<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="nimishakavi-etal-2018-higher">
    <titleInfo>
        <title>Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Madhav</namePart>
        <namePart type="family">Nimishakavi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Manish</namePart>
        <namePart type="family">Gupta</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Partha</namePart>
        <namePart type="family">Talukdar</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets we show how TFBA helps in dealing with sparsity and induce higher-order schemata.</abstract>
    <identifier type="citekey">nimishakavi-etal-2018-higher</identifier>
    <identifier type="doi">10.18653/v1/P18-1146</identifier>
    <location>
        <url>https://aclanthology.org/P18-1146</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>1575</start>
            <end>1584</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation
%A Nimishakavi, Madhav
%A Gupta, Manish
%A Talukdar, Partha
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F nimishakavi-etal-2018-higher
%X Relation Schema Induction (RSI) is the problem of identifying type signatures of arguments of relations from unlabeled text. Most of the previous work in this area have focused only on binary RSI, i.e., inducing only the subject and object type signatures per relation. However, in practice, many relations are high-order, i.e., they have more than two arguments and inducing type signatures of all arguments is necessary. For example, in the sports domain, inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is more informative than inducing just win(WinningPlayer, OpponentPlayer). We refer to this problem as Higher-order Relation Schema Induction (HRSI). In this paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a novel framework for the HRSI problem. To the best of our knowledge, this is the first attempt at inducing higher-order relation schemata from unlabeled text. Using the experimental analysis on three real world datasets we show how TFBA helps in dealing with sparsity and induce higher-order schemata.
%R 10.18653/v1/P18-1146
%U https://aclanthology.org/P18-1146
%U https://doi.org/10.18653/v1/P18-1146
%P 1575-1584

Markdown (Informal)

[Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation](https://aclanthology.org/P18-1146) (Nimishakavi et al., ACL 2018)

• Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation (Nimishakavi et al., ACL 2018)

ACL

• Madhav Nimishakavi, Manish Gupta, and Partha Talukdar. 2018. Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1575–1584, Melbourne, Australia. Association for Computational Linguistics.