acl acl2013 acl2013-387 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jong-Hoon Oh ; Kentaro Torisawa ; Chikara Hashimoto ; Motoki Sano ; Stijn De Saeger ; Kiyonori Ohtake
Abstract: In this paper, we explore the utility of intra- and inter-sentential causal relations between terms or clauses as evidence for answering why-questions. To the best of our knowledge, this is the first work that uses both intra- and inter-sentential causal relations for why-QA. We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. (2012). By applying these ideas to Japanese why-QA, we improved precision by 4.4% against all the questions in our test set over the current state-of-theart system for Japanese why-QA. In addi- tion, unlike the state-of-the-art system, our system could achieve very high precision (83.2%) for 25% of all the questions in the test set by restricting its output to the confident answers only.
Reference: text
sentIndex sentText sentNum sentScore
1 j p l j Abstract In this paper, we explore the utility of intra- and inter-sentential causal relations between terms or clauses as evidence for answering why-questions. [sent-4, score-0.931]
2 To the best of our knowledge, this is the first work that uses both intra- and inter-sentential causal relations for why-QA. [sent-5, score-0.895]
3 We also propose a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation proposed by Hashimoto et al. [sent-6, score-1.243]
4 1 Introduction “Why-question answering” (why-QA) is a task to retrieve answers from a given text archive for a why-question, such as “Why are tsunamis generated? [sent-12, score-0.326]
5 Consider the sentence A1 in Table 1, which represents the causal relation between the cause, “the ocean’s water mass . [sent-20, score-0.98]
6 Cause and effect parts of each causal relation, marked with [. [sent-24, score-0.881]
7 ” This is a good answer to the question, “Why are tsunamis generated? [sent-33, score-0.384]
8 Our method finds text fragments that include such causal relations with an effect part that resembles a given question and provides them as answers. [sent-35, score-0.988]
9 Some methods utilized the causal relations between terms as evidence for finding answers (i. [sent-38, score-0.973]
10 , matching a cause term with an answer text and its effect term with a question) (Girju, 2003; Higashinaka and Isozaki, 2008). [sent-40, score-0.357]
11 , a text fragment that may be provided as an answer, explicitly contains a complex causal relation sen1733 Proce dingsS o f ita h,e B 5u1lgsta Arinan,u Aaulg Musete 4ti-n9g 2 o0f1 t3h. [sent-45, score-0.93]
12 For example, A5 in Table 1is an incorrect answer to “Why are tsunamis generated? [sent-48, score-0.384]
13 The first challenge is to accurately identify a wide range of causal relations like those in Table 1 in answer candidates. [sent-54, score-1.031]
14 To meet this challenge, we developed a sequence labeling method that identifies not only intra-sentential causal relations, i. [sent-55, score-0.812]
15 , the causal relations between two terms/phrases/clauses expressed in a single sentence (e. [sent-57, score-0.895]
16 , A1 in Table 1), but also the intersentential causal relations, which are the causal relations between two terms/phrases/clauses expressed in two adjacent sentences (e. [sent-59, score-1.707]
17 The second challenge is assessing the appropriateness of each identified causal relation as an an- swer to a given question. [sent-62, score-1.007]
18 This is important since the causal relations identified in the answer candidates may have nothing to do with a given question. [sent-63, score-1.13]
19 In this case, we have to reject these causal relations because they are inappropriate as an answer to the question. [sent-64, score-1.031]
20 When a single answer candidate contains many causal relations, we also have to select the appropriate ones. [sent-65, score-1.037]
21 Those in A1–A3 are appropriate answers to “Why are tsunamis generated? [sent-67, score-0.37]
22 When we made our system provide only its confident answers according to their confidence score given by our system, the precision of these confident answers was 83. [sent-84, score-0.297]
23 2 Related Work Although there were many previous works on the acquisition of intra- and inter-sentential causal relations from texts (Khoo et al. [sent-89, score-0.895]
24 , 2012), their application to why-QA was limited to causal relations between terms (Girju, 2003; Higashinaka and Isozaki, 2008). [sent-95, score-0.895]
25 , 2012), and causal relations between terms (Girju, 2003; Higashinaka and Isozaki, 2008) has been used. [sent-99, score-0.895]
