Investigating convolutional networks and domain-specific embeddings for semantic classification of citations

Lauscher, Anne ; Glavaš, Goran ; Ponzetto, Simone Paolo ; Eckert, Kai

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URN: urn:nbn:de:bsz:180-madoc-428184
Document Type: Conference or workshop publication
Year of publication: 2017
Book title: WOSP 2017 : 6th International Workshop on Mining Scientific Publications : Toronto, ON, Canada, June 19, 2017
The title of a journal, publication series: ACM international conference proceedings series : AICPS
Page range: 24-28
Conference title: 6th International workshop On Mining Scientific Publications
Location of the conference venue: Toronto, ON, Canada
Date of the conference: 19 June 2017
Publisher: Knoth, Petr
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-5388-5
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik III (Ponzetto 2016-)
Subject: 004 Computer science, internet
Keywords (English): Natural Language Processing , Convolutional Neural Networks , Citation Analysis , Word Embeddings
Abstract: Citation graphs and indices underpin most bibliometric analyses. However, measures derived from citation graphs do not provide insights into qualitative aspects of scientific publications. In this work, we aim to semantically characterize citations in terms of polarity and purpose. We frame polarity and purpose detection as classification tasks and investigate the performance of convolutional networks with general and domain-specific word embeddings on these tasks. Our best performing model outperforms previously reported results on a benchmark dataset by a wide margin.

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