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An Unsupervised Word Sense Disambiguation System for Under-Resourced Languages
[article]
2018
arXiv
pre-print
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In this paper, we present Watasense, an unsupervised system for word sense disambiguation. ...
Watasense has two modes of operation. The sparse mode uses the traditional vector space model to estimate the most similar word sense corresponding to its context. ...
User Interface
Word Sense Disambiguation We use two different unsupervised approaches for word sense disambiguation. ...
arXiv:1804.10686v1
fatcat:rb4hftuf6nck3oj2ymbfcc7ndm
An Insight into Word Sense Disambiguation Techniques
2015
International Journal of Computer Applications
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This paper presents various techniques used in the area of Word Sense Disambiguation (WSD). ...
previously semantically annotated corpus; Unsupervised approaches that form cluster occurrences of words. ...
For a given concept c, the conceptual density is given by: where denotes the mean number of hyponyms per node, m is the number of (marks) senses of words to disambiguate, is the height of sub hierarchy ...
doi:10.5120/20888-3666
fatcat:igoikbgvavfetfptwinwbz5z7y
The Uned systems at Senseval-2
[article]
2009
arXiv
pre-print
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This is slightly worse than the first sense heuristic for all words and 3.6% better for the lexical sample, a strong indication that unsupervised Word Sense Disambiguation remains being a strong challenge ...
We have participated in the SENSEVAL-2 English tasks (all words and lexical sample) with an unsupervised system based on mutual information measured over a large corpus (277 million words) and some additional ...
Introduction We advocate researching unsupervised techniques for Word Sense Disambiguation (WSD). ...
arXiv:0910.5410v1
fatcat:blgxqh6dk5e67kteop3kf2ms2u
Disambiguating Tags in Blogs
[chapter]
2009
Lecture Notes in Computer Science
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The dynamic nature of tag meanings makes current word sense disambiguation(WSD) methods not applicable. In this paper, we propose an unsupervised method for disambiguating tags in blogs. ...
Blog users enjoy tagging for better document organization, while ambiguity in tags leads to inaccuracy in tag-based applications, such as retrieval, visualization or trend discovery. ...
Conclusion In this paper, we present an unsupervised method for tag disambiguation, and showed its effectiveness on Chinese blog tags. Our method is based on context clustering. ...
doi:10.1007/978-3-642-04208-9_22
fatcat:pvmyurucbvbkfjtedtph2fls2y
Unsupervised Word Sense Disambiguation for Automatic Essay Scoring
[chapter]
2014
Smart Innovation, Systems and Technologies
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We incorporate an unsupervised word sense disambiguation (WSD) algorithm which measures similarity between sentences as a preprocessing step to our existing AES system. ...
Many also incorporate syntactic information about essays such as the number of spelling mistakes, number of words and so on. ...
This work derives direction and inspiration from the Chancellor of Amrita University, Sri Mata Amritanandamayi Devi. We thank Dr.M ...
doi:10.1007/978-3-319-07353-8_51
fatcat:aem22d5snfeefnxpydicovr7v4
Preliminary Results for Biomedical Word Sense Disambiguation Based on Semantic Clustering
2011
2011 22nd International Workshop on Database and Expert Systems Applications
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Knowledge-based methods compare the context of the ambiguous word to the information available in the terminological resource, but their main purpose is not only word sense disambiguation. ...
Our aim is to design scalable unsupervised WSD methods for the semantic annotation of large biomedical corpora. ...
In this paper, we introduce a kernel-based method that uses clustering for word sense disambiguation. ...
doi:10.1109/dexa.2011.66
dblp:conf/dexaw/Martin-WantonLJ11
fatcat:hsqeyuvzo5h5behjkzkpp2zof4
Determine Word Sense Based on Semantic and Syntax Information
2016
International Journal of Database Theory and Application
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A new approach of determining true meanings of ambiguous words based on support vector machine (SVM) is given. ...
Word sense disambiguation (WSD) plays an important role in natural language processing fields. Semantic category is semantic knowledge and part-of-speech is syntax knowledge. ...
Acknowledgement This work is supported by China Postdoctoral Science Foundation Funded Project(2014M560249) and Natural Science Foundation of Heilongjiang Province of China(F2015041). ...
doi:10.14257/ijdta.2016.9.2.03
fatcat:su5oapovuzdqxjnjgfukcaed2i
Numerical Simulation of Ambiguity Resolution in Multiple Information Streams Based on Network Machine Translation
2020
Complexity
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This paper proposes a method for disambiguation of word segmentation in professional fields based on unsupervised learning. ...
Word sense disambiguation is a key issue in the field of natural language processing. ...
