acl acl2010 acl2010-146 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Weiwei Sun
Abstract: Developing features has been shown crucial to advancing the state-of-the-art in Semantic Role Labeling (SRL). To improve Chinese SRL, we propose a set of additional features, some of which are designed to better capture structural information. Our system achieves 93.49 Fmeasure, a significant improvement over the best reported performance 92.0. We are further concerned with the effect of parsing in Chinese SRL. We empirically analyze the two-fold effect, grouping words into constituents and providing syntactic information. We also give some preliminary linguistic explanations.
Reference: text
sentIndex sentText sentNum sentScore
1 wsun @ co l uni i Abstract Developing features has been shown crucial to advancing the state-of-the-art in Semantic Role Labeling (SRL). [sent-2, score-0.185]
2 To improve Chinese SRL, we propose a set of additional features, some of which are designed to better capture structural information. [sent-3, score-0.03]
3 We are further concerned with the effect of parsing in Chinese SRL. [sent-7, score-0.143]
4 We empirically analyze the two-fold effect, grouping words into constituents and providing syntactic information. [sent-8, score-0.212]
5 Similar to English, parsing is a standard pre-processing for Chinese SRL. [sent-11, score-0.097]
6 Many features are extracted to represent constituents in the input parses (Sun and Jurafsky, 2004; Xue, 2008; Ding and Chang, 2008). [sent-12, score-0.263]
7 By using these features, semantic classifiers are trained to predict whether a constituent fills a semantic role. [sent-13, score-0.221]
8 Developing features that capture the right kind of information encoded in the input parses has been shown crucial to advancing the state-of-the-art. [sent-14, score-0.278]
9 Though there has been some work on feature design in Chinese SRL, information encoded in the syntactic trees is not fully exploited and requires more research effort. [sent-15, score-0.058]
10 In this paper, we propose a set of additional ∗The work was partially completed while this author was at Peking University. [sent-16, score-0.027]
11 de features, some of which are designed to better cap- ture structural information of sub-trees in a given parse. [sent-18, score-0.03]
12 0 (Xue, 2008), shows that these features yield a significant improvement of the state-of-the-art. [sent-22, score-0.107]
13 We further analyze the effect of syntactic parsing in Chinese SRL. [sent-23, score-0.242]
14 First, grouping words into constituents, parsing helps to find argument candidates. [sent-25, score-0.236]
15 Second, parsers provide semantic classifiers plenty of syntactic information, not to only recognize arguments from all candidate constituents but also to classify their detailed semantic types. [sent-26, score-0.413]
16 2 Chinese SRL The Chinese PropBank (CPB) is a semantic annotation for the syntactic trees of the Chinese TreeBank (CTB). [sent-29, score-0.125]
17 The arguments of a predicate are labeled with a contiguous sequence of integers, in the form of AN (N is a natural number); the adjuncts are annotated as such with the label AM followed by a secondary tag that represents the semantic classification of the adjunct. [sent-30, score-0.241]
18 The assignment of semantic roles is illustrated in Figure 1, where the predicate is the verb “调查/investigate”. [sent-31, score-0.125]
19 , the NP “事故原 因/the cause of the accident” is labeled as A1, meaning that it is the Patient. [sent-34, score-0.041]
20 In previous research, SRL methods that are successful on English are adopted to resolve Chinese SRL (Sun and Jurafsky, 2004; Xue, 2008; Ding and Chang, 2008, 2009; Sun et al. [sent-35, score-0.037]
21 Xue (2008) produced complete and systematic research on full parsing based methods. [sent-37, score-0.139]
22 Their method divided SRL into three sub-tasks: 1) pruning with a heuristic rule, 2) Argument Identification (AI) to recognize arguments, and 3) Semantic Role Classification (SRC) to predict semantic types. [sent-41, score-0.193]
23 The main two sub-tasks, AI and SRC, are formulated as two classification problems. [sent-42, score-0.029]
24 Ding and Chang (2008) divided SRC into two sub-tasks in sequence: Each argument should first be determined whether it is a core argument or an adjunct, and then be classified into fine-grained categories. [sent-43, score-0.219]
25 However, delicately designed features are more important and our experiments suggest that by using rich features, a better SRC solver can be directly trained without using hierarchical architecture. [sent-44, score-0.107]
26 There are also some attempts at relaxing the necessity of using full syntactic parses, and semantic chunking methods have been introduced by (Sun et al. [sent-45, score-0.167]
27 In the pruning step, our system keeps all constituents (except punctuations) that c-command1 current predicate in focus as argument candidates. [sent-51, score-0.255]
