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Automatic Construction of Inference-Supporting Knowledge Bases
Peter Clark, Niranjan Balasubramanian, Sumithra Bhakthavatsalam, Kevin Humphreys, Jesse Kinkead, Ashish Sabharwal, and Oyvind TafjordAKBC • 2014 While there has been tremendous progress in automatic database population in recent years, most of human knowledge does not naturally fit into a database form. For example, knowledge that "metal objects can conduct electricity" or "animals grow fur to help…Chinese Open Relation Extraction for Knowledge Acquisition
Yuen-Hsien Tseng, Lung-Hao Lee, Shu-Yen Lin, Bo-Shun Liao, Mei-Jun Liu, Hsin-Hsi Chen, Oren Etzioni, and Anthony FaderEACL • 2014 This study presents the Chinese Open Relation Extraction (CORE) system that is able to extract entity-relation triples from Chinese free texts based on a series of NLP techniques, i.e., word segmentation, POS tagging, syntactic parsing, and extraction rules…Freebase QA: Information Extraction or Semantic Parsing?
Xuchen Yao, Jonathan Berant, and Benjamin Van DurmeACL • Workshop on Semantic Parsing • 2014 We contrast two seemingly distinct approaches to the task of question answering (QA) using Freebase: one based on information extraction techniques, the other on semantic parsing. Results over the same test-set were collected from two state-ofthe-art, open…Insights Into Parallelism with Intensive Knowledge Sharing
Ashish Sabharwal and Horst SamulowitzInternational Conference on Principles and Practice of Constraint Programming • 2014 Novel search space splitting techniques have recently been successfully exploited to paralleliz Constraint Programming and Mixed Integer Programming solvers. We first show how universal hashing can be used to extend one such interesting approach to a…Modeling Biological Processes for Reading Comprehension
Jonathan Berant, Vivek Srikumar, Pei-Chun Chen, Brad Huang, Christopher D. Manning, Abby Vander Linden, Brittany Harding, and Peter ClarkEMNLP • 2014 Machine reading calls for programs that read and understand text, but most current work only attempts to extract facts from redundant web-scale corpora. In this paper, we focus on a new reading comprehension task that requires complex reasoning over a single…Open Question Answering Over Curated and Extracted Knowledge Bases
Anthony Fader, Luke Zettlemoyer, and Oren EtzioniKDD • 2014 We consider the problem of open-domain question answering (Open QA) over massive knowledge bases (KBs). Existing approaches use either manually curated KBs like Freebase or KBs automatically extracted from unstructured text. In this paper, we present oqa, the…Discourse Complements Lexical Semantics for Non-factoid Answer Reranking
Peter Jansen, Mihai Surdeanu, and Peter ClarkACL • 2014 We propose a robust answer reranking model for non-factoid questions that integrates lexical semantics with discourse information, driven by two representations of discourse: a shallow representation centered around discourse markers, and a deep one based on…A Lightweight and High Performance Monolingual Word Aligner
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkACL • 2013 Fast alignment is essential for many natural language tasks. But in the setting of monolingual alignment, previous work has not been able to align more than one sentence pair per second. We describe a discriminatively trained monolingual word aligner that…Automatic Coupling of Answer Extraction and Information Retrieval
Xuchen Yao, Benjamin Van Durme, and Peter ClarkACL • 2013 Information Retrieval (IR) and Answer Extraction are often designed as isolated or loosely connected components in Question Answering (QA), with repeated overengineering on IR, and not necessarily performance gain for QA. We propose to tightly integrate them…Answer Extraction as Sequence Tagging with Tree Edit Distance
Xuchen Yao, Benjamin Van Durme, Chris Callision-Burch, and Peter ClarkNAACL • 2013 Our goal is to extract answers from preretrieved sentences for Question Answering (QA). We construct a linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer…