scholarly journals All-words Word Sense Disambiguation for Russian Using Automatically Generated Text Collection

2020 ◽  
Vol 20 (4) ◽  
pp. 90-107
Author(s):  
Bolshina Angelina ◽  
Natalia Loukachevitch
Keyword(s):  
Word Sense Disambiguation ◽  
Automatic Generation ◽  
Word Sense ◽  
High Quality ◽  
Full Coverage ◽  
Sense Disambiguation ◽  
Polysemous Words

AbstractThe limited amount of the sense annotated data is a big challenge for the word sense disambiguation task. As a solution to this problem, we propose an algorithm of automatic generation and labelling of the training collections based on the monosemous relatives concept. In this article we explore the limits of this algorithm: we employ it to harvest training collections for all ambiguous nouns, verbs and adjectives presented in RuWordNet thesaurus and then evaluate the quality of the obtained collections. We demonstrate that our approach can create high-quality labelled collections with almost full-coverage of the RuWordNet polysemous words. Furthermore, we show that our method can be applied to the Word-in-Context task.

scholarly journals Word sense disambiguation to improve precision for ambiguous queries

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Adrian-Gabriel Chifu ◽  
Radu-Tudor Ionescu
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Word Sense ◽  
Interdisciplinary Approaches ◽  
Sense Disambiguation ◽  
Ranking Technique

AbstractSuccess in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

On the Quality of Lexical Resources for Word Sense Disambiguation

2004 ◽  
pp. 291-302
Author(s):  
Lluís Màrquez ◽  
Mariona Taulé ◽  
Lluís Padró ◽  
Luis Villarejo ◽  
Maria Antònia Martí
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Lexical Resources ◽  
Sense Disambiguation

scholarly journals Interactive medical word sense disambiguation through informed learning

2018 ◽  
Vol 25 (7) ◽  
pp. 800-808 ◽  
Author(s):  
Yue Wang ◽  
Kai Zheng ◽  
Hua Xu ◽  
Qiaozhu Mei
Keyword(s):  
Active Learning ◽  
Learning Curve ◽  
Domain Knowledge ◽  
Learning Algorithm ◽  
Interactive Learning ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
High Quality ◽  
Clinical Notes ◽  
Sense Disambiguation

Abstract Objective Medical word sense disambiguation (WSD) is challenging and often requires significant training with data labeled by domain experts. This work aims to develop an interactive learning algorithm that makes efficient use of expert’s domain knowledge in building high-quality medical WSD models with minimal human effort. Methods We developed an interactive learning algorithm with expert labeling instances and features. An expert can provide supervision in 3 ways: labeling instances, specifying indicative words of a sense, and highlighting supporting evidence in a labeled instance. The algorithm learns from these labels and iteratively selects the most informative instances to ask for future labels. Our evaluation used 3 WSD corpora: 198 ambiguous terms from Medical Subject Headings (MSH) as MEDLINE indexing terms, 74 ambiguous abbreviations in clinical notes from the University of Minnesota (UMN), and 24 ambiguous abbreviations in clinical notes from Vanderbilt University Hospital (VUH). For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy on the test set against the number of labeled instances was generated. The area under the learning curve was used as the primary evaluation metric. Results Our interactive learning algorithm significantly outperformed active learning, the previous fastest learning algorithm for medical WSD. Compared to active learning, it achieved 90% accuracy for the MSH corpus with 42% less labeling effort, 35% less labeling effort for the UMN corpus, and 16% less labeling effort for the VUH corpus. Conclusions High-quality WSD models can be efficiently trained with minimal supervision by inviting experts to label informative instances and provide domain knowledge through labeling/highlighting contextual features.

scholarly journals Techniques for Lexical Semantics in Hindi Language

2019 ◽  
Vol 8 (12) ◽  
pp. 4075-4080
Keyword(s):  
Language Processing ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Official Language ◽  
Indian Languages ◽  
Semantic Task ◽  
Sense Disambiguation ◽  
Polysemous Word ◽  
The Given ◽  
Polysemous Words

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is constrained by non-availability of the standard corpora. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the available sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favourable results are achieved

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.

CO-graph: A new graph-based technique for cross-lingual word sense disambiguation

2015 ◽  
Vol 21 (5) ◽  
pp. 743-772 ◽  
Author(s):  
ANDRES DUQUE ◽  
LOURDES ARAUJO ◽  
JUAN MARTINEZ-ROMO
Keyword(s):  
Word Sense Disambiguation ◽  
Automatic Generation ◽  
Word Sense ◽  
Bilingual Dictionary ◽  
Bilingual Dictionaries ◽  
Sense Disambiguation ◽  
A New Technique ◽  
Cross Lingual ◽  
Selection Of ◽  
And Task

AbstractIn this paper, we present a new method based on co-occurrence graphs for performing Cross-Lingual Word Sense Disambiguation (CLWSD). The proposed approach comprises the automatic generation of bilingual dictionaries, and a new technique for the construction of a co-occurrence graph used to select the most suitable translations from the dictionary. Different algorithms that combine both the dictionary and the co-occurrence graph are then used for performing this selection of the final translations: techniques based on sub-graphs (communities) containing clusters of words with related meanings, based on distances between nodes representing words, and based on the relative importance of each node in the whole graph. The initial output of the system is enhanced with translation probabilities, provided by a statistical bilingual dictionary. The system is evaluated using datasets from two competitions: task 3 of SemEval 2010, and task 10 of SemEval 2013. Results obtained by the different disambiguation techniques are analysed and compared to those obtained by the systems participating in the competitions. Our system offers the best results in comparison with other unsupervised systems in most of the experiments, and even overcomes supervised systems in some cases.

scholarly journals EViLBERT: Learning Task-Agnostic Multimodal Sense Embeddings

2020 ◽  
Author(s):  
Agostina Calabrese ◽  
Michele Bevilacqua ◽  
Roberto Navigli
Keyword(s):  
Word Sense Disambiguation ◽  
Learning Task ◽  
Concept Image ◽  
Word Sense ◽  
Alternative Technologies ◽  
Open Set ◽  
Sense Disambiguation ◽  
Image Pairs ◽  
Polysemous Words ◽  
Limited Coverage

The problem of grounding language in vision is increasingly attracting scholarly efforts. As of now, however, most of the approaches have been limited to word embeddings, which are not capable of handling polysemous words. This is mainly due to the limited coverage of the available semantically-annotated datasets, hence forcing research to rely on alternative technologies (i.e., image search engines). To address this issue, we introduce EViLBERT, an approach which is able to perform image classification over an open set of concepts, both concrete and non-concrete. Our approach is based on the recently introduced Vision-Language Pretraining (VLP) model, and builds upon a manually-annotated dataset of concept-image pairs. We use our technique to clean up the image-to-concept mapping that is provided within a multilingual knowledge base, resulting in over 258,000 images associated with 42,500 concepts. We show that our VLP-based model can be used to create multimodal sense embeddings starting from our automatically-created dataset. In turn, we also show that these multimodal embeddings improve the performance of a Word Sense Disambiguation architecture over a strong unimodal baseline. We release code, dataset and embeddings at http://babelpic.org.

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.

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.

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