Xiao Ling


2021

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Evaluating Entity Disambiguation and the Role of Popularity in Retrieval-Based NLP
Anthony Chen | Pallavi Gudipati | Shayne Longpre | Xiao Ling | Sameer Singh
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Retrieval is a core component for open-domain NLP tasks. In open-domain tasks, multiple entities can share a name, making disambiguation an inherent yet under-explored problem. We propose an evaluation benchmark for assessing the entity disambiguation capabilities of these retrievers, which we call Ambiguous Entity Retrieval (AmbER) sets. We define an AmbER set as a collection of entities that share a name along with queries about those entities. By covering the set of entities for polysemous names, AmbER sets act as a challenging test of entity disambiguation. We create AmbER sets for three popular open-domain tasks: fact checking, slot filling, and question answering, and evaluate a diverse set of retrievers. We find that the retrievers exhibit popularity bias, significantly under-performing on rarer entities that share a name, e.g., they are twice as likely to retrieve erroneous documents on queries for the less popular entity under the same name. These experiments on AmbER sets show their utility as an evaluation tool and highlight the weaknesses of popular retrieval systems.

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Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text
Maya Varma | Laurel Orr | Sen Wu | Megan Leszczynski | Xiao Ling | Christopher Ré
Findings of the Association for Computational Linguistics: EMNLP 2021

Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge bases as well as the use of training datasets that provide low coverage over uncommon resources. In this work, we address these issues by proposing a cross-domain data integration method that transfers structural knowledge from a general text knowledge base to the medical domain. We utilize our integration scheme to augment structural resources and generate a large biomedical NED dataset for pretraining. Our pretrained model with injected structural knowledge achieves state-of-the-art performance on two benchmark medical NED datasets: MedMentions and BC5CDR. Furthermore, we improve disambiguation of rare entities by up to 57 accuracy points.

2019

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Improving Knowledge Base Construction from Robust Infobox Extraction
Boya Peng | Yejin Huh | Xiao Ling | Michele Banko
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers)

A capable, automatic Question Answering (QA) system can provide more complete and accurate answers using a comprehensive knowledge base (KB). One important approach to constructing a comprehensive knowledge base is to extract information from Wikipedia infobox tables to populate an existing KB. Despite previous successes in the Infobox Extraction (IBE) problem (e.g., DBpedia), three major challenges remain: 1) Deterministic extraction patterns used in DBpedia are vulnerable to template changes; 2) Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic-based extraction of unlinkable entities yields low precision, hurting both accuracy and completeness of the final KB. This paper presents a robust approach that tackles all three challenges. We build probabilistic models to predict relations between entity mentions directly from the infobox tables in HTML. The entity mentions are linked to identifiers in an existing KB if possible. The unlinkable ones are also parsed and preserved in the final output. Training data for both the relation extraction and the entity linking models are automatically generated using distant supervision. We demonstrate the empirical effectiveness of the proposed method in both precision and recall compared to a strong IBE baseline, DBpedia, with an absolute improvement of 41.3% in average F1. We also show that our extraction makes the final KB significantly more complete, improving the completeness score of list-value relation types by 61.4%.

2017

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Learning Translations via Matrix Completion
Derry Tanti Wijaya | Brendan Callahan | John Hewitt | Jie Gao | Xiao Ling | Marianna Apidianaki | Chris Callison-Burch
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Bilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework for completing the matrix. This method harnesses diverse bilingual and monolingual signals, each of which may be incomplete or noisy. Our model achieves state-of-the-art performance for both high and low resource languages.

2016

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Effective Crowd Annotation for Relation Extraction
Angli Liu | Stephen Soderland | Jonathan Bragg | Christopher H. Lin | Xiao Ling | Daniel S. Weld
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Design Challenges for Entity Linking
Xiao Ling | Sameer Singh | Daniel S. Weld
Transactions of the Association for Computational Linguistics, Volume 3

Recent research on entity linking (EL) has introduced a plethora of promising techniques, ranging from deep neural networks to joint inference. But despite numerous papers there is surprisingly little understanding of the state of the art in EL. We attack this confusion by analyzing differences between several versions of the EL problem and presenting a simple yet effective, modular, unsupervised system, called Vinculum, for entity linking. We conduct an extensive evaluation on nine data sets, comparing Vinculum with two state-of-the-art systems, and elucidate key aspects of the system that include mention extraction, candidate generation, entity type prediction, entity coreference, and coherence.

2011

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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
Raphael Hoffmann | Congle Zhang | Xiao Ling | Luke Zettlemoyer | Daniel S. Weld
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

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Machine Reading at the University of Washington
Hoifung Poon | Janara Christensen | Pedro Domingos | Oren Etzioni | Raphael Hoffmann | Chloe Kiddon | Thomas Lin | Xiao Ling | Mausam | Alan Ritter | Stefan Schoenmackers | Stephen Soderland | Dan Weld | Fei Wu | Congle Zhang
Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading