XLE nt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment (2021)

Abstract: Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.

## introduction named entities are references in natural text to real-world objects such as persons, locations, or organizations that can be denoted with a proper name. recognizing and handling these named entities in many languages is a difficult, yet crucial, step to language-agnostic text understanding and multilingual natural language processing (nlp) @xcite . as such, cross-lingual named entity lexica can be invaluable resources towards making tasks such as entity linking, named entity recognition @xcite , and information and knowledge base construction @xcite inherently multilingual. however, the coverage of many such multilingual entity lexica (e.g., wikipedia titles) is less complete for lower-resource languages and approaches to automatically generate them under-perform due to the poor performance of low-resource taggers @xcite . to perform low-resource ner, previous efforts have applied word alignment techniques to project available labels to other languages. @xcite applies heuristic approaches with alignment correction using an alignment dictionary of entity mentions. @xcite introduced a novel label propagation technique that creates a tag lexicon for the target language, while wang and manning (2014) instead projected model expectation rather than labels thus transferring word boundary uncertainty. additional work jointly performs word alignment while training bilingual name tagging @xcite ; however this method assumes the availability of named entity taggers in both languages. other methods have leveraged bilingual embeddings for projection @xcite . in this work, we propose using named-entity projection to automatically curate a large crosslingual entity lexicon for many language pairs. as shown figure 1 , we construct this resource by performing ner in a higher-resource language, then projecting the entities onto text in a lowerresource language using word-alignment models. our main contribution is the construction and release of a large web-mined cross-lingual entity dataset that will be beneficial to the nlp community. our proposed alignment model, lsp-align, principally combines the lexical, semantic, and phonetic signals to extract higher-quality cross-lingual entity pairs as verified on a ground-truth entity pair set. with lsp-align, we mined over 164m distinct cross-lingual entity pairs spanning 120 language pairs and freely release the xlent dataset in hope it spurs further work in cross-lingual nlp. ## preliminaries we formally define an entity collection as a collection of extracted text spans tied to named entity mentions. we denote these named entity mentions @xmath0, where is the ℎ named entity in the mention collection and is the size of . cross-lingual entity lexicon creation seeks to create two entity collections 1 and 2 in a source and target language respectively. these two collections should be generated such that for each entity mention @xmath1, there is a corresponding named @xmath2. ## mining cross-lingual entities we introduce our approach to automatically extract cross-lingual entity pairs from large mined corpora. ## experiments & results datasets we utilize a gold standard evaluation lexicon created by @xcite that leverages eight named parallel entity corpora . we select nine languages from a diverse set of resource availability, language families, and scripts for evaluation.