acl acl2010 acl2010-63 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Shasha Li ; Chin-Yew Lin ; Young-In Song ; Zhoujun Li
Abstract: Comparing one thing with another is a typical part of human decision making process. However, it is not always easy to know what to compare and what are the alternatives. To address this difficulty, we present a novel way to automatically mine comparable entities from comparative questions that users posted online. To ensure high precision and high recall, we develop a weakly-supervised bootstrapping method for comparative question identification and comparable entity extraction by leveraging a large online question archive. The experimental results show our method achieves F1measure of 82.5% in comparative question identification and 83.3% in comparable entity extraction. Both significantly outperform an existing state-of-the-art method. 1
Reference: text
sentIndex sentText sentNum sentScore
1 However, it is not always easy to know what to compare and what are the alternatives. [sent-7, score-0.028]
2 To address this difficulty, we present a novel way to automatically mine comparable entities from comparative questions that users posted online. [sent-8, score-1.135]
3 To ensure high precision and high recall, we develop a weakly-supervised bootstrapping method for comparative question identification and comparable entity extraction by leveraging a large online question archive. [sent-9, score-1.186]
4 The experimental results show our method achieves F1measure of 82. [sent-10, score-0.027]
5 For example, if someone is interested in certain products such as digital cameras, he or she would want to know what the alternatives are and compare different cameras before making a purchase. [sent-15, score-0.203]
6 This type of comparison activity is very common in our daily life but requires high knowledge skill. [sent-16, score-0.059]
7 Magazines such as Consumer Reports and PC Magazine and online media such as CNet. [sent-17, score-0.03]
8 com strive in providing editorial comparison content and surveys to satisfy this need. [sent-18, score-0.078]
9 In the World Wide Web era, a comparison activity typically involves: search for relevant web pages containing information about the targeted products, find competing products, read reviews, and identify pros and cons. [sent-19, score-0.059]
10 In this paper, we focus on finding a set of comparable entities given a user‟s input entity. [sent-20, score-0.176]
11 For example, given an entity, Nokia N95 (a cellphone), we want to find comparable entities such as Nokia N82, iPhone and so on. [sent-21, score-0.205]
12 In general, it is difficult to decide if two entities are comparable or not since people do com- pare apples and oranges for various reasons. [sent-22, score-0.282]
13 For example, “Ford” and “BMW” might be comparable as “car manufacturers” or as “market segments that their products are targeting”, but we rarely see people comparing “Ford Focus” (car model) and “BMW 328i”. [sent-23, score-0.188]
14 Things also get more complicated when an entity has several functionalities. [sent-24, score-0.055]
15 For example, one might compare “iPhone” and “PSP” as “portable game player” while compare “iPhone” and “Nokia N95” as “mobile phone”. [sent-25, score-0.085]
16 Fortunately, plenty of comparative questions are posted online, which provide evidences for what people want to compare, e. [sent-26, score-0.908]
17 In this paper, we define comparative questions and comparators as: Comparative question: A question that intends to compare two or more entities and it has to mention these entities explicitly in the question. [sent-31, score-1.23]
18 Comparator: An entity which is a target of comparison in a comparative question. [sent-32, score-0.698]
19 According to these definitions, Q1 and Q2 be- low are not comparative questions while Q3 is. [sent-33, score-0.709]
20 ” The goal of this work is mining comparators from comparative questions. [sent-38, score-0.914]
21 The results would be very useful in helping users‟ exploration of 650 Proce dinUgsp osfa tlhae, 4S8wthed Aen n,u 1a1l-1 M6e Jeutilnyg 2 o0f1 t0h. [sent-39, score-0.036]
22 c As2s0o1c0ia Atisosnoc foiart Cionom fopru Ctaotmiopnuatla Lti on gaulis Lti cnsg,u piasgtiecs 650–658, alternative choices by suggesting comparable entities based on other users‟ prior requests. [sent-41, score-0.202]
23 To mine comparators from comparative questions, we first have to detect whether a question is comparative or not. [sent-42, score-1.574]
24 According to our definition, a comparative question has to be a question with intent to compare at least two entities. [sent-43, score-0.814]
25 Please note that a question containing at least two entities is not a comparative question if it does not have comparison intent. [sent-44, score-0.878]
26 However, we observe that a question is very likely to be a comparative question if it contains at least two entities. [sent-45, score-0.762]
27 We leverage this insight and develop a weakly supervised bootstrapping method to identify comparative questions and extract comparators simultaneously. [sent-46, score-1.137]
28 To our best knowledge, this is the first attempt to specially address the problem on finding good comparators to support users‟ comparison activity. [sent-47, score-0.298]
