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A new word sense disambiguation system based on deduction

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Abstract

Word sense ambiguity resolution is one of the major issues in the process of machine translation. Statistical and example-based methods are usually applied for this purpose. In statistical methods, ambiguity resolution is mostly carried out by making use of some statistics extracted from previously translated documents or dual corpora of source and target languages. In this paper, we look at the problem from a different viewpoint. The proposed system consists of two main parts. The first part includes a data mining algorithm which runs offline and extracts some useful knowledge about the cooccurrences of the words. The second part of the system is an expert system whose knowledge base includes the set of association rules generated by the first part. For the inference engine of the expert system, we propose an efficient algorithm based on forward chaining in order to deduce the correct senses of the words. The performance of the system in terms of applicability and precision will be analyzed and discussed through a set of experiments.

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An Empirical Analysis of Word Sense Disambiguation through Machine Learning Approaches
International Journal of Scientific Research in Computer Science, Engineering and Information Technology IJSRCSEIT

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2022

The procedure to identify the appropriate meaning for the particular word in an ambiguous statement is considered as Word Sense Disambiguation. It is a complicated problem since it necessitates the utilization of information from a variety of sources. Since the start of machine learning, a significant amount of time and effort has been devoted to overcoming this challenge, and the work is currently ongoing. In WSD, a variety of methodologies were employed and executed on a variety of corpora representing practically all languages. WSD algorithms are grouped into three groups in this paper: Supervised algorithms, unsupervised algorithms and knowledge-based algorithms. Every subcategory will be examined thoroughly, with details elaborated for nearly all of the algorithms within each area. As a result, work samples for every technique were selected based on the language being used, the corpora being used, and other considerations. Each method's advantages and disadvantages were meticulously documented. Some of these strategies have limits in certain scenarios, and our work will assist scientists in the fields of machine learning in selecting the most appropriate algorithms to tackle their specific problem in WSD. When comparing the works that have been used and indeed the procedures that were employed, it is possible to notice the distinctiveness of the piece of work that was created. As a result of this research, it was observed that (i) size of the dataset has an considerable impact on algorithm's performance, (ii) some methodologies provide high performance accuracy for one language where as it gives low performance for some other, (iii) a few of these methodologies can be run quickly but with a limitation on accuracy, and (iv) the large number among those methodologies have been implemented successfully for a wide range of different languages.

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Supervised Word Sense Disambiguation
Tanya Sangoi

2016

Word Sense Disambiguation (WSD) is the method of the correct sense for word in a context. In this paper we have researched the various approaches for WSD: Knowledge based, Supervised, Semi-supervised, Unsupervised methods. This paper has further elaborated on the supervised methods used for WSD. The methods that are compared in this paper are: Decision Trees, Decision Lists, Support Vector Machines, Neural Networks, Naïve Bayes methods, Exemplar learning.

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A New Supervised Learning Algorithm for Word Sense Disambiguation
Ted Pedersen

1997

The Naive Mix is a new supervised learning algorithm that is based on a sequential method for selecting probabilistic models. The usual objective of model selection is to nd a single model that adequately characterizes the data in a training sample. However, during model selection a sequence of models is generated that consists of the best{ tting model at each level of model complexity. The Naive Mix utilizes this sequence of models to de ne a probabilistic model which is then used as a probabilistic classi er to perform word{sense disambiguation. The models in this sequence are restricted to the class of decomposable log{linear models. This class of models o ers a number of computational advantages. Experiments disambiguating twelve di erent words show that a Naive Mix formulated with a forward sequential search and Akaike's Information Criteria rivals established supervised learning algorithms such as decision trees (C4.5), rule induction (CN2) and nearest{neighbor classi cation (PEBLS).

