An Optimized Lesk-Based Algorithm for Word Sense Disambiguation

Ayetiran, Eniafe Festus; Agbele, Kehinde K.

doi:10.1515/comp-2018-0015

Cited by 10 publications

(9 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: Topic Disambiguationmentioning

confidence: 81%

“…In agreement with Ayetiran and Agbele [3] that definitions of words best characterize them, we need to disambiguate each word in the class topics and expand their definitions. The algorithm used for the disambiguation is an adapted and modified version of an earlier published algorithm in [3]. The only difference between the algorithm described in original algorithm and the modified version used in this work is that the latent topics learned from each language subcorpus serve as the contextual information for each target word to be disambiguated in the topics.…”

Section: Topic Disambiguationmentioning

confidence: 81%

“…The evaluation and the results of word sense disambiguation on the official SemEval dataset 4 are fully described in [3][4][5]. The latest experimental results on coarse-grained all-words WSD of SemEval 2007 are as follows: precision -0.786, recall -0.780, and F1 -0.783.…”

Section: Discussionmentioning

confidence: 99%

“…The WSD helps to resolve the problem of ambiguity of concepts in the topics. An adaptation of existing knowledge-based WSD algorithms [3][4][5] is employed for the WSD task and uses BabelNet [6] as the knowledge resource. For the sake of clarity, in the rest of the paper we refer to the topics of the text categories as class topics or simply topics in contrast to the topics extracted from the corpus using LDA, which we henceforth refer to as latent topics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An index-based joint multilingual/cross-lingual text categorization using topic expansion via BabelNet

Ayetiran¹

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

The majority of the state-of-the-art text categorization algorithms are supervised and therefore require prior training. Besides the rigor involved in developing training datasets and the requirement for repetition of training for different texts, working with multilingual texts poses additional unique challenges. One of these challenges is that the developer is required to have many different languages involved. Term expansion such as query expansion has been applied in numerous applications; however, a major drawback of most of these applications is that the actual meaning of terms is not usually taken into consideration. Considering the semantics of terms is necessary because of the polysemous nature of most natural language words. In this paper, as a specific contribution to the document index approach for text categorization, we present a joint multilingual/cross-lingual text categorization algorithm (JointMC) based on semantic term expansion of class topic terms through an optimized knowledge-based word sense disambiguation. The lexical knowledge in BabelNet is used for the word sense disambiguation and expansion of the topics' terms. The categorization algorithm computes the distributed semantic similarity between the expanded class topics and the text documents in the test corpus. We evaluate our categorization algorithm using a multilabel text categorization problem. The multilabel categorization task uses the JRC-Acquis dataset. The JRC-Acquis dataset is based on subject domain classification of the European Commission's EuroVoc microthesaurus. We compare the performance of the classifier with a model of it using the original class topics. Furthermore, we compare the performance of our classifier with two state-of-the-art supervised algorithms (each for multilingual and cross-lingual tasks) using the same dataset. Empirical results obtained on five experimental languages show that categorization with expanded topics shows a very wide performance margin when compared to usage of the original topics. Our algorithm outperforms the existing supervised technique, which used the same dataset. Cross-language categorization surprisingly shows similar performance and is marginally better for some of the languages.

show abstract

Section: Topic Disambiguationmentioning

confidence: 81%

Section: Topic Disambiguationmentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An index-based joint multilingual/cross-lingual text categorization using topic expansion via BabelNet

Ayetiran¹

2020

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the same vein, the word "years" (the plural of second noun sense of the word "year" in WordNet) can be replaced with any of the words "months" or "weeks" in the two previous statements representing the hyponymy relation. Ayetiran and Agbele [5] and Ayetiran [4] posit that words are best characterized by their specific definitions and explore this assertion for WSD and text classification. Following up on this position, lexically related word senses have something in common in their definitions.…”

Section: Introductionmentioning

confidence: 99%

EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses

Ayetiran

Sojka

Novotný

2021

Knowledge-Based Systems

Self Cite

View full text Add to dashboard Cite

Several language applications often require word semantics as a core part of their processing pipeline either as precise meaning inference or semantic similarity. Multi-sense embeddings (M-SE) can be exploited for this important requirement. M-SE seeks to represent each word by their distinct senses in order to resolve the conflation of meanings of words as used in different contexts. Previous works usually approach this task by training a model on a large corpus and often ignore the effect and usefulness of the semantic relations offered by lexical resources. However, even with large training data, coverage of all possible word senses is still an issue. In addition, a considerable percentage of contextual semantic knowledge are never learned because a huge amount of possible distributional semantic structures are never explored. In this paper, we leverage the rich semantic structures in WordNet using a graph-theoretic walk technique over word senses to enhance the quality of multisense embeddings. This algorithm composes enriched texts from the original texts. Furthermore, we derive new distributional semantic similarity measures for M-SE from prior ones. We adapt these measures to word sense disambiguation (WSD) aspect of our experiment. We report evaluation results on 11 benchmark datasets involving WSD and Word Similarity tasks and show that our method for enhancing distributional semantic structures improves embeddings quality on the baselines. Despite the small training data, it achieves state-of-the-art performance on some of the datasets.

show abstract

A Comparative Study of Existing Knowledge Based Techniques for Word Sense Disambiguation

Purohit

Yogi

2022

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

An Optimized Lesk-Based Algorithm for Word Sense Disambiguation

Cited by 10 publications

References 2 publications

An index-based joint multilingual/cross-lingual text categorization using topic expansion via BabelNet

An index-based joint multilingual/cross-lingual text categorization using topic expansion via BabelNet

EDS-MEMBED: Multi-sense embeddings based on enhanced distributional semantic structures via a graph walk over word senses

A Comparative Study of Existing Knowledge Based Techniques for Word Sense Disambiguation

Contact Info

Product

Resources

About