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{W}iki{NE}u{R}al: {C}ombined Neural and Knowledge-based Silver Data Creation for Multilingual {NER} ...
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IR like a SIR: Sense-enhanced Information Retrieval for Multiple Languages ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.79/ Abstract: 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, ...
Keyword: Data Management System; Machine translation; Natural Language Processing; Neural Network
URL: https://underline.io/lecture/37802-ir-like-a-sir-sense-enhanced-information-retrieval-for-multiple-languages
https://dx.doi.org/10.48448/z11a-gr02
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