IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages
| Autor: | Rexhina Blloshmi, Tommaso Pasini, Niccolò Campolungo, Somnath Banerjee, Roberto Navigli, Gabriella Pasi |
|---|---|
| Jazyk: | angličtina |
| Předmět: |
sense-enhanced
InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 02 engineering and technology 010501 environmental sciences nlp 01 natural sciences natural language processing information retrieval ir word sense disambiguation wsd multilinguality strategies tools standards for lexicographic resources (objective 3) WP3 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing 0105 earth and related environmental sciences |
| Zdroj: | Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing |
| DOI: | 10.18653/v1/2021.emnlp-main.79 |
| Popis: | With the advent of contextualized embeddings, attention towards neural ranking approaches for Information Retrieval increased considerably. However, two aspects have remained largely neglected: i) queries usually consist of few keywords only, which increases ambiguity and makes their contextualization harder, and ii) performing neural ranking on non-English documents is still cumbersome due to shortage of labeled datasets. In this paper we present SIR (Sense-enhanced Information Retrieval) to mitigate both problems by leveraging word sense information. At the core of our approach lies a novel multilingual query expansion mechanism based on Word Sense Disambiguation that provides sense definitions as additional semantic information for the query. Importantly, we use senses as a bridge across languages, thus allowing our model to perform considerably better than its supervised and unsupervised alternatives across French, German, Italian and Spanish languages on several CLEF benchmarks, while being trained on English Robust04 data only. We release SIR at https://github.com/SapienzaNLP/sir. |
| Databáze: | OpenAIRE |
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