P S j P Context S j , i � 1, 2, 3, . . . , m. (4) Word sense disambiguation based on Bayesian method is to judge the classification of word meaning based on the size of the posterior probability. ...
doi:10.1155/2020/7278085
fatcat:dhqlzzft4bchfkm5uca6mhepua
Development of an Approach for Disambiguating Ambiguous Hindi postposition
2010
International Journal of Computer Applications
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Word Sense Disambiguation (WSD) refers to the resolution of lexical semantic ambiguity and its goal is to attribute the correct senses to words in a given context. ...
In this survey paper, we have taken the problem as "Development of an approach for disambiguating ambiguous Hindi postposition". ...
APPROACHES-Word Sense
Disambiguation (WSD is the problem of
determining in which sense a word having a
number of distinct senses is used in a given
sentence. ...
doi:10.5120/939-1317
fatcat:vk5hl2okr5amhn3aizrng3rvym
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
2016
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
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Just as textual word sense disambiguation is useful for a wide range of NLP tasks, visual sense disambiguation can be useful for multimodal tasks such as image retrieval, image description, and text illustration ...
We propose an unsupervised algorithm based on Lesk which performs visual sense disambiguation using textual, visual, or multimodal embeddings. ...
Introduction Word sense disambiguation (WSD) is a widely studied task in natural language processing: given a word and its context, assign the correct sense of the word based on a pre-defined sense inventory ...
doi:10.18653/v1/n16-1022
dblp:conf/naacl/GellaLK16
fatcat:6ccdv2n7qfa5vbn7y2gqvcjwnq
Unsupervised Visual Sense Disambiguation for Verbs using Multimodal Embeddings
[article]
2016
arXiv
pre-print
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Just as textual word sense disambiguation is useful for a wide range of NLP tasks, visual sense disambiguation can be useful for multimodal tasks such as image retrieval, image description, and text illustration ...
We propose an unsupervised algorithm based on Lesk which performs visual sense disambiguation using textual, visual, or multimodal embeddings. ...
Introduction Word sense disambiguation (WSD) is a widely studied task in natural language processing: given a word and its context, assign the correct sense of the word based on a pre-defined sense inventory ...
arXiv:1603.09188v1
fatcat:nyjkir373jfepccrxyznlvz7xy
Distributional Semantics Approach To Thai Word Sense Disambiguation
2008
Zenodo
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This paper pays attention to the corpus-based strategy that employs an unsupervised learning method for disambiguation. ...
We report our investigation of Latent Semantic Indexing (LSI), an information retrieval technique and unsupervised learning, to the task of Thai noun and verbal word sense disambiguation. ...
The data are free running text and have large number of senses per word (twenty senses for หั ว /hua4/ and nine senses for เก็ บ /kep1/). ...
doi:10.5281/zenodo.1079186
fatcat:ifzvqcskljad3f25cxp55tq3oe
Addressing Cross-Lingual Word Sense Disambiguation on Low-Density Languages: Application to Persian
[article]
2018
arXiv
pre-print
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We explore the use of unsupervised methods in Cross-Lingual Word Sense Disambiguation (CL-WSD) with the application of English to Persian. ...
Our proposed approach targets the languages with scarce resources (low-density) by exploiting word embedding and semantic similarity of the words in context. ...
Introduction Word Sense Disambiguation (WSD) is the task of automatically selecting the most related sense for a word occurring in a context. ...
arXiv:1711.06196v3
fatcat:tk4pnjv66raifdh7rfuyqbvw4e
Unsupervised Approach to Word Sense Disambiguation in Malayalam
2016
Procedia Technology - Elsevier
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Word Sense Disambiguation (WSD) is the task of identifying the correct sense of a word in a specific context when the word has multiple meaning. ...
Since the sense of a word depends on its context of use, disambiguation process requires the understanding of word knowledge. ...
Bhadran V K, Associate Director, Centre for Development of Advanced Computing, for his sincere directions imparted with the project. ...
doi:10.1016/j.protcy.2016.05.106
fatcat:xdmg3pgokzdnnnbymmnsl5g3c4
Preposition Sense Disambiguation and Representation
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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In this paper we match each preposition's left-and right context, and their interplay to the geometry of the word vectors to the left and right of the preposition. ...
Extracting these features from a large corpus and using them with machine learning models makes for an efficient preposition sense disambiguation (PSD) algorithm, which is comparable to and better than ...
Acknowledgments This work is supported by IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) -a research collaboration as part of the IBM AI Horizons Network. ...
doi:10.18653/v1/d18-1180
dblp:conf/emnlp/GongMBV18
fatcat:p7q4dmh6efaw5jvcesqs4juzr4
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