The Role of Temporal Expressions in Word Sense Disambiguation

Proceedings of the workshop on Human Language Technology - HLT '94, 1994

This paper presents and evaluates models created according to a schema that provides a description of the joint distribution of the values of sense tags and contextual features that is potentially applicable to a wide range of content words. The models are evaluated through a series of experiments, the results of which suggest that the schema is particularly well suited to nouns but that it is also applicable to words in other syntactic categories.

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.

2018

In natural language processing (NLP), word sense disambiguation (WSD) is an automatic process carried out by a machine to sense the appropriate meaning of a word in a particular context or in a discourse. Natural language is ambiguous, so that many words may be interpreted in multiple methods depending on the context wherein they occur. The computational identification of which means for words in context is known as word sense disambiguation (WSD). In this paper, we will discuss the ambiguity of the words in the languages and the essential measures to deal with the ambiguous words.

1997

In this position paper, we make several observations about the state of the art in automatic word sense disambiguation. Motivated by these observations, we offer several specific proposals to the community regarding improved evaluation criteria, common training and testing resources, and the definition of sense inventories.

Natural Language Engineering, 2002

Has system performance on Word Sense Disambiguation (WSD) reached a limit? Automatic systems don't perform nearly as well as humans on the task, and from the results of the SENSEVAL exercises, recent improvements in system performance appear negligible or even negative. Still, systems do perform much better than the baselines, so something is being done right. System evaluation is crucial to explain these results and to show the way forward. Indeed, the success of any project in WSD is tied to the evaluation methodology used, and especially to the formalization of the task that the systems perform. The evaluation of WSD has turned out to be as difficult as designing the systems in the first place.

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.

SSRN, 2022

In present era, Natural Language Processing (NLP) is critical for improving human-machine communication. It is a broad interest to process textual data and gathers valuable and exact information from these texts. NLP compiles the text and sends the data to a computer for further processing. The current state of NLP's mathematical model for proper understanding of word meaning is unclear, and the meaning of words in context is unclear, evoking multiple senses. The spread and improvement of Natural Language Processing applications are being hampered by ambiguity in interpreting the precise meaning of texts such as machine translation (MT), Human-Machine interfaces, and so on. The approach of discovering the correct interpretation of ambiguous word in a given sentence is accepted as Word Sense Disambiguation (WSD).WSD is recognized as being one of natural language processing's more challenging and unsolved problems. Many ambiguities in natural languages are apparent, and researchers are offering to solve the problem in a variety of languages to achieve good disambiguation. These ambiguities must be solved in order to make sense including its texts and advance NLP processing and applications. WSD has a number of NLP applications for which it could be a problem, such as Machine Translation (MT), Information Retrieval (IR), Dialogues, Speech Synthesis (SS), and Question Answering (QA). The effectiveness of many strategies directly applied to WSD, such as Dictionary and Knowledge-based, Supervised, Semi-Supervised and Unsupervised approach, is compared in this study.

Lecture Notes in Computer Science, 2018

The paper aims at the community of researchers and practitioners that work in the area of natural language processing but do not specialize in the word sense disambiguation (WSD). It contains a brief introduction into WSD and describes the classical approaches to solve the problem. The experimental part reports results of disambiguation that were achieved using a set of methods which are available on a widely acclaimed web site. The data used in the test have been tagged by a professional linguist. The senses were represented by the WordNet 3.1 synsets. The conducted experiment studies the basic and ensemble methods and the effects of sense unification.

GWC, 2016

In this paper we present an analysis of different semantic relations extracted from WordNet, Extended WordNet and Sem-Cor, with respect to their role in the task of knowledge-based word sense disambiguation. The experiments use the same algorithm and the same test sets, but different variants of the knowledge graph. The results show that different sets of relations have different impact on the results: positive or negative. The beneficial ones are discussed with respect to the combination of relations and with respect to the test set. The inclusion of inference has only a modest impact on accuracy, while the addition of syntactic relations produces stable improvement over the baselines.

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.

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

Journal of King Saud University - Computer and Information Sciences, 2021

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Journal of Information System and Technology Management, 2021

Background: Word Sense Disambiguation (WSD) is known to have a detrimental effect on the precision of information retrieval systems, where WSD is the ability to identify the meanings of words in context. There is a challenge in inference-correct-sensing on ambiguous words. Through many years of research, there have been various solutions to WSD that have been proposed; they have been divided into supervised and knowledge-based unsupervised. Objective: The first objective of this study was to explore the state-of-art of the WSD method with a hybrid method using ontology concepts. Then, with the findings, we may understand which tools are available to build WSD components. The second objective was to determine which method would be the best in giving good performance results of WSD, by analysing how the methods were used to answer specific WSD questions, their production, and how their performance was analysed. Methods: A review of the literature was conducted relating to the performa...

Computational Linguistics, 2001

Word sense disambiguation (WSD) is a computational linguistics task likely to benefit from the tradition of combining different knowledge sources in articial intelligence research. An important step in the exploration of this hypothesis is to determine which linguistic knowledge sources are most useful and whether their combination leads to improved results. We present a sense tagger which uses several knowledge sources. Tested accuracy exceeds 94% on our evaluation corpus. Our system attempts to disambiguate all content words in running text rather than limiting itself to treating a restricted vocabulary of words. It is argued that this approach is more likely to assist the creation of practical systems.

2002

Abstract This paper explores the role of domain information in word sense disambiguation. The underlying hypothesis is that domain labels, such as MEDICINE, ARCHITECTURE and SPORT, provide a useful way to establish semantic relations among word senses, which can be profitably used during the disambiguation process. Results obtained at the SENSEVAL-2 initiative confirm that for a significant subset of words domain information can be used to disambiguate with a very high level of precision.