An Application of Word Sense Disambiguation to Information Retrieval

The problems of word sense disambiguation and document indexing for information retrieval have been extensively studied. It has been observed that indexing using disambiguated meanings, rather than word stems, should improve information retrieval results. We present a new corpus-based algorithm for performing word sense disambiguation. The algorithm does not need to train on many senses of each word; it uses instead the probability that certain concepts will occur together. That algorithm is then used to index several corpa of documents. Our indexing algorithm does not generally outperform the traditional stem-based tf.idf model

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.

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

HKUST at SemEval-2023 Task 1: Visual Word Sense Disambiguation with Context Augmentation and Visual Assistance

Visual Word Sense Disambiguation (VWSD) is a multi-modal task that aims to select, among a batch of candidate images, the one that best entails the target word's meaning within a limited context. In this paper, we propose a multi-modal retrieval framework that maximally leverages pretrained Vision-Language models, as well as open knowledge bases and datasets. Our system consists of the following key components: (1) Gloss matching: a pretrained bi-encoder model is used to match contexts with proper senses of the target words; (2) Prompting: matched glosses and other textual information, such as synonyms, are incorporated using a prompting template; (3) Image retrieval: semantically matching images are retrieved from large open datasets using prompts as queries; (4) Modality fusion: contextual information from different modalities are fused and used for prediction. Although our system does not produce the most competitive results at SemEval-2023 Task 1, we are still able to beat nearly half of the teams. More importantly, our experiments reveal acute insights for the field of Word Sense Disambiguation (WSD) and multi-modal learning. Our code is available on GitHub

Learning word-to-concept mappings for automatic text classification

For both classification and retrieval of natural language text documents, the standard document representation is a term vector where a term is simply a morphological normal form of the corresponding word. A potentially better approach would be to map every word onto a concept, the proper word sense and use this additional information in the learning process. In this paper we address the problem of automatically classifying natural language text documents. We investigate the effect of word to concept mappings and word sense disambiguation techniques on improving classification accuracy. We use the WordNet thesaurus as a background knowledge base and propose a generative language model approach to document classification. We show experimental results comparing the performance of our model with Naive Bayes and SVM classifiers

Biomedical word sense disambiguation with word embeddings

There is a growing need for automatic extraction of information and knowledge from the increasing amount of biomedical and clinical data produced, namely in textual form. Natural language processing comes in this direction, helping in tasks such as information extraction and information retrieval. Word sense disambiguation is an important part of this process, being responsible for assigning the proper concept to an ambiguous term. In this paper, we present results from machine learning and knowledge-based algorithms applied to biomedical word sense disambiguation. For the supervised machine learning algorithms we used word embeddings, calculated from the full MEDLINE literature database, as global features and compare the results to the use of local unigram and bigram features. For the knowledge-based method we represented the textual definitions of biomedical concepts from the UMLS database as word embedding vectors, and combined this with concept associations derived from the MeSH term co-occurrences. Both the machine learning and the knowledge-based results indicate that word embeddings are informative and improve the biomedical word disambiguation accuracy. Applied to the reference MSH WSD data set, our knowledge-based approach achieves 85.1% disambiguation accuracy, which is higher than some previously proposed approaches that do not use machine-learning strategies.publishe