Corpus-level Fine-grained Entity Typing Using Contextual Information

This paper addresses the problem of corpus-level entity typing, i.e., inferring from a large corpus that an entity is a member of a class such as "food" or "artist". The application of entity typing we are interested in is knowledge base completion, specifically, to learn which classes an entity is a member of. We propose FIGMENT to tackle this problem. FIGMENT is embedding-based and combines (i) a global model that scores based on aggregated contextual information of an entity and (ii) a context model that first scores the individual occurrences of an entity and then aggregates the scores. In our evaluation, FIGMENT strongly outperforms an approach to entity typing that relies on relations obtained by an open information extraction system.

[1]  Ralph Grishman,et al.  Distant Supervision for Relation Extraction with an Incomplete Knowledge Base , 2013, NAACL.

[2]  Ramesh Nallapati,et al.  Multi-instance Multi-label Learning for Relation Extraction , 2012, EMNLP.

[3]  Zellig S. Harris,et al.  Distributional Structure , 1954 .

[4]  Hans-Peter Kriegel,et al.  Factorizing YAGO: scalable machine learning for linked data , 2012, WWW.

[5]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[6]  Xueyan Jiang,et al.  Link Prediction in Multi-relational Graphs using Additive Models , 2012, SeRSy.

[7]  Thomas Hofmann,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2007 .

[8]  Hong Sun,et al.  A Hybrid Neural Model for Type Classification of Entity Mentions , 2015, IJCAI.

[9]  Ming-Wei Chang,et al.  Inferring Missing Entity Type Instances for Knowledge Base Completion: New Dataset and Methods , 2015, NAACL.

[10]  Andrew McCallum,et al.  Relation Extraction with Matrix Factorization and Universal Schemas , 2013, NAACL.

[11]  Georgiana Dinu,et al.  Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors , 2014, ACL.

[12]  Gerhard Weikum,et al.  HYENA: Hierarchical Type Classification for Entity Names , 2012, COLING.

[13]  Christopher D. Manning,et al.  Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling , 2005, ACL.

[14]  Daniel S. Weld,et al.  Design Challenges for Entity Linking , 2015, TACL.

[15]  Oren Etzioni,et al.  No Noun Phrase Left Behind: Detecting and Typing Unlinkable Entities , 2012, EMNLP.

[16]  Ellen Riloff,et al.  A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts , 2002, EMNLP.

[17]  Zhiyuan Liu,et al.  Representation Learning for Measuring Entity Relatedness with Rich Information , 2015, IJCAI.

[18]  Zellig S. Harris,et al.  Distributional Structure , 1954 .

[19]  Christopher D. Manning,et al.  Improved Pattern Learning for Bootstrapped Entity Extraction , 2014, CoNLL.

[20]  Praveen Paritosh,et al.  Freebase: a collaboratively created graph database for structuring human knowledge , 2008, SIGMOD Conference.

[21]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[22]  David K. Levine,et al.  Against Intellectual Monopoly , 2008 .

[23]  Danqi Chen,et al.  Reasoning With Neural Tensor Networks for Knowledge Base Completion , 2013, NIPS.

[24]  Zhen Wang,et al.  Knowledge Graph and Text Jointly Embedding , 2014, EMNLP.

[25]  Oren Etzioni,et al.  Identifying Relations for Open Information Extraction , 2011, EMNLP.

[26]  Jason Weston,et al.  Irreflexive and Hierarchical Relations as Translations , 2013, ArXiv.

[27]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[28]  Nevena Lazic,et al.  Embedding Methods for Fine Grained Entity Type Classification , 2015, ACL.

[29]  Daniel S. Weld,et al.  Fine-Grained Entity Recognition , 2012, AAAI.

[30]  Gerhard Weikum,et al.  Fine-grained Semantic Typing of Emerging Entities , 2013, ACL.

[31]  Jason Weston,et al.  Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction , 2013, EMNLP.

[32]  Gerhard Weikum,et al.  WWW 2007 / Track: Semantic Web Session: Ontologies ABSTRACT YAGO: A Core of Semantic Knowledge , 2022 .

[33]  Dan Klein,et al.  A Joint Model for Entity Analysis: Coreference, Typing, and Linking , 2014, TACL.

[34]  Heng Ji,et al.  Overview of the TAC 2010 Knowledge Base Population Track , 2010 .