NDLI: Relation classification via modeling augmented dependency paths

Content Provider	ACM Digital Library
Author	Li, Sujian Wei, Furu Ji, Heng Liu, Yang
Copyright Year	2016
Abstract	Previous research on relation classification has verified the effectiveness of using dependency shortest paths or dependency subtrees. How to efficiently unify these two kinds of dependency information in relation classification is still an open problem. In this paper, we propose a novel structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop the dependency-based neural networks (DepNN) model which combines the advantages of the recursive neural network (RNN) and the convolutional neural network (CNN). In DepNN, RNN is designed to model the dependency subtrees since it is good at capturing the hierarchical structures. Then, the semantic representation in subtrees is passed to the nodes on the shortest path and CNN is used to get the most important features on the ADP. Experiments on the SemEval-2010 dataset show that the ADP structure including both the shortest dependency path and the attached subtrees is helpful to classify the semantic relations between two entities and our proposed method can achieve the state-of-the-art performance.
Starting Page	1585
Ending Page	1594
Page Count	10
File Format	PDF
ISSN	23299290
e-ISSN	23299304
Volume Number	24
Issue Number	9
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Convolution neural network Dependency subtree Recursive neural network Relation classification Shortest dependency path
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Adaptive multi-compositionality for recursive neural network models

Dependency parse reranking with rich subtree features

Adaptive Multi-Compositionality for Recursive Neural Network Models

Online speech dereverberation using kalman filter and EM algorithm

Online Speech Dereverberation Using Kalman Filter and EM Algorithm

Convolutional neural networks for speech recognition

Extractive broadcast news summarization leveraging recurrent neural network language modeling techniques

Dependency Parse Reranking with Rich Subtree Features

Regression-based context-dependent modeling of deep neural networks for speech recognition

Relation classification via modeling augmented dependency paths

Similar Documents

Adaptive multi-compositionality for recursive neural network models

Dependency parse reranking with rich subtree features

Adaptive Multi-Compositionality for Recursive Neural Network Models

Online speech dereverberation using kalman filter and EM algorithm

Online Speech Dereverberation Using Kalman Filter and EM Algorithm

Convolutional neural networks for speech recognition

Extractive broadcast news summarization leveraging recurrent neural network language modeling techniques

Dependency Parse Reranking with Rich Subtree Features

Regression-based context-dependent modeling of deep neural networks for speech recognition

Relation classification via modeling augmented dependency paths