26 On the other hand, our method explicitly identifies intra- and inter-sentential causal relations between terms/phrases/clauses that have complex struc- tures and uses the identified relations to answer a why-question. [sent-101, score-1.133]
27 We extended our previous work by introducing causal relations recognized from answer candidates to the answer re-ranking. [sent-111, score-1.247]
28 The top ranked passages are regarded as answer candidates in the answer re-ranking. [sent-120, score-0.371]
29 In this work, we propose causal relation features generated from intra- and inter-sentential causal rela- tions in answer candidates and use them along with the features proposed in our previous work for training our re-ranker. [sent-126, score-2.031]
30 4 Causal Relations for Why-QA We describe causal relation recognition in Section 4. [sent-127, score-0.949]
31 1and describe the features (of our re-ranker) generated from causal relations in Section 4. [sent-128, score-0.935]
32 1 Causal Relation Recognition We restrict causal relations to those expressed by such cue phrases for causality as (the Japanese counterparts of) because and as a result like in the previous work (Khoo et al. [sent-131, score-1.033]
33 , 2000; Inui and Okumura, 2005) and recognize them in the following two steps: extracting causal relation candidates and recognizing causal relations from these candidates. [sent-132, score-1.924]
34 1 Extracting Causal Relation Candidates We identify cue phrases for causality in answer candidates using the regular expressions in Table 2. [sent-135, score-0.354]
35 Then, for each identified cue phrase, we extract three sentences as a causal relation candi- date, where one contains the cue phrase and the other two are the previous and next sentences in the answer candidates. [sent-136, score-1.229]
36 When there is more than one cue phrase in an answer candidate, we use all of them for extracting the causal relation candidates, assuming that each of the cue phrases is linked to different causal relations. [sent-137, score-2.022]
37 We call a cue phrase used for extracting a causal relation candidate a c-marker (causality marker) of the candidate to distinguish it from the other cue phrases in the same causal relation candidate. [sent-138, score-2.094]
38 2 Recognizing Causal Relations Next, we recognize the spans of the cause and effect parts of a causal relation linked to a c-marker. [sent-148, score-1.095]
39 In our task, CRFs take three sentences of a causal relation candidate as input and generate their cause-effect annotations with a set of possible cause-effect IOB labels, including BeginCause (B-C), Inside-Cause (I-C), Begin-Effect (BE), Inside-Effect (I-E), and Outside (O). [sent-151, score-0.975]
40 We used the three types offeature sets in Table 3 for training the CRFs, where j is in the range of i−4 ≤ j ≤ i+ 4 for current position iin a causal rie−la4tio ≤n cja ≤nd iid+at4e. [sent-154, score-0.812]
41 Figure 2: Recognizing causal relations by sequence labeling: Underlined text This causes represents a c-marker, and EOS and EOA represent end-of-sentence and end-of-answer candidates. [sent-156, score-0.937]
42 Syntactic features: The span of the causal relations in a given causal relation candidate strongly depends on the c-marker in the candidate. [sent-159, score-1.87]
43 Especially for intra-sentential causal relations, their cause and effect parts often appear in the subtrees of the c-marker’s node or those of the c-marker’s parent node in a syntactic dependency tree struc- ture. [sent-160, score-1.071]
44 , 2012) to assess the appropriateness ofeach causal relation obtained by our causal relation recognizer as an answer to a given question. [sent-185, score-2.037]
45 Finding answers with term matching and partial tree matching has been used in the literature of question answering (Girju, 2003; Narayanan and Harabagiu, 2004; Moschitti et al. [sent-186, score-0.313]
46 Each feature type expresses the causal relations in an answer candidate that are determined to be appropriate as answers to a given question by term matching (tf1–tf4), partial tree matching (pf1– pf4) and excitation polarity matching (ef1–ef4). [sent-193, score-1.639]
47 We call these causal relations used for generating our causal relation features candidates of an appropriate causal relation in this section. [sent-194, score-2.896]
48 Note that if one answer candidate has more than one candidate of an appropriate causal relation found by one matching method, we generated features for each appropriate candidate and merged all of them for the answer candidate. [sent-195, score-1.502]