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Mining Rules for Word Sense Disambiguation
Lucia Ana Maria

This paper describes the automatic generation and the evaluation of sets of rules for word sense disambiguation (WSD) in machine translation. The ultimate aim is to identify high-quality rules that can be used as knowledge sources in a relational WSD model. The evaluation was carried out both automatically, by means of four objective measures (error, coverage, support and novelty), and manually, by means of a subjective analysis of the level of interest of the best rules as pointed out by the objective measures. As a result, we selected 63 rules addressing seven highly ambiguous verbs. The evaluation also evidenced which kinds of knowledge were effectively used by the WSD rules, which are not always the same as those revealed by traditional evaluations of complete WSD models.

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Word Sense Disambiguation with andwithout Supervision Paolo Rosso, Francesco Masulli2, Davide Buscaldi3Dpto. de Sistemas Informéaticos y Computaciéon Univ. Politéecnica de Valencia, Spain
Francesco Masulli

Abstract Word Sense Disambiguation (WSD) is one of the most important open problems in Natural Language Processing. One of the most successful current lines of research in WSD is the corpus-based approach, in which machine learning algorithms are applied to learn statistical models or classifiers from corpora. When a machine learning approach learns from previously semantically annotated corpora it is said to be supervised, whereas when it does not use sense tagged data during training it is called unsupervised.

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An Attempt to Formalize Word Sense Disambiguation: Maximizing Efficiency by Minimizing Computational Costs
Pascual Cantos Gómez

Revista Espanola De Linguistica Aplicada, 2009

This paper presents an algorithm based on collocational data for word sense disambiguation (WSD). The aim of this algorithm is to maximize efficiency by minimizing (1) computational costs and (2) linguistic tagging/annotation. The formalization of our WSD algorithm is based on discriminant function analysis (DFA). This statistical technique allows us to parameterize each collocational item with its meaning, using just bare text. The parameterized data allow us to classify cases (sentences with an ambiguous word) into the values of a categorical dependent (each of the meanings of the ambiguous word). To evaluate the validity and efficiency of our WSD algorithm, we previously hand sense-tagged all the sentences containing ambiguous words and then cross-validated the hand sense-tagged data with the automatic WSD performance. Finally, we present the global results of our algorithm after applying it to a limited set of words in both languages: Spanish and English, highlighting the points...

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An Hybrid Approach to Word Sense Disambiguation with and Without Learned Knowledge
roshan karwa, International Journal on Natural Language Computing (IJNLC)

Word Sense Disambiguation is a classification of meaning of word in a precise context which is a tricky task to perform in Natural Language Processing which is used in application like machine translation, information extraction and retrieval, automatic or closed domain question answering system for the reason that of its semantics perceptive. Researchers tried for unsupervised and knowledge based learning approaches however such approaches have not proved more helpful. Various supervised learning algorithms have been made, but in vain as the attempt of creating the training corpus which is a tagged sense marked corpora is tricky. This paper presents a hybrid approach for resolving ambiguity in a sentence which is based on integrating lexical knowledge and world knowledge. English Wordnet developed at Princeton University, SemCor corpus and the JAWS library (Java API for WordNet searching) has been used for this purpose.

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An Insight into Word Sense Disambiguation Techniques
Harsimran Singh

International Journal of Computer Applications, 2015

This paper presents various techniques used in the area of Word Sense Disambiguation (WSD). There are a number of techniques such as: Knowledge based approaches, which use the knowledge encoded in Lexical resources; Supervised Machine Leaning methods in which the classifier is made to learn from previously semantically annotated corpus; Unsupervised approaches that form cluster occurrences of words. Then there are also semi supervised approaches which use semi annotated corpus as reference data along with unlabeled data.

