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Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic

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Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic Discourse Level Explanatory Relation Extraction from Product Reviews Using First-order Logic Qi Zhang, Jin Qian, Huan Chen, Jihua Kang, Xuanjing Huang School of Computer Science Fudan University Shanghai, P.R. China qz, 12110240030, 12210240054, 12210240059, xjhuang @fudan.edu.cn { } Abstract For example, let us consider the following snip- pets extracted from online reviews: Explanatory sentences are employed to clarify reasons, details, facts, and so on. High quality Example 1: TVs with lower refresh rates may online product reviews usually include not suffer from motion blur. If you’re watching only positive or negative opinions, but also a a fast-paced football game, for example, you variety of explanations of why these opinions were given. These explanations can help may notice a bit of blurring as the players run readers get easily comprehensible informa- around the field. tion of the discussed products and aspect- s. Moreover, explanatory relations can also Example 2: The LED screen is highly reflec- benefit sentiment analysis applications. In tive. The reflection of my own face makes it very this work, we focus on the task of identi- hard to see the subject I am trying to shoot. fying subjective text segments and extracting their corresponding explanations from prod- The first sentence of example 1 expresses negative uct reviews in discourse level. We propose opinion about refresh rate, which is one of the most a novel joint extraction method using first- important attributes of TV. The second sentence order logic to model rich linguistic features describes the consequence of it through an example. and long distance constraints. Experimental In example 2, detail descriptions are used to explain results demonstrate the effectiveness of the the reflection problem of the camera screen. proposed method. Although, explanations provide valuable infor- mation, to the best of our knowledge, there is no 1 Introduction existing work that deals with explanation extraction Through analyzing product reviews with high help- for opinions in discourse level. We think that if fulness ratings assigned by readers, we find that a explanatory relations can be automatically identified large number of explanatory sentences are used to from reviews, sentiment analysis applications may clarify the causes, details, or consequences of opin- benefit from it. Existing opinion mining approaches ions. According to the statistic based on the dataset mainly focus on subjective text. They try to de- we crawled from a popular product review website, termine the subjectivity and polarity of fragments more than 56.1% opinion expressions are further of documents (e.g. a paragraph, a sentence, a explained by other sentences. Since most consumers phrase and a word) (Pang et al., 2002; Riloff et are not experts, these explanations would bring lots al., 2003; Takamura et al., 2005; Mihalcea et al., of helpful and easy comprehension information for 2007; Dasgupta and Ng, ; Hassan and Radev, 2010; them. Suggestions about writing a product review Meng et al., 2012; Dragut et al., 2012). Fine-grained also advise authors to include not only whether they methods were also introduced to extract opinion like or dislike a product, but also why.1 holder, opinion expression, opinion target, and other opinion elements (Kobayashi et al., 2007; Wu et al., 1http://www.reviewpips.com/ http://www.amazon.com/gp/community-help/customer- reviews-guidelines 946 Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 946–957, Seattle, Washington, USA, 18-21 October 2013. c 2013 Association for Computational Linguistics 2011; Xu et al., 2013; Yang and Cardie, 2013). Ma- G = (V, E) is used to represent the subjectivity jor research directions and challenges of sentiment of clauses and explanatory relationships between analysis can also be found in surveys (Pang and Lee, them. In the graph, vertices represent clauses, 2008; Liu, 2012). whose categories are specified by the vertex at- In this work, we aim to identify subjective tex- tributes. Directed edges describe the explanatory t segments and extract their corresponding expla- relationships between them, of which the heads are nations from product reviews in discourse level. explanatory clauses. If clause ca describes a set of We propose to use Markov Logic Networks (ML- facts which clarify the causes, context, situation, or N) (Richardson and Domingos, 2006) to learn the consequences of another clause cb, ca cb is used −→ joint model for subjective classification and explana- to indicate that clause ca explains cb. tory relation extraction. MLN has been applied in Adopting clause unit-based definition is based several natural language processing tasks (Singla on the following reasons: 1) clause is normally and Domingos, 2006; Poon and Domingos, 2008; considered as the smallest grammatical unit which Yoshikawa et al., 2009; Andrzejewski et al., 2011; can express a complete proposition (Kroeger, 2005); Song et al., 2012) and demonstrated its advantages. 