28 In the AI step, a lot of syntactic features are extracted to distinguish argument and non-argument. [sent-52, score-0.259]
29 In other words, a binary classifier is trained to classify each argument candidate as either an argument or not. [sent-53, score-0.22]
30 Finally, a multi-class classifier is trained to label each argument recognized in the former stage with a specific semantic role label. [sent-54, score-0.216]
31 In both AI and SRC, the main job is to select strong syntactic features. [sent-55, score-0.058]
32 3 Features A majority of features used in our system are a combination of features described in (Xue, 2008; Ding and Chang, 2008) as well as the word formation and coarse frame features introduced in (Sun et al. [sent-58, score-0.476]
33 , 2009), the c-command thread features proposed in (Sun et al. [sent-59, score-0.15]
34 We give a brief description of features used in previous work, but explain new features in details. [sent-61, score-0.214]
35 To conveniently illustrate, we denote a candidate constituent ck with a fixed context wi−1[ckwi. [sent-63, score-0.324]
36 wj]wj+1, where wh is the head word of ck, and denote predicate in focus with a context w−v2w−v1wvw+v1w+v2, where is the predicate in f−o2cu−s. [sent-69, score-0.397]
37 1 Baseline Features The following features are introduced in previous Chinese SRL systems. [sent-71, score-0.107]
38 Word content of wh, wi, wj and wi+wj ; POS tag of wh. [sent-73, score-0.183]
39 Head word POS, head word of PP phrases, category of ck’s lift and right siblings, CFG rewrite rule that expands ck and ck’s parent (from (Ding and Chang, 2008)). [sent-76, score-0.567]
40 2 New Word Features We introduce some new features which can be extracted without syntactic structure. [sent-78, score-0.165]
41 They include: Word content of w−v1, w+v1, wi−1 and wj+1 ; POS tag of w−v1, w+v1, w−v2, w+v2, wi−1, wi, wj, wj+1, wi+2 an−d1 wj−2. [sent-80, score-0.036]
42 Word before “LC”: If the POS of wj is “LC” (localizer), we use wj−1 and its POS tag as two new features. [sent-82, score-0.183]
43 NT: Does ck contain a word with POS “NT” (temporal noun)? [sent-83, score-0.292]
44 3 New Syntactic Features Taking complex syntax trees as inputs, the classifiers should characterize their structural properties. [sent-88, score-0.064]
45 We put forward a number of new features to encode the structural information. [sent-89, score-0.137]
46 Category of ck’s parent; head word and POS of head word of parent, left sibling and right sibling of ck. [sent-90, score-0.411]
47 Lexicalized Rewrite rules: Conjuction of rewrite rule and head word of its corresponding RHS. [sent-91, score-0.199]
48 These features of candidate (lrw-c) and its parent (lrw-p) are used. [sent-92, score-0.184]
49 Partial Path: Path from the ck or to the low- 故 wv wv. [sent-94, score-0.647]
50 est common ancestor of ck and One path feature, hence, is divided into left path and right path. [sent-95, score-0.509]
51 Clustered Path: We use the manually created clusters (see (Sun and Sui, 2009)) of categories of all nodes in the path (cpath) and right path. [sent-96, score-0.123]
52 C-commander thread between ck and (cct): (proposed by (Sun et al. [sent-97, score-0.306]
53 Head Trace: The sequential container of the head down upon the phrase (from (Sun and Sui, 2009)). [sent-100, score-0.123]
54 We design two kinds of traces (htr-p, htrw): one uses POS of the head word; the other uses the head word word itself. [sent-101, score-0.33]
55 , the head word of 事故原 因 is “原 因” therefore these feature of this NP are NP↓NN and NP↓原因. [sent-104, score-0.152]
56 The data is divided into three parts: files from 081 to 899 are used as training set; files from 041 to 080 as development set; files from 001 to 040, and 900 to 93 1 as test set. [sent-110, score-0.16]
57 All parsing and SRL experiments use this data setting. [sent-113, score-0.097]
58 To resolve classification problems, we use a linear SVM classifier SVMlin3, along with One-Vs-All approach for multi-class classification. [sent-114, score-0.066]
59 We use gold segmentation and POS as input to the Bikel parser and use it parsing results as input to our SRL system. [sent-116, score-0.129]
60 2 Overall Performance Table 1 summarizes precision, recall and Fmeasure of AI, SRC and the whole task (AI+SRC) of our system respectively. [sent-122, score-0.037]
61 The forth line is the best published SRC performance reported in (Ding and Chang, 2008), and the sixth line is the best SRL performance reported in (Xue, 2008). [sent-123, score-0.108]
62 3 Two-fold Effect of Parsing in SRL The effect of parsing in SRL is two-fold. [sent-131, score-0.143]
63 On the one hand, SRL systems should group words as ar- gument candidates, which are also constituents in a given sentence. [sent-132, score-0.068]
64 As arguments should c-command the predicate, a full parser can further prune a majority of useless constituents. [sent-134, score-0.093]
