017 ◽  
Vol 5 (1) ◽  
pp. 196 ◽  
Author(s):  
Mohamed Biniz ◽  
Rachid El Ayachi ◽  
Mohamed Fakir
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Word Sense Disambiguation ◽  
Similarity Measures ◽  
Ontology Matching ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Lesk Algorithm ◽  
Reference Ontology ◽  
Selection Of

<p>Ontology matching is a discipline that means two things: first, the process of discovering correspondences between two different ontologies, and second is the result of this process, that is to say the expression of correspondences. This discipline is a crucial task to solve problems merging and evolving of heterogeneous ontologies in applications of the Semantic Web. This domain imposes several challenges, among them, the selection of appropriate similarity measures to discover the correspondences. In this article, we are interested to study algorithms that calculate the semantic similarity by using Adapted Lesk algorithm, Wu &amp; Palmer Algorithm, Resnik Algorithm, Leacock and Chodorow Algorithm, and similarity flooding between two ontologies and BabelNet as reference ontology, we implement them, and compared experimentally. Overall, the most effective methods are Wu &amp; Palmer and Adapted Lesk, which is widely used for Word Sense Disambiguation (WSD) in the field of Automatic Natural Language Processing (NLP).</p>

scholarly journals A Novel Approach to Word Sense Disambiguation Based on Topical and Semantic Association

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xin Wang ◽  
Wanli Zuo ◽  
Ying Wang
Keyword(s):  
Language Processing ◽  
Fundamental Problem ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Features ◽  
Word Sense ◽  
Semantic Association ◽  
Data Set ◽  
Novel Approach ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is a fundamental problem in nature language processing, the objective of which is to identify the most proper sense for an ambiguous word in a given context. Although WSD has been researched over the years, the performance of existing algorithms in terms of accuracy and recall is still unsatisfactory. In this paper, we propose a novel approach to word sense disambiguation based on topical and semantic association. For a given document, supposing that its topic category is accurately discriminated, the correct sense of the ambiguous term is identified through the corresponding topic and semantic contexts. We firstly extract topic discriminative terms from document and construct topical graph based on topic span intervals to implement topic identification. We then exploit syntactic features, topic span features, and semantic features to disambiguate nouns and verbs in the context of ambiguous word. Finally, we conduct experiments on the standard data set SemCor to evaluate the performance of the proposed method, and the results indicate that our approach achieves relatively better performance than existing approaches.

scholarly journals Capsule Network Improved Multi-Head Attention for Word Sense Disambiguation

2021 ◽  
Vol 11 (6) ◽  
pp. 2488
Author(s):  
Jinfeng Cheng ◽  
Weiqin Tong ◽  
Weian Yan
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Semantic Representation ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Great Contribution ◽  
Specific Context ◽  
The Core ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is one of the core problems in natural language processing (NLP), which is to map an ambiguous word to its correct meaning in a specific context. There has been a lively interest in incorporating sense definition (gloss) into neural networks in recent studies, which makes great contribution to improving the performance of WSD. However, disambiguating polysemes of rare senses is still hard. In this paper, while taking gloss into consideration, we further improve the performance of the WSD system from the perspective of semantic representation. We encode the context and sense glosses of the target polysemy independently using encoders with the same structure. To obtain a better presentation in each encoder, we leverage the capsule network to capture different important information contained in multi-head attention. We finally choose the gloss representation closest to the context representation of the target word as its correct sense. We do experiments on English all-words WSD task. Experimental results show that our method achieves good performance, especially having an inspiring effect on disambiguating words of rare senses.

