Neural Networks Models for Entity Discovery and Linking
This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests. We have built two systems for entity discovery and mention detection (MD): one uses the conditional RNNLM and the other one uses the attention-based encoder-decoder framework. The entity linking (EL) system consists of two modules: a rule based candidate generation and a neural networks probability ranking model. Moreover, some simple string matching rules are used for NIL clustering. At the end, our best system has achieved an F1 score of 0.624 in the end-to-end typed mention ceaf plus metric.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringDecoderEntity LinkingKnowledge Base PopulationSimilar Papers 제목 키워드 기반
Entity Linking and Discovery via Arborescence-based Supervised Clustering
Previous work has shown promising results in performing entity linking by measuring not only the affinities between mentions and entities but also those amongst mentions. In this paper, we present novel training and infe…
ClusteringEntity LinkingA Knowledge Graph Based Solution for Entity Discovery and Linking in Open-Domain Questions
Named entity discovery and linking is the fundamental and core component of question answering. In Question Entity Discovery and Linking (QEDL) problem, traditional methods are challenged because multiple entities in one…
Entity LinkingLearning-To-RankQuestion AnsweringRetrieval+3EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing
Existing work on Entity Linking mostly assumes that the reference knowledge base is complete, and therefore all mentions can be linked. In practice this is hardly ever the case, as knowledge bases are incomplete and beca…
Entity LinkingNovel ConceptsRetrievalMahānāma: A Unique Testbed for Literary Entity Discovery and Linking
High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. We present Mahānāma, the first large-scale dataset for end-to-end Entity Discov…
Entity ResolutionEntity LinkingEntity Linking via Explicit Mention-Mention Coreference Modeling
Learning representations of entity mentions is a core component of modern entity linking systems for both candidate generation and making linking predictions. In this paper, we present and empirically analyze a novel tra…
Entity LinkingRe-Ranking