29 We are also the first to propose using comparative questions posted online that reflect what users truly care about as the medium from which we mine comparable entities. [sent-48, score-1.052]
30 8% in end-to-end comparative question identification and comparator extraction which outperform the most relevant state-of-the-art method by Jindal & Liu (2006b) significantly. [sent-52, score-1.127]
31 Section 3 presents our weakly-supervised method for comparator mining. [sent-55, score-0.31]
32 1 Related Work Overview In terms of discovering related items for an entity, our work is similar to the research on recommender systems, which recommend items to a user. [sent-58, score-0.189]
33 Recommender systems mainly rely on similarities between items and/or their statistical correlations in user log data (Linden et al. [sent-59, score-0.078]
34 For example, Amazon recommends products to its customers based on their own purchase histories, similar customers‟ purchase histories, and similarity between products. [sent-61, score-0.26]
35 However, recommending an item is not equivalent to finding a comparable item. [sent-62, score-0.158]
36 In the case of Amazon, the purpose of recommendation is to entice their customers to add more items to their shopping carts by suggesting similar or related items. [sent-63, score-0.21]
37 While in the case of comparison, we would like to help users explore alternatives, i. [sent-64, score-0.083]
38 For example, it is reasonable to recommend “iPod speaker” or “iPod batteries” if a user is interested in “iPod”, but we would not compare them with “iPod”. [sent-67, score-0.065]
39 However, items that are comparable with “iPod” such as “iPhone” or “PSP” which were found in comparative questions posted by users are difficult to be predicted simply based on item similarity between them. [sent-68, score-1.066]
40 Although they are all music players, “iPhone” is mainly a mobile phone, and “PSP” is mainly a portable game device. [sent-69, score-0.159]
41 They are similar but also different therefore beg comparison with each other. [sent-70, score-0.029]
42 It is clear that comparator mining and item recommendation are related but not the same. [sent-71, score-0.449]
43 Our work on comparator mining is related to the research on entity and relation extraction in information extraction (Cardie, 1997; Califf and Mooney, 1999; Soderland, 1999; Radev et al. [sent-72, score-0.504]
44 Specifically, the most relevant work is by Jindal and Liu (2006a and 2006b) on mining comparative sentences and relations. [sent-75, score-0.694]
45 Their methods applied class sequential rules (CSR) (Chapter 2, Liu 2006) and label sequential rules (LSR) (Chapter 2, Liu 2006) learned from annotated corpora to identify comparative sentences and extract comparative relations respectively in the news and review domains. [sent-76, score-1.401]
46 The same techniques can be applied to comparative question identification and comparator mining from questions. [sent-77, score-1.112]
47 However, ensuring high recall is crucial in our intended application scenario where users can issue arbitrary queries. [sent-79, score-0.122]
48 To address this problem, we develop a weakly-supervised bootstrapping pattern learning method by effectively leveraging unlabeled questions. [sent-80, score-0.172]
49 Bootstrapping methods have been shown to be very effective in previous information extraction research (Riloff, 1996; Riloff and Jones, 1999; Ravichandran and Hovy, 2002; Mooney and Bunescu, 2005; Kozareva et al. [sent-81, score-0.043]
50 Our work is similar to them in terms of methodology using bootstrapping technique to extract entities with a specific relation. [sent-83, score-0.147]
51 However, our task is different from theirs in that it requires not only extracting entities (comparator extraction) but also ensuring that the entities are extracted from comparative questions (comparative question identification), which is generally not required in IE task. [sent-84, score-0.996]
52 2 Jindal & Liu 2006 In this subsection, we provide a brief summary of the comparative mining method proposed by Jindal and Liu (2006a and 2006b), which is used as baseline for comparison and represents the state-of-the-art in this area. [sent-86, score-0.75]
53 We first introduce the definition of CSR and LSR rule used in their approach, and then describe their comparative mining method. [sent-87, score-0.694]
54 In our problem, C is either comparative or non-comparative. [sent-95, score-0.614]
55 Given a collection of sequences with class information, every CSR is associated to two parameters: support and confidence. [sent-96, score-0.097]
56 Support is the proportion of sequences in the collection containing S as a subsequence. [sent-97, score-0.051]
57 Confidence is the proportion of sequences labeled as C in the sequences containing the S. [sent-98, score-0.102]
58 ) in the input sequence with a designated label (? [sent-117, score-0.07]
59 The anchor in the input sequence could be extracted if its corresponding label in the labeled sequence is what we want (in our case, a comparator). [sent-121, score-0.133]
60 LSRs are also mined from an annotated corpus, therefore each LSR also have two parameters: support and confidence. [sent-122, score-0.023]
