Joint Inference for Fine-grained Opinion Extraction
Published 1 August 2013
Bishan Yang, Claire Cardie
Citations198
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Experimental results demonstrate that the joint inference approach significantly outperforms traditional pipeline methods and baselines that tackle subtasks in isolation for the problem of opinion extraction.
Abstract
This paper addresses the task of finegrained opinion extraction – the identification of opinion-related entities: the opinion expressions, the opinion holders, and the targets of the opinions, and the relations between opinion expressions and their targets and holders. Most existing approaches tackle the extraction of opinion entities and opinion relations in a pipelined manner, where the interdependencies among different extraction stages are not captured. We propose a joint inference model that leverages knowledge from predictors that optimize subtasks of opinion extraction, and seeks a globally optimal solution. Experimental results demonstrate that our joint inference approach significantly outperforms traditional pipeline methods and baselines that tackle subtasks in isolation for the problem of opinion extraction. 1
Keywords
Computer Science
ScholarlyCommons (University of Pennsylvania)Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
12,978 Citations2001John Lafferty, Andrew McCallum +1 more
This work presents iterative parameter estimation algorithms for conditional random fields and compares the performance of the resulting models to HMMs and MEMMs on synthetic and natural-language data.
now publishers, Inc. eBooksOpinion Mining and Sentiment Analysis
6,745 Citations2008Bo Pang, Lillian Lee
LIBLINEAR: A Library for Large Linear Classification
6,625 Citations2008Rong-En Fan, Kai‐Wei Chang +3 more
Encyclopedia of Machine Learning and Data MiningSentiment Analysis and Opinion Mining
3,319 Citations2017Lei Zhang, Bing Liu
Computers and the HumanitiesAnnotating Expressions of Opinions and Emotions in Language
1,770 Citations2005Janyce Wiebe, Theresa Wilson +1 more
The manual annotation process and the results of an inter-annotator agreement study on a 10,000-sentence corpus of articles drawn from the world press are presented.
Mining opinion features in customer reviews
1,273 Citations2004Minqing Hu, Bing Liu
This project aims to summarize all the customer reviews of a product by mining opinion/product features that the reviewers have commented on and a number of techniques are presented to mine such features.
Computational LinguisticsOpinion Word Expansion and Target Extraction through Double Propagation
1,100 Citations2011Guang Qiu, Bing Liu +2 more
This article study two important problems, namely, opinion lexicon expansion and opinion target extraction, and proposes a method based on bootstrapping that outperforms these existing methods significantly.
Computational LinguisticsRecognizing Contextual Polarity: An Exploration of Features for Phrase-Level Sentiment Analysis
731 Citations2009Theresa Wilson, Janyce Wiebe +1 more
The goal of this work is to automatically distinguish between prior and contextual polarity, with a focus on understanding which features are important for this task, and it is shown that the presence of neutral instances greatly degrades the performance of features for distinguishing between positive and negative polarity.
Extracting opinions, opinion holders, and topics expressed in online news media text
460 Citations2006Soo-Min Kim, Eduard Hovy
This method uses semantic role labeling as an intermediate step to label an opinion holder and topic using data from FrameNet, and decomposes the task into three phases: identifying an opinion-bearing word, labeling semantic roles related to the word in the sentence, and then finding the holder and the topic of the opinion word among the labeled semantic roles.
Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)A Linear Programming Formulation for Global Inference in Natural Language Tasks
442 Citations2004Dan Roth, Wen-tau Yih
This work develops a linear programing formulation for this problem and evaluates it in the context of simultaneously learning named entities and relations to efficiently incorporate domain and task specific constraints at decision time, resulting in significant improvements in the accuracy and the "human-like" quality of the inferences.
Expanding domain sentiment lexicon through double propagation
415 Citations2009Guang Qiu, Bing Liu +2 more
A novel propagation approach is proposed that exploits the relations between sentiment words and topics or product features that the sentiment words modify, and also sentiment Words and product features themselves to extract new sentiment words.
Computational LinguisticsThe Importance of Syntactic Parsing and Inference in Semantic Role Labeling
413 Citations2008Vasin Punyakanok, Dan Roth +1 more
It is shown that full syntactic parsing information is, by far, most relevant in identifying the argument, especially in the very first stagethe pruning stage, and an effective and simple approach of combining different semantic role labeling systems through joint inference is proposed, which significantly improves its performance.
Identifying sources of opinions with conditional random fields and extraction patterns
366 Citations2005Yejin Choi, Claire Cardie +2 more
This work adopts a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a), and shows that the combination of these two methods performs better than either one alone.
