Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
Liu, Kang and Xu, Liheng and Zhao, Jun

Article Structure

Abstract

Mining opinion targets is a fundamental and important task for opinion mining from online reviews.

Introduction

With the rapid development of Web 2.0, huge amount of user reviews are springing up on the Web.

Related Work

Opinion target extraction isn’t a new task for opinion mining.

Opinion Target Extraction Methodology

To extract opinion targets from reviews, we adopt the framework proposed by (Liu et al., 2012), which is a graph-based extraction framework and

Experiments

4.1 Datasets and Evaluation Metrics

Conclusions and Future Work

This paper discusses the performance variation of syntax based methods and alignment based methods on opinion target extraction task for the dataset with different sizes, different languages and different domains.

Topics

alignment model

Appears in 32 sentences as: Alignment Model (4) alignment model (28)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. In contrast, alignment based methods used word alignment model to fulfill this task, which could avoid parsing errors without using parsing.
    Page 1, “Abstract”
  2. We further combine syntactic patterns with alignment model by using a partially supervised framework and investigate whether this combination is useful or not.
    Page 1, “Abstract”
  3. Nevertheless, we notice that the alignment model is a statistical model which needs sufficient data to estimate parameters.
    Page 2, “Introduction”
  4. To answer these questions, in this paper, we adopt a unified framework to extract opinion targets from reviews, in the key component of which we vary the methods between syntactic patterns and alignment model .
    Page 2, “Introduction”
  5. Furthermore, this paper naturally addresses another question: is it useful for opinion targets extraction when we combine syntactic patterns and word alignment model into a unified model?
    Page 2, “Introduction”
  6. this end, we employ a partially supervised alignment model (PSWAM) like (Gao et al., 2010; Liu et al., 2013).
    Page 2, “Introduction”
  7. Then, these partial alignment links can be regarded as the constrains for a standard unsupervised word alignment model .
    Page 2, “Introduction”
  8. (Liu et al., 2013) extend Liu’s method, which is similar to our method and also used a partially supervised alignment model to extract opinion targets from reviews.
    Page 3, “Related Work”
  9. In the first component, we respectively use syntactic patterns and unsupervised word alignment model (WAM) to capture opinion relations.
    Page 4, “Opinion Target Extraction Methodology”
  10. In addition, we employ a partially supervised word alignment model (PSWAM) to incorporate syntactic information into WAM.
    Page 4, “Opinion Target Extraction Methodology”
  11. 3.1.2 Unsupervised Word Alignment Model
    Page 4, “Opinion Target Extraction Methodology”

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word alignment

Appears in 22 sentences as: Word Alignment (4) word alignment (18)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. In contrast, alignment based methods used word alignment model to fulfill this task, which could avoid parsing errors without using parsing.
    Page 1, “Abstract”
  2. A word can find its corresponding modifiers by using a word alignment
    Page 1, “Introduction”
  3. Furthermore, this paper naturally addresses another question: is it useful for opinion targets extraction when we combine syntactic patterns and word alignment model into a unified model?
    Page 2, “Introduction”
  4. Then, these partial alignment links can be regarded as the constrains for a standard unsupervised word alignment model.
    Page 2, “Introduction”
  5. In the first component, we respectively use syntactic patterns and unsupervised word alignment model (WAM) to capture opinion relations.
    Page 4, “Opinion Target Extraction Methodology”
  6. In addition, we employ a partially supervised word alignment model (PSWAM) to incorporate syntactic information into WAM.
    Page 4, “Opinion Target Extraction Methodology”
  7. 3.1.2 Unsupervised Word Alignment Model
    Page 4, “Opinion Target Extraction Methodology”
  8. In this subsection, we present our method for capturing opinion relations using unsupervised word alignment model.
    Page 4, “Opinion Target Extraction Methodology”
  9. Similar to (Liu et al., 2012), every sentence in reviews is replicated to generate a parallel sentence pair, and the word alignment algorithm is applied to the monolingual scenario to align a nourflnoun phase with its modifiers.
    Page 4, “Opinion Target Extraction Methodology”
  10. In this subsection, we try to combine syntactic information with word alignment model.
    Page 5, “Opinion Target Extraction Methodology”
  11. They are treated as the constrains for the word alignment model.
    Page 5, “Opinion Target Extraction Methodology”

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conditional probabilities

Appears in 3 sentences as: conditional probabilities (2) conditional probability (1)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. Then the conditional probabilities between potential opinion target wt and potential opinion word wo can be es-
    Page 5, “Opinion Target Extraction Methodology”
  2. P (wt|w0) means the conditional probabilities between two words.
    Page 5, “Opinion Target Extraction Methodology”
  3. At the same time, we can obtain conditional probability P (w0|wt).
    Page 5, “Opinion Target Extraction Methodology”

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graph-based

Appears in 3 sentences as: graph-based (3)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. To extract opinion targets from reviews, we adopt the framework proposed by (Liu et al., 2012), which is a graph-based extraction framework and
    Page 3, “Opinion Target Extraction Methodology”
  2. In the second component, we adopt a graph-based algorithm used in (Liu et al., 2012) to compute the confidence of each opinion target candidate, and the candidates with higher confidence than the threshold will be extracted as the opinion targets.
    Page 6, “Opinion Target Extraction Methodology”
  3. In such situation, the graph-based ranking algorithm in the second component will be apt to be affected by the frequency information, so the final performance could not be sensitive to the performance of opinion relations iden-
    Page 7, “Experiments”

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iteratively

Appears in 3 sentences as: iteratively (3)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. Moreover, (Qiu et al., 2011) proposed a Double Propagation method to expand sentiment words and opinion targets iteratively , where they also exploited syntactic relations between words.
    Page 3, “Related Work”
  2. The alignment is updated iteratively until no additional inconsistent links can be removed.
    Page 5, “Opinion Target Extraction Methodology”
  3. To estimate the confidence of each opinion target candidate, we employ a random walk algorithm on our graph, which iteratively computes the weighted average of opinion target confidences from neighboring vertices.
    Page 6, “Opinion Target Extraction Methodology”

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syntactic parsing

Appears in 3 sentences as: syntactic parsing (3)
In Syntactic Patterns versus Word Alignment: Extracting Opinion Targets from Online Reviews
  1. To handle this problem, several methods exploited syntactic information, where several heuristic patterns based on syntactic parsing were designed (Popescu and Etzioni, 2005; Qiu et al., 2009; Qiu et al., 2011).
    Page 1, “Introduction”
  2. Without using syntactic parsing , the noises from parsing errors can be effectively avoided.
    Page 2, “Introduction”
  3. As mentioned in (Liu et al., 2013), using PSWAM can not only inherit the advantages of WAM: effectively avoiding noises from syntactic parsing errors when dealing with informal texts, but also can improve the mining performance by using partial supervision.
    Page 2, “Introduction”

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