2) from analyzing online reviews, we observe that It can easily incorporate rich linguistic features and a clause can express a complete opinion about one global constraints by designing various logic for- aspect in most of cases; 3) in Penn Discourse mulas, which can also be viewed as templates or Treebank, the basic unit of discourse relations (with rules. Logic formulas are combined in a proba- a few exceptions) is also taken to be a clause (Rash- bilistic framework to model soft constraints. Hence, mi Prasad and Webber, 2008). the proposed approach can benefit a lot from this Figure 1(a) illustrates a sample document. Figure framework. 1(b) is the corresponding output of the given docu- To evaluate the proposed method, we crawled a ment. In the graph, vertices whose color are black large number of product reviews and constructed a stand for subjective clauses. The other clauses are labeled corpus through Amazon’s Mechanical Turk. represented by white vertices. Edges describe the Two tasks were deployed for labeling the corpus. explanatory relationships between them, of which We compared the proposed method with state-of- the heads are explanatory clauses. the-art methods on the dataset. Experimental results Although the explanatory relation extraction task demonstrate that the proposed approach can achieve has been studied from the view of linguistic and better performance than state-of-the-art methods. discourse representation by existing works (Carston, The remaining part of this paper is organized as 1993; Lascarides and Asher, 1993), the automatic follows: In Section 2, we define the problem and extraction task is still an open question. Consider the give some examples to show the challenges of this following examples extracting from online reviews: task. Section 3 describes the proposed MLN based Example 3: It takes great pictures. Color ren- method. Dataset construction, experimental results ditions, skin tones, exposure levels are all first rate. and analyses are given in Section 4. In Section 5, we From the example, we can observe that the second present the related work and Section 6 concludes the sentence explains the first one. However, the second paper. sentence itself also expresses opinion on various opinion targets. In other words, both subjective and 2 Problem Statement objective sentences can be used as explanations. Example 4: When we called their service center Motivated by the argument structure of discourse they made us wait for them the whole day and no relations used in Penn Discourse Treebank (Rash- one turned up. This level of service is simply not mi Prasad and Webber, 2008), in this work, we acceptable. The first sentence in example 4 explains adopt the clause unit-based definition. It means that the second one. Hence, the feature of relative clauses are treated as the basic units of opinion ex- location between two sentences does not always pressions and explanations. Let d = c1, c2, ...cn work well in all cases. be the clauses of document d. Directed{ graph} Example 5: This backpack is great! its very big 947 (c1) I have both the Panasonic LX3 and the Canon C 1 S90. (c2) Both cameras are quite different but truly C 5 C 2 excellent. (c3) The S90 is a true pocket camera. C (c4) It is very compact. (c5) The build quality is C 6 3 also top notch. (c6) It feels solid and it is easy to C 7 C 4 grip. (c7) It is so small and convenient, (c8) you C 8 will find that you will always carry it with you. (a) Example Review (b) Directed Graph Representation Figure 1: Directed graph representation of a sample document. and fits more than enough stuff. Many sentences, formula) in MLN. which express explanatory relation, do not contain We use to represent a MLN and (ϕi, wi) M { } any connectives (e.g. “because”, “the reason is”, to represent formula ϕi and its weight wi. These and so on). Lin et al.(2009) generalized four chal- weighted formulas define a probability distribution lenges (include ambiguity, inference, context, and over sets of possible worlds. Let y denote a possible world knowledge) to automated implicit discourse world, the p(y) is defined as follows (Richardson relation recognition. In this task, we also need to and Domingos, 2006): address those challenges. From the these examples, we can observe that ex- 1 ϕi tracting explanatory relations from product reviews p(y) = exp wi fc (y) , Z n is a challenging task. Both linguistic and global (ϕi,wi) c C ϕi ∑∈M ∈∑ constraints should be carefully studied. where each c is a binding of free variable in ϕi to 3 The Proposed Approach ϕi constraints; fc (y) is a binary feature function that In this section, we present our method for jointly returns 1 if the true value is obtained in the ground classifying the subjectivity of text segments and formula we get by replacing the free variables in extracting explanatory relations.
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