The long road from performing word sense disambiguation to successfully using it in information retrieval: An overview of the unsupervised approach

2020 ◽  
Vol 36 (3) ◽  
pp. 1026-1062
Author(s):  
Florentina Hristea ◽  
Mihaela Colhon
Keyword(s):  
Information Retrieval ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Sense Disambiguation ◽  
Unsupervised Approach

scholarly journals Word sense disambiguation to improve precision for ambiguous queries

2012 ◽  
Vol 2 (4) ◽  
Author(s):  
Adrian-Gabriel Chifu ◽  
Radu-Tudor Ionescu
Keyword(s):  
Information Retrieval ◽  
Ad Hoc ◽  
Naive Bayes ◽  
Word Sense Disambiguation ◽  
Naïve Bayes ◽  
Word Sense ◽  
Interdisciplinary Approaches ◽  
Sense Disambiguation ◽  
Ranking Technique

AbstractSuccess in Information Retrieval (IR) depends on many variables. Several interdisciplinary approaches try to improve the quality of the results obtained by an IR system. In this paper we propose a new way of using word sense disambiguation (WSD) in IR. The method we develop is based on Naïve Bayes classification and can be used both as a filtering and as a re-ranking technique. We show on the TREC ad-hoc collection that WSD is useful in the case of queries which are difficult due to sense ambiguity. Our interest regards improving the precision after 5, 10 and 30 retrieved documents (P@5, P@10, P@30), respectively, for such lowest precision queries.

scholarly journals Generating Sense Inventories for Ambiguous Arabic Words

2021 ◽  
Author(s):  
Marwah Alian ◽  
Arafat Awajan
Keyword(s):  
Word Sense Disambiguation ◽  
Word Sense ◽  
Arabic Word ◽  
Word Sense Induction ◽  
Ambiguous Words ◽  
Sense Disambiguation ◽  
Sentence Similarity ◽  
Unsupervised Approach ◽  
Similar Accuracy

The process of selecting the appropriate meaning of an ambigous word according to its context is known as word sense disambiguation. In this research, we generate a number of Arabic sense inventories based on an unsupervised approach and different pre-trained embeddings, such as Aravec, Fast text, and Arabic-News embeddings. The resulted inventories from the pre-trained embeddings are evaluated to investigate their efficiency in Arabic word sense disambiguation and sentence similarity. The sense inventories are generated using an unsupervised approach that is based on a graph-based word sense induction algorithm. Results show that the Aravec-Twitter inventory achieves the best accuracy of 0.47 for 50 neighbors and a close accuracy to the Fast text inventory for 200 neighbors while it provides similar accuracy to the Arabic-News inventory for 100neighbors. The experiment of replacing ambiguous words with their sense vectors is tested for sentence similarity using all sense inventories and the results show that using Aravec-Twitter sense inventory provides a better correlation value

Sign in / Sign up

Export Citation Format

Share Document