49 1 Term Matching Our term matching method judges that a causal relation is a candidate of an appropriate causal relation if its effect part contains at least one content word (nouns, verbs, and adjectives) in the question. [sent-206, score-2.071]
50 For example, all the causal relations of A1– A4 in Table 1 are candidates of an appropriate causal relation to the question, “Why is a tsunami generated? [sent-207, score-1.975]
51 tf1–tf4 are generated from candidates of an appropriate causal relation identified by term matching. [sent-209, score-1.116]
52 For example, word 3-gram “this/cause/QW” is extracted from This causes tsunamis in A2 for “Why is a tsunami generated? [sent-212, score-0.292]
53 tf3 is a binary feature that indicates the existence of candidates of an appropriate causal relation identified by term matching in an answer candidate. [sent-218, score-1.266]
54 tf4 represents the degree of the relevance of the candidates of an appropriate causal relation measured by the number of matched terms: one, two, and more than two. [sent-219, score-1.078]
55 2 Partial Tree Matching Our partial tree matching method judges a causal relation as a candidate of an appropriate causal relation if its effect part contains at least one partial tree in a question, where the partial tree covers more than one content word. [sent-222, score-2.231]
56 For example, only the causal relation A1 among A1–A4 is a candidate of an appropriate causal relation for question “Why are tsunamis generated? [sent-223, score-2.242]
57 pf1–pf4 are generated from candidates of an appropriate causal relation identified by the partial tree matching. [sent-225, score-1.156]
58 r pf3 aisc a binary feature that indicates whether an answer candidate contains candidates of an appropriate causal relation identified by partial tree matching. [sent-229, score-1.314]
59 pf4 rep- resents the degree of the relevance of the candidate of an appropriate causal relation measured by the number of matched partial trees: one, two, and more than two. [sent-230, score-1.057]
60 This consistency suggests that A1 is a good answer to question “Why are tsunamis caused? [sent-243, score-0.429]
61 This suggests that A4 is not a good answer to “Why are tsunamis caused? [sent-246, score-0.384]
62 Next, we assume that a causal relation is ap- propriate as an answer to a question if the effect part of the causal relation and the question share at least one common noun with the same polarity. [sent-255, score-2.134]
63 More detailed information concerning the configurations of all the nouns in all the candidates of an appropriate causal relation (including their cause parts) and the question are encoded into our feature set ef1–ef4 in Table 4 and the final judgment is done by our re-ranker. [sent-256, score-1.232]
64 ef1 indicates whether each type of noun-polarity pair exists in a causal relation. [sent-258, score-0.812]
65 In other words, ef2 indicates whether each type of noun-polarity pair exists in the causal relation for each word class. [sent-261, score-0.93]
66 ef3 indicates the existence of candidates of an appropriate causal relation identified by this matching scheme, and ef4 represents the number of noun-polarity pairs shared by the question and the candidates of an appropriate causal relations (one, two, and more than two). [sent-262, score-2.198]
67 5 Experiments We experimented with causal relation recognition and why-QA with our causal relation features. [sent-263, score-1.879]
68 This why-QA data set is composed of 850 Japanese why-questions and their top-20 answer candidates obtained by answer candidate extraction from 600 million Japanese web pages. [sent-267, score-0.413]
69 2 Data Set for Causal Relation Recognition We built a data set composed of manually annotated causal relations for evaluating our causal relation recognition. [sent-279, score-1.841]
70 Finally, we had a data set made of 16,051 causal relation candidates, 8,117 of which had a true causal relation; the number of intra- and inter-sentential causal relations were 7,120 and 997, respectively. [sent-282, score-2.637]
71 We performed 10-fold cross validation to evaluate our causal relation recognition with this 10-fold data. [sent-284, score-0.949]
72 3 Causal Relation Recognition We used CRF++5 for training our causal relation recognizer. [sent-286, score-0.93]
73 com/p/crfpp/ 1739 the result for our baseline system that recognizes a causal relation by simply taking the two phrases adjacent to a c-marker (i. [sent-295, score-0.93]
