Text Frame Detector: Slot Filling Based On Domain Knowledge Bases

English. In this paper we present a system called Text Frame Detector (TFD) which aims at populating a frame-based ontology in a graph-based structure. Our system organizes textual information into frames, according to a predefined set of semantically informed patterns linking pre-coded information such as named entities, simple and complex terms. Given the semiautomatic expansion of such information with word embeddings, the system can be easily adapted to new domains.

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

[2]  Dong-Hong Ji,et al.  Relation Extraction Using Label Propagation Based Semi-Supervised Learning , 2006, ACL.

[3]  Diego Reforgiato Recupero,et al.  Semantic Web Machine Reading with FRED , 2017, Semantic Web.

[4]  Razvan C. Bunescu,et al.  Subsequence Kernels for Relation Extraction , 2005, NIPS.

[5]  Andrew McCallum,et al.  Modeling Relations and Their Mentions without Labeled Text , 2010, ECML/PKDD.

[6]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[7]  Daniel S. Weld,et al.  Using Wikipedia to bootstrap open information extraction , 2009, SGMD.

[8]  Ralph Grishman,et al.  Relation Extraction: Perspective from Convolutional Neural Networks , 2015, VS@HLT-NAACL.

[9]  C. Fillmore.  FRAME SEMANTICS AND THE NATURE OF LANGUAGE * , 1976 .

[10]  Marco Rospocher,et al.  Frame-Based Ontology Population with PIKES , 2016, IEEE Transactions on Knowledge and Data Engineering.

[11]  Daniel Jurafsky,et al.  Distant supervision for relation extraction without labeled data , 2009, ACL.

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

[13]  Mihai Surdeanu.  Overview of the TAC2013 Knowledge Base Population Evaluation: English Slot Filling and Temporal Slot Filling , 2013, TAC.

[14]  Guy Aston,et al.  Introducing the La Repubblica Corpus: A Large, Annotated, TEI(XML)-compliant Corpus of Newspaper Italian , 2004, LREC.

[15]  Gerard de Melo,et al.  FrameBase: Enabling integration of heterogeneous knowledge , 2017, Semantic Web.

[16]  Alessandro Lenci,et al.  Extracting Terms with EXTra , 2016 .

[17]  Tomas Mikolov,et al.  Enriching Word Vectors with Subword Information , 2016, TACL.

[18]  Luis Gravano,et al.  Snowball: extracting relations from large plain-text collections , 2000, DL '00.

[19]  Diego Reforgiato Recupero,et al.  Framester: A Wide Coverage Linguistic Linked Data Hub , 2016, EKAW.

[20]  Dmitry Zelenko,et al.  Kernel Methods for Relation Extraction , 2002, J. Mach. Learn. Res..

[21]  James H. Martin,et al.  Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition, 2nd Edition , 2000, Prentice Hall series in artificial intelligence.

[22]  Romaric Besançon,et al.  Text Segmentation and Graph-based Method for Template Filling in Information Extraction , 2011, IJCNLP.

[23]  Alessandro Lenci,et al.  INFORMed PA: A NER for the Italian Public Administration Domain , 2017, CLiC-it.

[24]  Philippe Cudré-Mauroux,et al.  Relation Extraction Using Distant Supervision , 2018, ACM Comput. Surv..

[25]  Waqar Mahmood,et al.  A survey of ontology learning techniques and applications , 2018, Database J. Biol. Databases Curation.

[26]  Miguel Pignatelli,et al.  Database: The Journal of Biological Databases and Curation , 2016 .

[27]  Yue Zhang,et al.  End-to-End Neural Relation Extraction with Global Optimization , 2017, EMNLP.

[28]  Farrokh Mistree,et al.  An Ontology for Reusable and Executable Decision Templates , 2017, J. Comput. Inf. Sci. Eng..