Phrase dependency parsing for opinion mining
365 Citations2009Yuanbin Wu, Qi Zhang +2 more
A novel approach for mining opinions from product reviews is presented, where it converts opinion mining task to identify product features, expressions of opinions and relations between them by taking advantage of the observation that a lot of product features are phrases.
Extracting and Ranking Product Features in Opinion Documents
267 Citations2010Lei Zhang, Bing Liu +2 more
The problem is formulated as a bipartite graph and the well-known web page ranking algorithm HITS is used to find important features and rank them high and demonstrates promising results on diverse real-life datasets.
International Joint Conference on Artificial IntelligenceIdentifying expressions of opinion in context
253 Citations2007Eric Breck, Yejin Choi +1 more
An approach for identifying opinion expressions that uses conditional random fields and achieves expression-level performance within 5% of the human interannotator agreement is presented.
Joint extraction of entities and relations for opinion recognition
213 Citations2006Yejin Choi, Eric Breck +1 more
An integer linear programming approach is employed to solve the joint opinion recognition task, and it is shown that global, constraint-based inference can significantly boost the performance of both relation extraction and the extraction of opinion-related entities.
Topic identification for fine-grained opinion analysis
192 Citations2008Veselin Stoyanov, Claire Cardie
This paper provides an operational definition of opinion topic and presents an algorithm for opinion topic identification that, following the new definition, treats the task as a problem in topic coreference resolution.
Empirical Methods in Natural Language ProcessingExtracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining
192 Citations2007Nozomi Kobayashi, Kentaro Inui +1 more
This work defines an opinion unit as a quadruple consisting of the opinion holder, the subject being evaluated, the part or the attribute in which the subject is evaluated, and the value of the evaluation that expresses a positive or negative assessment.
Opinion Target Extraction Using Word-Based Translation Model
143 Citations2012Kang Liu, Liheng Xu +1 more
A graph-based algorithm is exploited to extract opinion targets based on word-based translation model, which can effectively alleviate the problem of error propagation in traditional bootstrap-based methods, such as Double Propagation.
Extracting Opinion Expressions with semi-Markov Conditional Random Fields
140 Citations2012Bishan Yang, Claire Cardie
This work extends the original semi-CRF model to allow the modeling of arbitrarily long expressions while accounting for their likely syntactic structure when modeling segment boundaries, and demonstrates that this approach outperforms state-of-the-art methods for both opinion expression tasks.
Finding the Sources and Targets of Subjective Expressions
97 Citations2008Josef Ruppenhofer, Swapna Somasundaran +1 more
It is argued that while automatic semantic role labeling systems (ASRL) have an important contribution to make, they cannot solve the problem for all cases and close attention to specific discourse contexts is needed to identify sources and targets correctly.
Computational LinguisticsRelational Features in Fine-Grained Opinion Analysis
68 Citations2012Richard Johansson, Alessandro Moschitti
A set of experiments are described that demonstrate that relational features, mainly derived from dependency-syntactic and semantic role structures, can significantly improve the performance of automatic systems for a number of fine-grained opinion analysis tasks: marking up opinion expressions, finding opinion holders, and determining the polarities of opinion expressions.
Institutional Research Information System (Università degli Studi di Trento)Syntactic and Semantic Structure for Opinion Expression Detection
65 Citations2010Richard Johansson, Alessandro Moschitti
It is demonstrated that relational features derived from dependency-syntactic and semantic role structures are useful for the task of detecting opinionated expressions in natural-language text, significantly improving over conventional models based on sequence labeling with local features.
Institutional Research Information System (Università degli Studi di Trento)Reranking Models in Fine-grained Opinion Analysis
51 Citations2010Richard Johansson, Alessandro Moschitti
The implementation of reranking models for fine-grained opinion analysis -- marking up opinion expressions and extracting opinion holders and the experiments showed significant improvements over a conventional system that only uses local information.
FigshareAn Exact Dual Decomposition Algorithm for Shallow Semantic Parsing with Constraints
51 Citations2018Dipanjan Das, André F. T. Martins +1 more
A novel technique for jointly predicting semantic arguments for lexical predicates using a dual decomposition algorithm, which can be adapted for exact decoding via a branch-and-bound technique.
A Joint Model for Extended Semantic Role Labeling
38 Citations2011Vivek Srikumar, Dan Roth
A joint inference model is defined that captures the inter-dependencies between verb semantic role labeling and relations expressed using prepositions and shows improvements in the performance of both tasks without retraining the individual models.