A novel word sense disambiguation approach using WordNet knowledge graph

Natural Languages are inherently ambiguous. Word Sense Disambiguation is one such problem where one word has multiple meaning depending upon the context in which it appears in the text. It can be considered as an intermediate step for many NLP applications like Machine Translation, Summarization, Query Processing, etc. Developing a Word Sense Disambiguation (WSD) system for the Gujarati language can have significant applications in the education sector. There are different approaches available for WSD which includes Supervised, Unsupervised and knowledge-based approaches. The work done is English language is extensive for this problem but for other regional languages more research needs to be done. This paper presents various works, mentioning their proposed method, datasets used, limitations and performance for some Indian languages like Hindi, Malayalam, Bengali, etc. The paper also enlists some general observations about existing approaches for word sense disambiguation. In the last section, the paper proposes a method to resolve WSD problem for Gujarati Language using Genetic Algorithm.

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach W3SD (This paper is an extended version of our work [4] published in the 8th International Conference on Computational Collective Intelligence.) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using WordNet and Wiktionary features through the taxonomic information content from WordNet and the glosses from Wiktionary. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks.

Word Sense Disambiguation (WSD) is the process of resolving the meaning of a word unambiguously in a given natural language context. Within the scope of this thesis, it is the process of marking text with explicit sense labels. What constitutes a sense is a subject of great debate. An appealing perspective, aims to define senses in terms of their multilingual correspondences, an idea explored by several researchers, Dyvik (1998), Ide (1999), Resnik & Yarowsky (1999), and Chugur, Gonzalo & Verdejo (2002) but to date it has not been given any practical demonstration. This thesis is an empirical validation of these ideas of characterizing word meaning using cross-linguistic correspondences. The idea is that word meaning or word sense is quantifiable as much as it is uniquely translated in some language or set of languages. Consequently, we address the problem of WSD from a multilingual perspective; we expand the notion of context to encompass multilingual evidence. We devise a new approach to resolve word sense ambiguity in natural language, using a source of information that was never exploited on a large scale for WSD before. The core of the work presented builds on exploiting word correspondences across languages for sense distinction. In essence, it is a practical and functional implementation of a basic idea common to research interest in defining word meanings in cross-linguistic terms. We devise an algorithm, SALAAM for Sense Assignment Leveraging Alignment And Multilinguality, that empirically investigates the feasibility and the validity of utilizing translations for WSD. SALAAM is an unsupervised approach for word sense tagging of large amounts of text given a parallel corpus—texts in translation—and a sense inventory for one of the languages in the corpus. Using SALAAM, we obtain large amounts of sense annotated data in both languages of the parallel corpus, simultaneously. The quality of the tagging is rigorously evaluated for both languages of the corpora. The automatic unsupervised tagged data produced by SALAAM is further utilized to bootstrap a supervised learning WSD system, in essence, combining supervised and unsupervised approaches in an intelligent way to alleviate the resources acquisition bottleneck for supervised methods. Essentially, SALAAM is extended as an unsupervised approach for WSD within a learning framework; in many of the cases of the words disambiguated, SALAAM coupled with the machine learning system rivals the performance of a canonical supervised WSD system that relies on human tagged data for training. Realizing the fundamental role of similarity for SALAAM, we investigate different dimensions of semantic similarity as it applies to verbs since they are relatively more complex than nouns, which are the focus of the previous evaluations. We design a human judgment experiment to obtain human ratings on verbs' semantic similarity. The obtained human ratings are cast as a reference point for comparing different automated similarity measures that crucially rely on various sources of information. Finally, a cognitively salient model integrating human judgments in SALAAM is proposed as a means of improving its performance on sense disambiguation for verbs in particular and other word types in general.

