Generative Modeling of Entity Comparisons in Text

Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison acr...

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Main Authors: TKACHENKO, Maksim, LAUW, Hady W.
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
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Online Access:https://ink.library.smu.edu.sg/sis_research/2329
https://ink.library.smu.edu.sg/context/sis_research/article/3329/viewcontent/cikm14a.pdf
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spelling sg-smu-ink.sis_research-33292017-12-26T09:04:05Z Generative Modeling of Entity Comparisons in Text TKACHENKO, Maksim LAUW, Hady W. Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is two-fold: to interpret the comparative direction in each sentence, and to determine the relative merits of each entity. This requires mining comparative relations at two levels of resolution: at the sentence level, as well as at the entity level. Our key observation is that there is significant synergy between the two levels. We therefore propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level. This model is tested comprehensively on Amazon reviews dataset with good empirical outperformance over the state-of-the-art baselines. 2014-11-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2329 info:doi/10.1145/2661829.2662016 https://ink.library.smu.edu.sg/context/sis_research/article/3329/viewcontent/cikm14a.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University generative model comparative sentences comparison mining Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic generative model
comparative sentences
comparison mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle generative model
comparative sentences
comparison mining
Databases and Information Systems
Numerical Analysis and Scientific Computing
TKACHENKO, Maksim
LAUW, Hady W.
Generative Modeling of Entity Comparisons in Text
description Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set of comparative sentences, each relating a pair of entities, our objective is two-fold: to interpret the comparative direction in each sentence, and to determine the relative merits of each entity. This requires mining comparative relations at two levels of resolution: at the sentence level, as well as at the entity level. Our key observation is that there is significant synergy between the two levels. We therefore propose a generative model for comparative text, which jointly models comparative directions at the sentence level, and ranking at the entity level. This model is tested comprehensively on Amazon reviews dataset with good empirical outperformance over the state-of-the-art baselines.
format text
author TKACHENKO, Maksim
LAUW, Hady W.
author_facet TKACHENKO, Maksim
LAUW, Hady W.
author_sort TKACHENKO, Maksim
title Generative Modeling of Entity Comparisons in Text
title_short Generative Modeling of Entity Comparisons in Text
title_full Generative Modeling of Entity Comparisons in Text
title_fullStr Generative Modeling of Entity Comparisons in Text
title_full_unstemmed Generative Modeling of Entity Comparisons in Text
title_sort generative modeling of entity comparisons in text
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2329
https://ink.library.smu.edu.sg/context/sis_research/article/3329/viewcontent/cikm14a.pdf
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