Automatic Word Sense Disambiguation Using Cooccurrence and Hierarchical (original) (raw)

Automatic Word Sense Disambiguation Using Cooccurrence and Hierarchical Information

Lecture Notes in Computer Science, 2010

We review in detail here a polished version of the systems with which we participated in the Senseval-2 competition English tasks (all words and lexical sample). It is based on a combination of selectional preference measured over a large corpus and hierarchical information taken from WordNet, as well as some additional heuristics. We use that information to expand sense glosses of the senses in WordNet and compare the similarity between the contexts vectors and the word sense vectors in a way similar to that used by Yarowsky and Schuetze. A supervised extension of the system is also discussed. We provide new and previously unpublished evaluation over the SemCor collection, which is two orders of magnitude larger than SENSEVAL-2 collections as well as comparison with baselines. Our systems scored first among unsupervised systems in both tasks. We note that the method is very sensitive to the quality of the characterizations of word senses; glosses being much better than training examples.

SensPick: Sense Picking for Word Sense Disambiguation

2021 IEEE 15th International Conference on Semantic Computing (ICSC)

Word sense disambiguation (WSD) methods identify the most suitable meaning of a word with respect to the usage of that word in a specific context. Neural network-based WSD approaches rely on a sense-annotated corpus since they do not utilize lexical resources. In this study, we utilize both context and related gloss information of a target word to model the semantic relationship between the word and the set of glosses. We propose SensPick, a type of stacked bidirectional Long Short Term Memory (LSTM) network to perform the WSD task. The experimental evaluation demonstrates that SensPick outperforms traditional and state-of-the-art models on most of the benchmark datasets with a relative improvement of 3.5% in F-1 score. While the improvement is not significant, incorporating semantic relationships brings SensPick in the leading position compared to others.

Improved Word Sense Disambiguation with Enhanced Sense Representations

2021

Current state-of-the-art supervised word sense disambiguation (WSD) systems (such as GlossBERT and bi-encoder model) yield surprisingly good results by purely leveraging pretrained language models and short dictionary definitions (or glosses) of the different word senses. While concise and intuitive, the sense gloss is just one of many ways to provide information about word senses. In this paper, we focus on enhancing the sense representations via incorporating synonyms, example phrases or sentences showing usage of word senses, and sense gloss of hypernyms. We show that incorporating such additional information boosts the performance on WSD. With the proposed enhancements, our system achieves an F1 score of 82.0% on the standard benchmark test dataset of the English all-words WSD task, surpassing previous published scores on this benchmark dataset.

On Contribution of Sense Dependencies to Word Sense Disambiguation

Journal of Natural Language Processing, 2009

Traditionally, many researchers have addressed word sense disambiguation (WSD) as an independent classification problem for each word in a sentence. However, the problem with their approaches is that they disregard the interdependencies of word senses. Additionally, since they construct an individual sense classifier for each word, their method is limited in its applicability to the word senses for which training instances are served. In this paper, we propose a supervised WSD model based on the syntactic dependencies of word senses. In particular, we assume that strong dependencies between the sense of a syntactic head and those of its dependents exist. We describe these dependencies on the tree-structured conditional random fields (T-CRFs), and obtain the most appropriate assignment of senses optimized over the sentence. Furthermore, we incorporate these sense dependencies in combination with various coarse-grained sense tag sets, which are expected to relieve the data sparseness problem, and enable our model to work even for words that do not appear in the training data. In experiments, we display the appropriateness of considering the syntactic dependencies of senses, as well as the improvements by the use of coarse-grained tag sets. The performance of our model is shown to be comparable to those of state-ofthe-art WSD systems. We also present an in-depth analysis of the effectiveness of the sense dependency features by showing intuitive examples.

Similarity-based Word Sense Disambiguation

We describe a method for automatic word sense disambiguation using a text corpus and a machinereadable dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method performs well, and can learn even from very sparse training data.

SenseRelate::TargetWord - A Generalized Framework for Word Sense Disambiguation

2005

We have previously introduced a method of word sense disambiguation that computes the intended sense of a target word, using WordNet-based measures of semantic relatedness . SenseRelate::TargetWord is a Perl package that implements this algorithm. The disambiguation process is carried out by selecting that sense of the target word which is most related to the context words. Relatedness between word senses is measured using the WordNet::Similarity Perl modules.

Enhancing Modern Supervised Word Sense Disambiguation Models by Semantic Lexical Resources

2018

Supervised models for Word Sense Disambiguation (WSD) currently yield to state-of-the-art results in the most popular benchmarks. Despite the recent introduction of Word Embeddings and Recurrent Neural Networks to design powerful context-related features, the interest in improving WSD models using Semantic Lexical Resources (SLRs) is mostly restricted to knowledge-based approaches. In this paper, we enhance “modern” supervised WSD models exploiting two popular SLRs: WordNet and WordNet Domains. We propose an effective way to introduce semantic features into the classifiers, and we consider using the SLR structure to augment the training data. We study the effect of different types of semantic features, investigating their interaction with local contexts encoded by means of mixtures of Word Embeddings or Recurrent Neural Networks, and we extend the proposed model into a novel multi-layer architecture for WSD. A detailed experimental comparison in the recent Unified Evaluation Framewo...

Word sense disambiguation using WordNet relations

2003

In this paper, the "Weighted Overlapping" Disambiguation method is presented and evaluated. This method extends the Lesk's approach to disambiguate a specific word appearing in a context (usually a sentence). Sense's definitions of the specific word, "Synset" definitions, the "Hypernymy" relation, and definitions of the context features (words in the same sentence) are retrieved from the WordNet database and used as an input of our Disambiguation algorithm. More precisely, for each sense of the word a sense bag is formed using the WordNet definition and the definitions of all the "Hypernyms" associated with the nouns and verbs in the sense's definition. A similar technique is used, for all the context words and the definitions of the "Hypernyms" (associated with the context nouns and verbs), to form a context bag. Then, a technique of assigning weights to words is applied. The weight for every word is inversely proportional to the hierarchy depth in the WordNet taxonomy of the associated "synset". Eventually, the disambiguation of a word in a context is based on the calculation of the similarity between the words of the sense bags and the context bag. The proposed method is evaluated in disambiguating all the nouns for all the sentences in the Brown files.