Automatic processing of biomedical documents is made difficult by the fact that many of the terms they contain are ambiguous. Word Sense Disambiguation (WSD) systems attempt to resolve these ambiguities and identify the correct meaning. However, the published literature on WSD systems for biomedical documents report considerable differences in performance for different terms. The development of WSD systems is often expensive with respect to acquiring the necessary training data. It would therefore be useful to be able to predict in advance which terms WSD systems are likely to perform well or badly on.This paper explores various methods for estimating the performance of WSD systems on a wide range of ambiguous biomedical terms (including ambiguous words/phrases and abbreviations). The methods include both supervised and unsupervised approaches. The supervised approaches make use of information from labeled training data while the unsupervised ones rely on the UMLS Metathesaurus. The approaches are evaluated by comparing their predictions about how difficult disambiguation will be for ambiguous terms against the output of two WSD systems. We find the supervised methods are the best predictors of WSD difficulty, but are limited by their dependence on labeled training data. The unsupervised methods all perform well in some situations and can be applied more widely.

BackgroundThe volume of research published in the biomedical domain has increasingly lead to researchers focussing on specific areas of interest and connections between findings being missed. Literature based discovery (LBD) attempts to address this problem by searching for previously unnoticed connections between published information (also known as “hidden knowledge”). A common approach is to identify hidden knowledge via shared linking terms. However, biomedical documents are highly ambiguous which can lead LBD systems to over generate hidden knowledge by hypothesising connections through different meanings of linking terms. Word Sense Disambiguation (WSD) aims to resolve ambiguities in text by identifying the meaning of ambiguous terms. This study explores the effect of WSD accuracy on LBD performance.MethodsAn existing LBD system is employed and four approaches to WSD of biomedical documents integrated with it. The accuracy of each WSD approach is determined by comparing its output against a standard benchmark. Evaluation of the LBD output is carried out using timeslicing approach, where hidden knowledge is generated from articles published prior to a certain cutoff date and a gold standard extracted from publications after the cutoff date.ResultsWSD accuracy varies depending on the approach used. The connection between the performance of the LBD and WSD systems are analysed to reveal a correlation between WSD accuracy and LBD performance.ConclusionThis study reveals that LBD performance is sensitive to WSD accuracy. It is therefore concluded that WSD has the potential to improve the output of LBD systems by reducing the amount of spurious hidden knowledge that is generated. It is also suggested that further improvements in WSD accuracy have the potential to improve LBD accuracy.

The identification of the correct sense of a word is necessary for many tasks in automatic natural language processing like machine translation, information retrieval, speech and text processing. Automatic Word Sense Disambiguation (WSD) is difficult and accuracies with state-of-the art methods are substantially lower than in other areas of text understanding like part-of-speech tagging. One shortcoming of these methods is that they do not utilize substantial sources of background knowledge, such as semantic taxonomies and dictionaries, which are now available in electronic form (the methods largely use shallow syntactic features). Empirical results from the use of Inductive Logic Programming (ILP) have repeatedly shown the ability of ILP systems to use diverse sources of background knowledge. In this paper we investigate the use of ILP for WSD in two different ways: (a) as a stand-alone constructor of models for WSD; and (b) to build interesting features, which can then be used by standard model-builders such as SVM. In our experiments we examine a monolingual WSD task using the 32 English verbs contained in the SENSEVAL-3 benchmark data; and a bilingual WSD task using 7 highly ambiguous verbs in machine translation from English to Portuguese. Background knowledge available is from eight sources that provide a wide range of syntactic and semantic information. For both WSD tasks, experimental results show that ILP-constructed models and models built using ILP-generated features have higher accuracies than those obtained using a state-of-the art feature-based technique equipped with shallow syntactic features. This suggests that the use of ILP with diverse sources of background knowledge can provide one way for making substantial progress in the field of automatic WSD.

ns, alunos, tenhamos a liberdade para o desenvolvimento do nosso trabalho, o que to importante para o nosso crescimento e formao plena como pesquisadores

Natural language is most common way to communicate with each other but sometime we can't understand other languages to understand different languages machine translation is needed. Machine translation is the best application which helps to understand any other language in less cost and less time. In this some problems are faced by researchers: words which pronounce same but having different meaning, some words spelled different but having same meaning, in some cases combination of words may change the meaning. To resolve such kind of problems Word Sense Disambiguation is needed. Word Sense Disambiguation is used to understand the correct meaning of the word with respect to context in which that is used; Word Sense Disambiguation is the part of natural language processing. Word Net plays an important role in Word Sense Disambiguation. In this paper, we will discuss about the basic concept of Natural language processing, Machine Translation and Word sense disambiguation.

