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.

IMPROVING WORD SENSE DISAMBIGUATION WITH AUTOMATICALLY RETRIEVED SEMANTIC KNOWLEDGE

2008 ◽  
Vol 02 (03) ◽  
pp. 365-380 ◽  
Author(s):  
DMITRIY DLIGACH ◽  
MARTHA PALMER
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Knowledge ◽  
Word Sense ◽  
Novel Approach ◽  
Sense Disambiguation ◽  
Dependency Parser ◽  
Semantic Properties ◽  
Large Corpus

Word Sense Disambiguation (WSD) is an important problem in Natural Language Processing. Supervised WSD involves assigning a sense from some sense inventory to each occurrence of an ambiguous word. Verb sense distinctions often depend on the distinctions in the semantics of the target verb's arguments. Therefore, some method of capturing their semantics is crucial to the success of a VSD system. In this paper we propose a novel approach to encoding the semantics of the noun arguments of a verb. This approach involves extracting various semantic properties of that verb from a large text corpus. We contrast our approach with the traditional methods and show that it performs better while the only resources it requires are a large corpus and a dependency parser.

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.

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.

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.

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.

scholarly journals Supervised Word Sense Disambiguation with Recurrent Neural Network Model

2019 ◽  
Vol 9 (2) ◽  
pp. 1447-1453
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Natural Language ◽  
Language Processing ◽  
Word Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Single Word ◽  
Sense Disambiguation

Disambiguating words is a branch of artificial intelligence that deals with natural language processing. The dissatisfaction of the motive of the word deals with the polysemy of the ambiguous word, processing a single word in natural language, having two or more meanings where the corresponding context discriminates the meaning. Humans are intelligent enough to derive the meaning of the word because they are a biological neural network. Computers can be trained in such a way that they should function similarly to biological neural networks. There are four different suggested approaches to clutter as the knowledge-dependent approach and the machine learning based models which are further classified as supervised, semi-supervised and unpublished learning models. The purpose of this research is to improve better communication between computers and humans. The discussed model used a supervised learning approach with recurrent neural networks.

Word Sense Disambiguation Based on Center Window

2014 ◽  
Vol 981 ◽  
pp. 157-160
Author(s):  
Chun Xiang Zhang ◽  
Li Li Guo ◽  
Xue Yao Gao
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Research Problem ◽  
Word Sense ◽  
Automatic Summarization ◽  
Accuracy Rate ◽  
Sense Disambiguation ◽  
Semantic Comprehension ◽  
Important Research Problem

Word sense disambiguation is widely applied to information retrieval, semantic comprehension and automatic summarization. It is an important research problem in natural language processing. In this paper, the center window is determined from the target ambiguous word. The words in the center window are extracted as discriminative features. At the same time, a new method of word sense disambiguation is proposed and the disambiguation classifier is given. The classifier is optimized and tested on SemEval-2007 #Task5 corpus. Experimental results show that the accuracy rate of disambiguation arrives at 64.2%.

scholarly journals Graph Convolutional Network for Word Sense Disambiguation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Chun-Xiang Zhang ◽  
Rui Liu ◽  
Xue-Yao Gao ◽  
Bo Yu
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Semantic Category ◽  
Word Sense ◽  
Convolutional Network ◽  
Part Of Speech ◽  
Average Accuracy ◽  
Test Corpus ◽  
Sense Disambiguation

Word sense disambiguation (WSD) is an important research topic in natural language processing, which is widely applied to text classification, machine translation, and information retrieval. In order to improve disambiguation accuracy, this paper proposes a WSD method based on the graph convolutional network (GCN). Word, part of speech, and semantic category are extracted from contexts of the ambiguous word as discriminative features. Discriminative features and sentence containing the ambiguous word are used as nodes to construct the WSD graph. Word2Vec tool, Doc2Vec tool, pointwise mutual information (PMI), and TF-IDF are applied to compute embeddings of nodes and edge weights. GCN is used to fuse features of a node and its neighbors, and the softmax function is applied to determine the semantic category of the ambiguous word. Training corpus of SemEval-2007: Task #5 is adopted to optimize the proposed WSD classifier. Test corpus of SemEval-2007: Task #5 is used to test the performance of WSD classifier. Experimental results show that average accuracy of the proposed method is improved.

scholarly journals Word vs. Class-Based Word Sense Disambiguation

2015 ◽  
Vol 54 ◽  
pp. 83-122 ◽  
Author(s):  
Ruben Izquierdo ◽  
Armando Suarez ◽  
German Rigau
Keyword(s):  
Word Sense Disambiguation ◽  
Coarse Grained ◽  
Semantic Features ◽  
Word Sense ◽  
Simple Method ◽  
Word Meanings ◽  
Semantic Class ◽  
Semantic Classes ◽  
Sense Disambiguation ◽  
Word Senses

As empirically demonstrated by the Word Sense Disambiguation (WSD) tasks of the last SensEval/SemEval exercises, assigning the appropriate meaning to words in context has resisted all attempts to be successfully addressed. Many authors argue that one possible reason could be the use of inappropriate sets of word meanings. In particular, WordNet has been used as a de-facto standard repository of word meanings in most of these tasks. Thus, instead of using the word senses defined in WordNet, some approaches have derived semantic classes representing groups of word senses. However, the meanings represented by WordNet have been only used for WSD at a very fine-grained sense level or at a very coarse-grained semantic class level (also called SuperSenses). We suspect that an appropriate level of abstraction could be on between both levels. The contributions of this paper are manifold. First, we propose a simple method to automatically derive semantic classes at intermediate levels of abstraction covering all nominal and verbal WordNet meanings. Second, we empirically demonstrate that our automatically derived semantic classes outperform classical approaches based on word senses and more coarse-grained sense groupings. Third, we also demonstrate that our supervised WSD system benefits from using these new semantic classes as additional semantic features while reducing the amount of training examples. Finally, we also demonstrate the robustness of our supervised semantic class-based WSD system when tested on out of domain corpus.

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