scholarly journals A Knowledge Based Word Sense Disambiguation in Telugu Language

2020 ◽  
Vol 10 (1) ◽  
pp. 440-445
Keyword(s):  
Computational Linguistics ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Word Sense ◽  
Knowledge Based ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
The Senses ◽  
Definition Of ◽  
Polysemous Words

Telugu (తెలుగు) is one of the Dravidian languages which are morphologically rich. As within the other languages, it too consists of ambiguous words/phrases which have one-of-a-kind meanings in special contexts. Such words are referred as polysemous words i.e. words having a couple of experiences. A Knowledge based approach is proposed for disambiguating Telugu polysemous phrases using the computational linguistics tool, IndoWordNet. The task of WSD (Word sense disambiguation) requires finding out the similarity among the target phrase and the nearby phrase. In this approach, the similarity is calculated either by means of locating out the range of similar phrases (intersection) between the glosses (definition) of the target and nearby words or by way of finding out the exact occurrence of the nearby phrase's sense in the hierarchy (hypernyms/hyponyms) of the target phrase's senses. The above parameters are changed by using the intersection use of not simplest the glosses but also by using which include the related words. Additionally, it is a third parameter 'distance' which measures the distance among the target and nearby phrases. The proposed method makes use of greater parameters for calculating similarity. It scores the senses based on the general impact of parameters i.e. intersection, hierarchy and distance, after which chooses the sense with the best score. The correct meaning of Telugu polysemous phrase could be identified with this technique.

scholarly journals Arabic Gloss WSD Using BERT

2021 ◽  
Vol 11 (6) ◽  
pp. 2567
Author(s):  
Mohammed El-Razzaz ◽  
Mohamed Waleed Fakhr ◽  
Fahima A. Maghraby
Keyword(s):  
Target Word ◽  
Semantic Similarity ◽  
Test Data ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Written Text ◽  
Knowledge Based ◽  
Training Samples ◽  
Sense Disambiguation ◽  
Definition Of

Word Sense Disambiguation (WSD) aims to predict the correct sense of a word given its context. This problem is of extreme importance in Arabic, as written words can be highly ambiguous; 43% of diacritized words have multiple interpretations and the percentage increases to 72% for non-diacritized words. Nevertheless, most Arabic written text does not have diacritical marks. Gloss-based WSD methods measure the semantic similarity or the overlap between the context of a target word that needs to be disambiguated and the dictionary definition of that word (gloss of the word). Arabic gloss WSD suffers from a lack of context-gloss datasets. In this paper, we present an Arabic gloss-based WSD technique. We utilize the celebrated Bidirectional Encoder Representation from Transformers (BERT) to build two models that can efficiently perform Arabic WSD. These models can be trained with few training samples since they utilize BERT models that were pretrained on a large Arabic corpus. Our experimental results show that our models outperform two of the most recent gloss-based WSDs when we test them against the same test data used to evaluate our model. Additionally, our model achieves an F1-score of 89% compared to the best-reported F1-score of 85% for knowledge-based Arabic WSD. Another contribution of this paper is introducing a context-gloss benchmark that may help to overcome the lack of a standardized benchmark for Arabic gloss-based WSD.

A Semi-Supervised Graph-based Algorithm for Word Sense Disambiguation

2017 ◽  
Vol 8 (2) ◽  
pp. 13 ◽  
Author(s):  
Amita Jain ◽  
Devendra Kumar Tayal ◽  
Sonakshi Vij
Keyword(s):  
Comparative Study ◽  
Computational Linguistics ◽  
Word Sense Disambiguation ◽  
Considerable Improvement ◽  
Graph Connectivity ◽  
Word Sense ◽  
Text Corpus ◽  
Ambiguous Words ◽  
Sense Disambiguation

Word sense disambiguation is an issue of computational linguistics that aims at extracting the most appropriate sense of a word in a given context. Till date, several unsupervised graph-based methods have been devised for achieving word sense disambiguation but the majority of these methods use the notion of using multiple ambiguous words in a text corpus to create a WordNet® graph which enforces the concept of “blind leading the blind”. In this paper, a semi-supervised algorithm has been proposed and implemented that takes into consideration a clue-word for creating the desired WordNet® graph. The existing algorithms of word sense disambiguation consider all the graph connectivity measures to be equally significant but this is not the case. In this paper, a comparative study for all these graph connectivity measures is performed to discuss their connectivity aspects and priorities are assigned to them in order to generate an effective word sense disambiguation algorithm. The WordNet® graph is generated using python external libraries NetworkX and Matplotlib. The proposed algorithm’s results are tested using SemCor database and it shows considerable improvement over the unsupervised graph-based method suggested by Navigli.

scholarly journals Word Sense Disambiguation Using Prior Probability Estimation Based on the Korean WordNet

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2938
Author(s):  
Minho Kim ◽  
Hyuk-Chul Kwon
Keyword(s):  
Large Scale ◽  
Prior Probability ◽  
Word Sense Disambiguation ◽  
Probability Estimation ◽  
Word Sense ◽  
Occurrence Probability ◽  
Knowledge Based ◽  
Ambiguous Words ◽  
Lexical Disambiguation ◽  
Sense Disambiguation