A Word Sense Disambiguation Method Based on Reconstruction of Context by Correlation

2011 ◽  
Vol 135-136 ◽  
pp. 160-166 ◽  
Author(s):  
Xin Hua Fan ◽  
Bing Jun Zhang ◽  
Dong Zhou
Keyword(s):  
Information Entropy ◽  
Average Distance ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Experimental Results ◽  
Occurrence Frequency ◽  
Word Sense ◽  
Occurrence Data ◽  
Ambiguous Words ◽  
Sense Disambiguation

This paper presents a word sense disambiguation method by reconstructing the context using the correlation between words. Firstly, we figure out the relevance between words though the statistical quantity(co-occurrence frequency , the average distance and the information entropy) from the corpus. Secondly, we see the words that have lager correlation value between ambiguous word than other words in the context as the important words, and use this kind of words to reconstruct the context, then we use the reconstructed context as the new context of the ambiguous words .In the end, we use the method of the sememe co-occurrence data[10] for word sense disambiguation. The experimental results have proved the feasibility of this method.

scholarly journals Neural Correlates of Semantic Competition during Processing of Ambiguous Words

2009 ◽  
Vol 21 (5) ◽  
pp. 960-975 ◽  
Author(s):  
Natalia Y. Bilenko ◽  
Christopher M. Grindrod ◽  
Emily B. Myers ◽  
Sheila E. Blumstein
Keyword(s):  
Inferior Frontal Gyrus ◽  
Ambiguous Word ◽  
Semantic Content ◽  
Neural Correlates ◽  
Auditory Modality ◽  
Ambiguous Words ◽  
Semantic Competition ◽  
Subordinate Meaning ◽  
Prime Target ◽  
Selection Of

The current study investigated the neural correlates that underlie the processing of ambiguous words and the potential effects of semantic competition on that processing. Participants performed speeded lexical decisions on semantically related and unrelated prime–target pairs presented in the auditory modality. The primes were either ambiguous words (e.g., ball) or unambiguous words (e.g., athlete), and targets were either semantically related to the dominant (i.e., most frequent) meaning of the ambiguous prime word (e.g., soccer) or to the subordinate (i.e., less frequent) meaning (e.g., dance). Results showed increased activation in the bilateral inferior frontal gyrus (IFG) for ambiguous-related compared to unambiguous-related stimulus pairs, demonstrating that prefrontal areas are activated even in an implicit task where participants are not required to explicitly analyze the semantic content of the stimuli and to make an overt selection of a particular meaning based on this analysis. Additionally, increased activation was found in the left IFG and the left cingulate gyrus for subordinate meaning compared to dominant meaning conditions, suggesting that additional resources are recruited in order to resolve increased competition demands in accessing the subordinate meaning of an ambiguous word.

scholarly journals Query Expansion using Semantic Network for Information Retrieval in Telugu Language

2019 ◽  
Vol 8 (2) ◽  
pp. 874-877
Keyword(s):  
Information Retrieval ◽  
Language Processing ◽  
Query Expansion ◽  
Retrieval System ◽  
Semantic Network ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Information Retrieval System ◽  
Word Sense ◽  
Sense Disambiguation

Now-a-days digital documents are playing a major role in all the areas /web, as such all the information is digitalised. Queries are used by the search engines to retrieve the information. Query plays a major role in information retrieval system, as a result relevant and non relevant documents are retrieved. Query expansion techniques will better the performance of the information retrieval system. Our proposed query expansion technique is Word Sense Disambiguation. This is to find the correct sense of the ambiguous word in regional Telugu language. In Query expansion, if the added query term is an ambiguous word, accuracy of relevant documents will be very less. So to avoid this, proposed method Word Sense Disambiguation (WSD) is used, which is related to NLP Natural Language Processing and Artificial Intelligence AI. WSD improves the accuracy of information retrieval system.

Research of Word Sense Disambiguation Based on Soft Pattern

2011 ◽  
Vol 460-461 ◽  
pp. 130-135
Author(s):  
Ke Liang Jia
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Difficult Problem ◽  
Maximum Matching ◽  
Word Sense ◽  
Word Context ◽  
Nature Language Processing ◽  
Sense Disambiguation ◽  
Matching Degree

Word sense disambiguation (WSD) is always an important and difficult problem that requires to be solved in Nature Language Processing. This paper presents a new WSD method which is based on soft pattern matching. The method can learn the soft patterns from the sense of the ambiguous word and its context, to construct a soft pattern - based database. At last the sense of the ambiguous word is labeled by choosing the sense with the maximum matching degree between the ambiguous word context and the soft pattern. The experiment result shows that the method has high precision.

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