Micro Reading with Priors : Towards Second Generation Machine Readers

Micro reading in machine reading can be compared to deep reading in human reading. Deep reading has been defined as a set of processes that enable comprehension and that include inferential and deductive reasoning, analogical skills, critical analysis, reflection, and insight. In this paper, we sketch what we envision to be a viable approach to micro reading. The proposed approach leverages the knowledge that has been acquired by the machine readers that have been developed to date.

[1]  Mats Rooth,et al.  Structural Ambiguity and Lexical Relations , 1991, ACL.

[2]  Makoto Nagao,et al.  Corpus Based PP Attachment Ambiguity Resolution with a Semantic Dictionary , 1997, VLC.

[3]  Adwait Ratnaparkhi Statistical Models for Unsupervised Prepositional Phrase Attachment , 1998, COLING.

[4]  Patrick Pantel,et al.  An Unsupervised Approach to Prepositional Phrase Attachment using Contextually Similar Words , 2000, ACL.

[5]  Mark Steedman,et al.  The syntactic process , 2004, Language, speech, and communication.

[6]  Andrew Y. Ng,et al.  Learning random walk models for inducing word dependency distributions , 2004, ICML.

[7]  Luke S. Zettlemoyer,et al.  Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars , 2005, UAI.

[8]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[9]  J. Barlow The Shallows: What the Internet is Doing to Our Brains , 2010 .

[10]  Gerhard Weikum,et al.  From information to knowledge: harvesting entities and relationships from web sources , 2010, PODS '10.

[11]  Estevam R. Hruschka,et al.  Coupled semi-supervised learning for information extraction , 2010, WSDM '10.

[12]  Dan Klein,et al.  Learning Dependency-Based Compositional Semantics , 2011, CL.

[13]  Cong Yu,et al.  REX: Explaining Relationships between Entity Pairs , 2011, Proc. VLDB Endow..

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

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

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

[17]  Jeffrey Dean,et al.  Distributed Representations of Words and Phrases and their Compositionality , 2013, NIPS.

[18]  Manuela M. Veloso,et al.  OpenEval: Web Information Query Evaluation , 2013, AAAI.

[19]  Philipp Koehn,et al.  Abstract Meaning Representation for Sembanking , 2013, LAW@ACL.

[20]  Tom M. Mitchell,et al.  Aligning context-based statistical models of language with brain activity during reading , 2014, EMNLP.

[21]  Tom M. Mitchell,et al.  Language-Aware Truth Assessment of Fact Candidates , 2014, ACL.