Supervised disambiguation using a large amount of corpus data delivers better performance than other word sense disambiguation methods. However, it is not easy to construct large-scale, sense-tagged corpora since this requires high cost and time. On the other hand, implementing unsupervised disambiguation is relatively easy, although most of the efforts have not been satisfactory. A primary reason for the performance degradation of unsupervised disambiguation is that the semantic occurrence probability of ambiguous words is not available. Hence, a data deficiency problem occurs while determining the dependency between words. This paper proposes an unsupervised disambiguation method using a prior probability estimation based on the Korean WordNet. This performs better than supervised disambiguation. In the Korean WordNet, all the words have similar semantic characteristics to their related words. Thus, it is assumed that the dependency between words is the same as the dependency between their related words. This resolves the data deficiency problem by determining the dependency between words by calculating the χ2 statistic between related words. Moreover, in order to have the same effect as using the semantic occurrence probability as prior probability, which is used in supervised disambiguation, semantically related words of ambiguous vocabulary are obtained and utilized as prior probability data. An experiment was conducted with Korean, English, and Chinese to evaluate the performance of our proposed lexical disambiguation method. We found that our proposed method had better performance than supervised disambiguation methods even though our method is based on unsupervised disambiguation (using a knowledge-based approach).

Word sense disambiguation using implicit information

2019 ◽  
Vol 26 (4) ◽  
pp. 413-432 ◽  
Author(s):  
Goonjan Jain ◽  
D.K. Lobiyal
Keyword(s):  
Word Sense Disambiguation ◽  
Ambiguous Word ◽  
Word Sense ◽  
Implicit Information ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Novel Method ◽  
Unsupervised Approach ◽  
Polysemous Words ◽  
Better Than

AbstractHumans proficiently interpret the true sense of an ambiguous word by establishing association among words in a sentence. The complete sense of text is also based on implicit information, which is not explicitly mentioned. The absence of this implicit information is a significant problem for a computer program that attempts to determine the correct sense of ambiguous words. In this paper, we propose a novel method to uncover the implicit information that links the words of a sentence. We reveal this implicit information using a graph, which is then used to disambiguate the ambiguous word. The experiments show that the proposed algorithm interprets the correct sense for both homonyms and polysemous words. Our proposed algorithm has performed better than the approaches presented in the SemEval-2013 task for word sense disambiguation and has shown an accuracy of 79.6 percent, which is 2.5 percent better than the best unsupervised approach in SemEval-2007.

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.

DISTRIBUTIONAL ANALYSIS OF RELATED SYNSETS IN WordNet FOR A WORD SENSE DISAMBIGUATION TASK

2005 ◽  
Vol 14 (06) ◽  
pp. 919-934 ◽  
Author(s):  
KOSTAS FRAGOS ◽  
YANIS MAISTROS
Keyword(s):  
Target Word ◽  
Goodness Of Fit ◽  
Hypothesis Test ◽  
Word Sense Disambiguation ◽  
The Other ◽  
Reasonable Assumption ◽  
P Value ◽  
Word Sense ◽  
Chi Square ◽  
Sense Disambiguation

This work presents a new method for an unsupervised word sense disambiguation task using WordNet semantic relations. In this method we expand the context of a word being disambiguated with related synsets from the available WordNet relations and study within this set the distribution of the related synset that correspond to each sense of the target word. A single sample Pearson-Chi-Square goodness-of-fit hypothesis test is used to determine whether the null hypothesis of a composite normality PDF is a reasonable assumption for a set of related synsets corresponding to a sense. The calculated p-value from this test is a critical value for deciding the correct sense. The target word is assigned the sense, the related synsets of which are distributed more "abnormally" relative to the other sets of the other senses. Our algorithm is evaluated on English lexical sample data from the Senseval-2 word sense disambiguation competition. Three WordNet relations, antonymy, hyponymy and hypernymy give a distributional set of related synsets for the context that was proved quite a good word sense discriminator, achieving comparable results with the system obtained the better results among the other competing participants.

A HIGHLY ACCURATE BOOTSTRAPPING ALGORITHM FOR WORD SENSE DISAMBIGUATION

2001 ◽  
Vol 10 (01n02) ◽  
pp. 5-21 ◽  
Author(s):  
RADA F. MIHALCEA ◽  
DAN I. MOLDOVAN
Keyword(s):  
High Precision ◽  
Word Sense Disambiguation ◽  
Original Text ◽  
Word Sense ◽  
New Words ◽  
Input Text ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Very High

In this paper, we present a bootstrapping algorithm for Word Sense Disambiguation which succeeds in disambiguating a subset of the words in the input text with very high precision. It uses WordNet and a semantic tagged corpus, for the purpose of identifying the correct sense of the words in a given text. The bootstrapping process initializes a set of ambiguous words with all the nouns and verbs in the text. It then applies various disambiguation procedures and builds a set of disambiguated words: new words are sense tagged based on their relation to the already disambiguated words, and then added to the set. This process allows us to identify, in the original text, a set of words which can be disambiguated with high precision; 55% of the verbs and nouns are disambiguated with an accuracy of 92%.

Sign in / Sign up

Export Citation Format

Share Document