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A Comparative Study on Word Sense Disambiguation Methods
Sabeeha K P

Word Sense Disambiguation is the process of determining which sense of a word is used in a given context. Word Sense Disambiguation is a classi cation problem. Given an instance of a word and the context in which it occurs , the aim is to determine the sense of that occurrence of the word . In this paper a comparison of three recent word sense disambiguation tech- niques is presented. The Word Sense Disambiguation methods surveyed are as fol- lows. Word Sense Disambiguation with automatically acquired knowledge, Word Sense Disambiguation using enriched semantic relation network and a Word Sense Disambiguation based on Lexical Chaining. These approaches are evaluated with standard corporas and their results are compared. These approaches are extensively used in Natural Language Processing

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A Review Of Literature On Word Sense Disambiguation
MAHESH KSHIRSAGAR

2014

lexical ambiguity is a fundamental characteristic of language. Words can have more than one distinct meaning. Word sense disambiguation is defined as the problem of computationally determining which”sense”of a word is correct in given context. Word sense disambiguation is a task of classification where word senses are the classes, the context provides the evidence, and each occurrence of a word is assigned to one or more of its possible classes based on the evidence. There are various approaches for word sense disambiguation like knowledge based method, supervised method, unsupervised method and semi supervised method. There are various application of word sense disambiguation like machine translation, information extraction, information retrieval Keywords— ambiguity, word sense, word sense

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Classifier Approach for Word Sense Disambiguation System with a Focus to Improve the Translation Accuracy
Archana Maurya

Machine Translation (MT) is a crucial application of (NLP) Natural language Processing. This MT technique automatic and based on computers. One of the most modern techniques adopted in MT is machine learning (ML). Over the past few years, ML has grown in popularity during MT process among researchers. Ambiguity is a major challenge in MT. Word Sense Disambiguation (WSD) is a common technique for solving the ambiguity problem. ML approaches are commonly used for the WSD techniques and are used for training and testing purposes. The outcome prediction of the test data gives encouraging results. Text classification is one of the most significant techniques for resolving the WSD. In this paper, we have analyzed some common supervised ML text classification algorithms and also proposed a “hybrid model” called “AmbiF.” We have compared the results of all analyzed algorithms with the proposed model “AmbiF. The analyzed supervised algorithms are Decision Tree, Bayesian Network, Support Vect...

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Word-Sense Disambiguation Using Statistical Methods
mona anizi

1991

We describe a statistical technique for assigning senses to words. An instance of a word is assigned a sense by asking a question about the context in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another language. When we incorporated this method of assigning senses into our statistical machine translation system, the error rate of the system decreased by thirteen percent.

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Approaches for Word Sense Disambiguation - A Survey
Pranjal P Borah

International Journal of Recent Technology and Engineering, 2014

Word sense disambiguation is a technique in the field of natural language processing where the main task is to find the correct sense in which a word occurs in a particular context. It is found to be of vital help to applications such as question answering, machine translation, text summarization, text classification, information retrieval etc. This has resulted in excessive interest in approaches based on machine learning which performs classification of word senses automatically. The main motivation behind word sense disambiguation is to allow the users to make ample use of the available technologies because ambiguities present in any language provide great difficulty in the use of information technology as words in human language that occur in a particular context can be interpreted in more than one way depending on the context. In this paper we put forward a survey of supervised, unsupervised and knowledge based approaches and algorithms available in word sense disambiguation (WSD). Index Terms-Machine readable dictionary, Machine translation, Natural language processing, Wordnet, Word sense disambiguation.

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Word sense disambiguation: A survey
Roberto Navigli

2009

Abstract Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered an AI-complete problem, that is, a task whose solution is at least as hard as the most difficult problems in artificial intelligence. We introduce the reader to the motivations for solving the ambiguity of words and provide a description of the task. We overview supervised, unsupervised, and knowledge-based approaches.

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A Statistical Method for Word - Sense Disambiguation
Rebecca Bruce

1995

In this dissertation, I apply statistical techniques to the formidable natural language processing task of word-sense disambiguation. In particular, I develop probabilistic classi ers | systems that perform disambiguation by assigning, out of a set of word meaning designations, the one that is most probable according to a probabilistic model. The model expresses the relationships among the classi cation variable (in this case, the variable representing the sense tag of the ambiguous word) and variables that correspond to properties of the ambiguous word and the

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