65 In other words, parsing can effectively supply SRL with argument candidates. [sent-135, score-0.191]
66 Unfortunately, it is very hard to rightly produce full parses for Chinese text. [sent-136, score-0.13]
67 On the other hand, given a constituent, SRL systems should identify whether it is an argument and further predict detailed semantic types if 3http : / /peopl e . [sent-137, score-0.185]
68 html / l 170 TaskParserBracketFeatP(%)R(%)F/A AIBC-i TkBelBG io kled lW W+ S 897287. [sent-145, score-0.028]
69 In the Feat column, W means word features; W+S means word and syntactic feautres. [sent-150, score-0.116]
70 For the two classification problems, parsing can provide complex syntactic information such as path features. [sent-152, score-0.276]
71 1 The Effect of Parsing in AI In AI, full parsing is very important for both grouping words and classification. [sent-155, score-0.184]
72 Line 2 is the AI performance when gold candidate boundaries and word features are used; Line 3 is the performance with additional syntactic features. [sent-157, score-0.258]
73 Line 4 shows the performance by using automatic parses generated by Bikel parser. [sent-158, score-0.088]
74 We can see that: 1) word features only cannot train good classifiers to identify arguments; 2) it is very easy to recognize arguments with good enough syntactic parses; 3) there is a severe performance decline when automatic parses are used. [sent-159, score-0.403]
75 (px()x Tp,a(yby) le, to 3 shows the ten moPst useful features in AI. [sent-164, score-0.137]
76 We can see that the most important features all based on full parsing information. [sent-165, score-0.246]
77 Nine of these top 10 useful features are our new features. [sent-166, score-0.107]
78 2 The Effect of Parsing in SRC The second block in Table 2 summarizes the SRC performance with gold argument boundaries. [sent-171, score-0.163]
79 Line 5 is the accuracy when word features are used; Line 6 is the accuracy when additional syntactic features are added; The last row is the accuracy when syntactic features used are extracted from automatic parses (Bikel+Gold). [sent-172, score-0.583]
80 We can see that different from AI, word features only can train reasonable good semantic classifiers. [sent-173, score-0.203]
81 The comparison between Line 5 and 7 suggests that with parsing errors, automatic parsed syntactic features cause noise to the semantic role classifiers. [sent-174, score-0.425]
82 ti Table 4 shows the ten most useful features in SRC. [sent-178, score-0.137]
83 We can see that two of these ten features are word features (denoted by †). [sent-179, score-0.273]
84 Namely, word faeraetu wreorsd play a more important r)o. [sent-180, score-0.029]
85 Though the other eight features are based on full parsing, four of them (denoted by ‡) use tohne uhellad p wrsoirndg ,w fhoiucrh o can bme (wdeelnlo approximated by word features, according to some language specific properties. [sent-182, score-0.204]
86 The head rules described in (Sun and Jurafsky, 2004) are very popular in Chinese parsing research, such as in (Duan et al. [sent-183, score-0.22]
87 From these head rules, we can see that head words of most phrases in Chinese are located at the first or the last position. [sent-185, score-0.275]
88 12% 5 nodes realize their heads as either their first or last word. [sent-187, score-0.029]
89 Head position suggests that boundary words are good approximation of head word features. [sent-188, score-0.199]
90 If head words have good approximation word features, then it is not strange that the four features denoted by ‡ can be effec- tively represented by w deorndo tfeedat buyre ‡s. [sent-189, score-0.288]
91 Sanim bilear e fwfeitchfeature effect in AI, most of most useful features in SRC are our new features. [sent-190, score-0.153]
92 171 5 Conclusion This paper proposes an additional set of features to improve Chinese SRL. [sent-192, score-0.107]
93 These new features yield a significant improvement over the best published performance. [sent-193, score-0.107]
94 We further analyze the effect of parsing in Chinese SRL, and linguistically explain some phenomena. [sent-194, score-0.184]
95 We found that (1) full syntactic information playes an essential role only in AI and that (2) due to the head word position distribution, SRC is easy to resolve in Chinese SRL. [sent-195, score-0.391]
96 Acknowledgments The author is funded both by German Academic Exchange Service (DAAD) and German Research Center for Artificial Intelligence (DFKI). [sent-196, score-0.027]
97 The author would like to thank the anonymous reviewers for their helpful comments. [sent-197, score-0.027]
98 A distributional analysis of a lexicalized statistical parsing model. [sent-201, score-0.097]
99 Improving Chinese semantic role classification with hierarchical feature selection strategy. [sent-206, score-0.151]
100 Fast semantic role labeling for Chinese based on semantic chunking. [sent-211, score-0.236]
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