74 , before and after) as cause and effect parts of the causal relation. [sent-297, score-0.977]
75 In other words, we judged that a causal relation recognized by BASELINE is correct ifboth cause and effect parts in the gold standard are adjacent to a c-marker. [sent-299, score-1.095]
76 INTRA-SENT and INTER-SENT represent the results for intra- and inter-sentential causal relations and ALL represents the result for the both causal relations by our method. [sent-300, score-1.814]
77 From these results, we confirmed that our method recognized both intra- and inter-sentential causal relations with over 80% precision, and it significantly outperformed our baseline system in both precision and recall rates. [sent-301, score-0.956]
78 l091ation recognition (%) We also investigated the contribution of the three types of features used in our causal relation recognition to the performance. [sent-305, score-0.985]
79 We used the causal relations obtained from the 10-fold cross validation for our why-QA experiments. [sent-309, score-0.895]
80 4 Why-Question Answering We performed why-QA experiments to confirm the effectiveness of intra- and inter-sentential causal relations in a why-QA task. [sent-311, score-0.895]
81 OURCF uses a re-ranker trained with only our causal relation features. [sent-314, score-0.93]
82 OH+PREVCF is a system with a re-ranker trained with the features used in OH and with the causal relation feature proposed in Higashinaka and Isozaki (2008). [sent-317, score-0.963]
83 The causal relation feature includes an indicator that determines whether the causal relations between two terms appear in a question-answer pair; cause in an answer and its effect in a question. [sent-318, score-2.105]
84 We acquired the causal relation instances (between terms) from 600 million Japanese web pages using the method of De Saeger et al. [sent-319, score-0.93]
85 (2009) and exploited the top-100,000 causal relation instances in this system. [sent-320, score-0.93]
86 PROPOSED has a re-ranker trained with our causal relation features as well as the three types of features proposed in Oh et al. [sent-321, score-0.98]
87 Comparison between OH and PROPOSED reveals the contribution of our causal relation features to why-QA. [sent-323, score-0.947]
88 Although this suggests the effectiveness of our causal relation features, the overall performance of OURCF was lower than that of OH. [sent-339, score-0.93]
89 % of questions Figure 4: Effect of causal relation features on the top-answers We also compared confident answers of OURCF, OH, and PROPOSED by making each system provide only the k confident top-answers (for k questions) selected by their SVM scores given by each system’s re-ranker. [sent-342, score-1.197]
90 This experiment confirmed that our causal relation features were also effective in improving the quality of the highly confident answers. [sent-349, score-1.025]
91 We think that one of the reasons is the relatively small coverage of the excitation polarity lexicon, a core resource in our excitation polarity matching. [sent-352, score-0.41]
92 Next, we investigated the contribution of the intra- and inter-sentential causal relations to the performance of our method. [sent-354, score-0.895]
93 We used only one of the two types of causal relations for generating causal relation features (INTRA-SENT and INTERSENT) for training our re-ranker and compared the results in these settings with the one when both were used (ALL (PROPOSED)). [sent-355, score-1.842]
94 Both intra- and inter-sentential causal relations contributed to the performance improvement. [sent-357, score-0.895]
95 075P Table 8: Results with/without sentential causal relations (%) intra- and inter- We also investigated the contributions of the three types of causal relation features by ablation tests (Table 9). [sent-360, score-1.842]
96 7905yP-QA(%) 6 Conclusion In this paper, we explored the utility of intra- and inter-sentential causal relations for ranking answer candidates to why-questions. [sent-365, score-1.111]
97 We also proposed a method for assessing the appropriateness of causal relations as answers to a given question using the semantic orientation of excitation. [sent-366, score-1.1]
98 2% precision for its confident answers, when it only provided its confident answers for 25% of all the questions in our test set. [sent-371, score-0.275]
99 Investigating the characteristics of causal relations in Japanese text. [sent-430, score-0.895]
100 Extracting causal knowledge from a medical database using graphical patterns. [sent-440, score-0.812]
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