61 … … … … Supervised Comparative Mining Method J&L; treated comparative sentence identification as a classification problem and comparative relation extraction as an information extraction problem. [sent-124, score-1.375]
62 They first manually created a set of 83 keywords such as beat, exceed, and outperform that are likely indicators of comparative sentences. [sent-125, score-0.707]
63 These keywords were then used as pivots to create part-of-speech (POS) sequence data. [sent-126, score-0.152]
64 comparative or non-comparative, was used to create sequences and CSRs were mined. [sent-129, score-0.665]
65 The classifier was then used to identify comparative sentences. [sent-131, score-0.614]
66 Given a set of comparative sentences, J&L; manually annotated two comparators with labels $ES 1 and $ES2 and the feature compared with label $FT for each sentence. [sent-132, score-0.87]
67 J&L;‟s method was only applied to noun and pronoun. [sent-133, score-0.027]
68 To differentiate noun and pronoun that are not comparators or features, they added the fourth label $NEF, i. [sent-134, score-0.256]
69 These labels were used as pivots together with special tokens li & rj1 (token position), #start (beginning of a sentence), and #end (end of a sentence) to generate sequence data, sequences with single label only and minimum support greater than 1% are retained, and then LSRs were created. [sent-137, score-0.194]
70 J&L;‟s method have been proved effective in their experimental setups. [sent-139, score-0.027]
71 However, it has the following weaknesses: The performance of J&L;‟s method relies heavily on a set of comparative sentence indicative keywords. [sent-140, score-0.641]
72 These keywords were manually created and they offered no guidelines to select keywords for inclusion. [sent-141, score-0.136]
73 Users can express comparative sentences or questions in many different ways. [sent-143, score-0.709]
74 It is a surprise that their rules achieved high precision but low recall. [sent-147, score-0.023]
75 However, we suspect that their rules might be too specific and overfit their small training set (about 2,600 sentences). [sent-149, score-0.023]
76 We would like to increase recall, avoid overfitting, and allow rules to include discriminative lexical tokens to retain precision. [sent-150, score-0.023]
77 In the next section, we introduce our method to address these shortcomings. [sent-151, score-0.053]
wordName wordTfidf (topN-words)
[('comparative', 0.614), ('comparator', 0.283), ('ipod', 0.283), ('comparators', 0.22), ('iphone', 0.193), ('jindal', 0.188), ('lsrs', 0.188), ('csr', 0.165), ('lsr', 0.157), ('csrs', 0.126), ('questions', 0.095), ('psp', 0.094), ('posted', 0.089), ('comparable', 0.089), ('entities', 0.087), ('users', 0.083), ('mining', 0.08), ('nokia', 0.076), ('liu', 0.075), ('question', 0.074), ('products', 0.071), ('keywords', 0.068), ('customers', 0.067), ('bmw', 0.063), ('zune', 0.063), ('weakly', 0.062), ('identification', 0.061), ('bootstrapping', 0.06), ('entity', 0.055), ('histories', 0.055), ('mine', 0.052), ('items', 0.051), ('sequences', 0.051), ('pivots', 0.05), ('recommender', 0.05), ('token', 0.048), ('purchase', 0.047), ('cameras', 0.045), ('touch', 0.045), ('item', 0.045), ('extraction', 0.043), ('china', 0.041), ('pivot', 0.041), ('recommendation', 0.041), ('ford', 0.039), ('ensuring', 0.039), ('portable', 0.039), ('phone', 0.037), ('mobile', 0.037), ('recommend', 0.037), ('hd', 0.036), ('helping', 0.036), ('mooney', 0.036), ('label', 0.036), ('sequence', 0.034), ('sequential', 0.034), ('beijing', 0.032), ('car', 0.032), ('riloff', 0.031), ('supervised', 0.03), ('amazon', 0.03), ('activity', 0.03), ('leveraging', 0.03), ('online', 0.03), ('alternatives', 0.03), ('want', 0.029), ('game', 0.029), ('develop', 0.029), ('comparison', 0.029), ('marks', 0.029), ('compare', 0.028), ('evidences', 0.028), ('rj', 0.028), ('recommends', 0.028), ('batteries', 0.028), ('oranges', 0.028), ('people', 0.028), ('method', 0.027), ('mainly', 0.027), ('ft', 0.026), ('address', 0.026), ('suggesting', 0.026), ('outperform', 0.025), ('strive', 0.025), ('apples', 0.025), ('califf', 0.025), ('intends', 0.025), ('manufacturers', 0.025), ('pare', 0.025), ('plenty', 0.025), ('shopping', 0.025), ('end', 0.025), ('shasha', 0.024), ('recommending', 0.024), ('camera', 0.024), ('editorial', 0.024), ('intent', 0.024), ('support', 0.023), ('class', 0.023), ('rules', 0.023)]
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Abstract: This paper proposes a convolution forest kernel to effectively explore rich structured features embedded in a packed parse forest. As opposed to the convolution tree kernel, the proposed forest kernel does not have to commit to a single best parse tree, is thus able to explore very large object spaces and much more structured features embedded in a forest. This makes the proposed kernel more robust against parsing errors and data sparseness issues than the convolution tree kernel. The paper presents the formal definition of convolution forest kernel and also illustrates the computing algorithm to fast compute the proposed convolution forest kernel. Experimental results on two NLP applications, relation extraction and semantic role labeling, show that the proposed forest kernel significantly outperforms the baseline of the convolution tree kernel. 1
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