Retrieving with good sense

Although always present in text, word sense ambiguity only recently became regarded as a problem to information retrieval which was potentially solvable. The growth of interest in word senses resulted from new directions taken in disambiguation research. This paper first outlines this research and surveys the resulting efforts in information retrieval. Although the majority of attempts to improve retrieval effectiveness were unsuccessful, much was learnt from the research. Most notably a notion of under what circumstance disambiguation may prove of use to retrieval

Word sense disambiguation and information retrieval

It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval (IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will increase. However, recent research into the application of a word sense disambiguator to an IR system failed to show any performance increase. From these results it has become clear that more basic research is needed to investigate the relationship between sense ambiguity, disambiguation, and IR. Using a technique that introduces additional sense ambiguity into a collection, this paper presents research that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of accuracy

Word sense disambiguation and information retrieval

It has often been thought that word sense ambiguity is a cause of poor performance in Information Retrieval (IR) systems. The belief is that if ambiguous words can be correctly disambiguated, IR performance will increase. However, recent research into the application of a word sense disambiguator to an IR system failed to show any performance increase. From these results it has become clear that more basic research is needed to investigate the relationship between sense ambiguity, disambiguation, and IR. Using a technique that introduces additional sense ambiguity into a collection, this paper presents research that goes beyond previous work in this field to reveal the influence that ambiguity and disambiguation have on a probabilistic IR system. We conclude that word sense ambiguity is only problematic to an IR system when it is retrieving from very short queries. In addition we argue that if a word sense disambiguator is to be of any use to an IR system, the disambiguator must be able to resolve word senses to a high degree of accuracy

A Word Sense-Oriented User Interface for Interactive Multilingual Text Retrieval

In this paper we present an interface for supporting a user in an interactive cross-language search process using semantic classes. In order to enable users to access multilingual information, different problems have to be solved: disambiguating and translating the query words, as well as categorizing and presenting the results appropriately. Therefore, we first give a brief introduction to word sense disambiguation, cross-language text retrieval and document categorization and finally describe recent achievements of our research towards an interactive multilingual retrieval system. We focus especially on the problem of browsing and navigation of the different word senses in one source and possibly several target languages. In the last part of the paper, we discuss the developed user interface and its functionalities in more detail

An Information Retrieval Approach to Sense Ranking

In word sense disambiguation, choosing the most frequent sense for an ambiguous word is a powerful heuristic. However, its usefulness is restricted by the availability of sense-annotated data. In this paper, we propose an information retrieval-based method for sense ranking that does not require annotated data. The method queries an information retrieval engine to estimate the degree of association between a word and its sense descriptions. Experiments on the Senseval test materials yield state-ofthe-art performance. We also show that the estimated sense frequencies correlate reliably with native speakers ’ intuitions.

Incorporating Knowledge Base in Unsupervised Approach of Word Sense Disambiguation of Malay Documents

The problem of ambiguity in a text document or query is among the issues found in information retrieval. This problem occurs when a word has more than one meaning. The presence of ambiguity in a text or query will have a negative impact to the information retrieval process and the query expansion process. Addition of supplementary keywords in the query expansion process would be inaccurate without identifying the exact sense of the word. Ambiguous terms need to be disambiguated to avoid this problem. The process of identifying the proper sense is known as word sense disambiguation (WSD). The study of word sense disambiguation in text documents have been carried out by researchers worldwide. However, a study on this issue in the Malay language context is still insufficient. The proposed method is an adaptation of a famous unsupervised and knowledge-based method

Word sense discrimination in information retrieval: a spectral clustering-based approach

International audienceWord sense ambiguity has been identified as a cause of poor precision in information retrieval (IR) systems. Word sense disambiguation and discrimination methods have been defined to help systems choose which documents should be retrieved in relation to an ambiguous query. However, the only approaches that show a genuine benefit for word sense discrimination or disambiguation in IR are generally supervised ones. In this paper we propose a new unsupervised method that uses word sense discrimination in IR. The method we develop is based on spectral clustering and reorders an initially retrieved document list by boosting documents that are semantically similar to the target query. For several TREC ad hoc collections we show that our method is useful in the case of queries which contain ambiguous terms. We are interested in improving the level of precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30) respectively. We show that precision can be improved by 8% above current state-of-the-art baselines. We also focus on poor performing queries