We present an unsupervised learning strategy for word sense disambiguation (WSD) that exploits multiple linguistic resources including a parallel corpus, a bilingual machine readable dictionary, and a thesaurus. The approach is based on Class Based Sense Definition Model (CBSDM) that generates the glosses and translations for a class of word senses. The model can be applied to resolve sense ambiguity for words in a parallel corpus. That sense tagging procedure, in effect, produces a semantic bilingual concordance, which can be used to train WSD systems for the two languages involved. Experimental results show that CBSDM trained on Longman Dictionary of Contemporary English, English-Chinese Edition (LDOCE E-C) and Longman Lexicon of Contemporary English (LLOCE) is very effectively in turning a Chinese-English parallel corpus into sense tagged data for development of WSD systems.

Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach (WSD-TIC) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using taxonomic information content. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks.

In this paper we study unsupervised word sense disambiguation (WSD) based on sense definition. We learn low-dimensional latent semantic vectors of concept definitions to construct a more robust sense similarity measure wmfvec. Experiments on four all-words WSD data sets show significant improvement over the baseline WSD systems and LDA based similarity measures, achieving results comparable to state of the art WSD systems.

Word Sense Disambiguation (WSD) is the task of choosing the right sense of a polysemous word given a context. It is obviously essential for many natural language processing applications such as human-computer communication, machine translation, and information retrieval. In recent years, much attention have been paid to improve the performance of WSD systems by using combination of classifiers. In (Kittler, Hatef, Duin, and Matas 1998), six combination rules including product, sum, max, min, median, and majority voting were derived with a number of strong assumptions, that are unrealistic in many situations and especially in text-related applications. This paper considers a framework of combination strategies based on different representations of context in WSD resulting in these combination rules as well, but without the unrealistic assumptions mentioned above. The experiment was done on four words interest, line, hard, serve; on the DSO dataset it showed high accuracies with median and min combination rules.

Word-sense disambiguation is an open challenge in natural language processing. It is the process of identifying the actual meaning of the word based on the senses of the surrounding words of the context in which it is used. Knowledge-based approaches are becoming most popular than other approaches for word-sense disambiguation. Knowledge-based approaches do not require large volumes of training data instead uses Lexical knowledge bases to construct undirected graphs. In this paper, traditional Page Rank algorithms and random walk approaches are compared extensively.

Word Sense Disambiguation is a long-standing task in Natural Language Processing, lying at the core of human language understanding. However, the evaluation of automatic systems has been problematic, mainly due to the lack of a reliable evaluation framework. In this paper we develop a unified evaluation framework and analyze the performance of various Word Sense Disambiguation systems in a fair setup. The results show that supervised systems clearly outperform knowledge-based models. Among the supervised systems, a linear classifier trained on conventional local features still proves to be a hard baseline to beat. Nonetheless, recent approaches exploiting neural networks on unlabeled corpora achieve promising results, surpassing this hard baseline in most test sets.

Automatic word sense disambiguation (WSD) from text is a task of great importance in various applications of natural language processing, for example, in machine translation, question answering, automatic summarization or sentiment analysis. There are different approaches to finding the meaning of a word within a context, whether using supervised, unsupervised, semi-supervised or knowledge-based methods. Several studies have been conducted to automatically translate from text to sign language, reproducing the result of the translation with a signing avatar, in a way that deaf users have access to informative contents that otherwise are highly inaccessible, because sign language is their mother tongue. The many proposals that have been made look forward to minimize these informative and communicative barriers. Sign languages, however, do not have as many words as the spoken languages, so an automatic translation must be as accurate and free of ambiguities as possible. In this paper, we propose to evaluate the use of public access big data resources, as well as appropriate techniques to access this type of resources for WSD tasks, illustrating their effects in a translation system from text in Spanish to Costa Rican Sign Language (LESCO). The architecture of the actual system incorporates the use of a folksonomy, from which the disambiguation process will benefit. When an exact word is not found for a given detected sense in the source text, the ontology will be fed back with a new relationship of hyperonymy, to alert the curator on the need to propose a new sign in that category, thus promoting an enrichment in a key component of the architecture. As a result of the evaluation, the most appropriate big data public resources and techniques for WSD for sign language will be elucidated.