acl acl2013 acl2013-208 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Guangyou Zhou ; Jun Zhao
Abstract: This paper is concerned with the problem of heterogeneous dependency parsing. In this paper, we present a novel joint inference scheme, which is able to leverage the consensus information between heterogeneous treebanks in the parsing phase. Different from stacked learning methods (Nivre and McDonald, 2008; Martins et al., 2008), which process the dependency parsing in a pipelined way (e.g., a second level uses the first level outputs), in our method, multiple dependency parsing models are coordinated to exchange consensus information. We conduct experiments on Chinese Dependency Treebank (CDT) and Penn Chinese Treebank (CTB), experimental results show that joint infer- ence can bring significant improvements to all state-of-the-art dependency parsers.
Reference: text
sentIndex sentText sentNum sentScore
1 Joint Inference for Heterogeneous Dependency Parsing Guangyou Zhou and Jun Zhao National Laboratory of Pattern Recognition Institute of Automation, Chinese Academy of Sciences 95 Zhongguancun East Road, Beijing 100190, China {gy zhou , j zhao} @ nlpr . [sent-1, score-0.132]
2 cn a Abstract This paper is concerned with the problem of heterogeneous dependency parsing. [sent-4, score-0.466]
3 In this paper, we present a novel joint inference scheme, which is able to leverage the consensus information between heterogeneous treebanks in the parsing phase. [sent-5, score-1.108]
4 Different from stacked learning methods (Nivre and McDonald, 2008; Martins et al. [sent-6, score-0.063]
5 , 2008), which process the dependency parsing in a pipelined way (e. [sent-7, score-0.482]
6 , a second level uses the first level outputs), in our method, multiple dependency parsing models are coordinated to exchange consensus information. [sent-9, score-0.735]
7 We conduct experiments on Chinese Dependency Treebank (CDT) and Penn Chinese Treebank (CTB), experimental results show that joint infer- ence can bring significant improvements to all state-of-the-art dependency parsers. [sent-10, score-0.46]
8 Over the past few years, supervised learning methods have obtained state-of-the-art performance for dependency parsing (Yamada and Matsumoto, 2003; McDonald et al. [sent-13, score-0.412]
9 These methods usually rely heavily on the manually annotated treebanks for training the dependency models. [sent-18, score-0.466]
10 (Hongkong ) ns Figure 1: Different grammar formalisms of syntactic structures between CTB (upper) and CDT (below). [sent-33, score-0.132]
11 CTB is converted into dependency grammar based on the head rules of (Zhang and Clark, 2008). [sent-34, score-0.322]
12 tic structure, either phrase-based or dependencybased, is both time consuming and labor intensive. [sent-35, score-0.068]
13 Making full use ofthe existing manually annotated treebanks would yield substantial savings in dataannotation costs. [sent-36, score-0.225]
14 In this paper, we present a joint inference scheme for heterogenous dependency parsing. [sent-37, score-0.847]
15 This scheme is able to leverage consensus information between heterogenous treebanks during the inference phase instead of using individual output in a pipelined way, such as stacked learning methods (Nivre and McDonald, 2008; Martins et al. [sent-38, score-1.031]
16 The basic idea is very simple: although heterogenous treebanks have different grammar formalisms, they share some consensus information in dependency structures for the same sen- tence. [sent-40, score-0.959]
17 For example in Figure 1, the dependency structures actually share some partial agreements for the same sentence, the two words “eyes” and “Hongkong” depend on “cast” in both Chinese Dependency Treebank (CDT) (Liu et al. [sent-41, score-0.383]
18 Therefore, we would like to train the dependency parsers on individual heterogenous treebank and jointly parse the same sentences with consensus information exchanged between them. [sent-44, score-1.013]
19 c A2s0s1o3ci Aatsiosonc fioartio Cno fmorpu Ctoamtiopnuatalt Lioin gauli Lsitnicgsu,i psatgices 104–109, Figure2:GnaJloiTjtrePanbsfiercn1oktcseniutfosermdationexchTaPrnge sbarcnk2hemofterogeneous dependency parsing. [sent-47, score-0.286]
20 Section 2 gives a formal description of the joint inference for heterogeneous dependency parsing. [sent-49, score-0.787]
21 2 Our Approach The general joint inference scheme of heterogeneous dependency parsing is shown in Figure 2. [sent-52, score-0.992]
22 Here, heterogeneous treebanks refer to two Chinese treebanks: CTB and CDT, therefore we have only two parsers, but the framework is generic enough to integrate more parsers. [sent-53, score-0.36]
23 For easy explanation of the joint inference scheme, we regard a parser without consensus information as a baseline parser, a parser incorporates consensus information called a joint parser. [sent-54, score-1.291]
24 Joint inference provides a framework that accommodates and coordinates multiple dependency parsing models. [sent-55, score-0.628]
25 (2010), the joint inference for heterogeneous dependency parsing consists of four components: (1) Joint In- ference Model; (2) Parser Coordination; (3) Joint Inference Features; (4) Parameter Estimation. [sent-58, score-0.949]
26 1 Joint Inference Model For a given sentence x, a joint dependency parsing model finds the best dependency parsing tree y∗ among the set of possible candidate parses Y(x) baamsoedng on a scoring fousnscibtiloen c Fs: y∗ = aryg∈Ym(xa)xFs(x,y) (1) Following (Li et al. [sent-60, score-1.009]
27 , 2009), we will use dk to denote the kth joint parser, and also use the notation Hk (x) for a list of parse candidates of sentence x determined by dk. [sent-61, score-0.625]
28 Feature index lranges over all consensusbased features in equation (3). [sent-63, score-0.046]
29 2 Parser Coordination Note that in equation (2), though the baseline score function Ps (x, y) can be computed individually, the case of Ψk (y, Hk (x)) is more complicated. [sent-65, score-0.082]
30 It is not feasible yto, Henumerate all parse candidates for dependency parsing. [sent-66, score-0.466]
31 The basic idea is that we can use baseline models’ nbest output as seeds, and iteratively refine joint models’ n-best output with joint inference. [sent-68, score-0.388]
32 The joint inference process is shown in Algorithm 1. [sent-69, score-0.321]
33 a tFei risnt, H Hwe extract bigram-subtrees tahnadt sctoonrtea tihne tmw ion wHords. [sent-71, score-0.085]
34 If two words have a dependency relation, we add these two words as a subtree into Hk′ (x). [sent-72, score-0.286]
35 rSeimlatiiloarnl,y, w we ea cda tnh eesxetra twcto t wriogrradms- assu abt sruebestr. [sent-73, score-0.084]
36 Besides, we also store the “ROOT” word of each candidate in Hk′ (x) ; Step3: Use joint parsers to re-parse the sentence x with the baseline features and joint inference features (see subsection 2. [sent-75, score-0.684]
37 For joint parser dk, consensus-based features of any dependency parsing candidate are computed based on current setting of Hs′ (x) for all s but k. [sent-77, score-0.73]
38 New depenodenn ccyur preanrtsin segt tcianngd oidfa Htes generated by dk in re-parsing are cached in H′k′ (x); Step4: Update all Hk (x) with H′k′ (x) ; Step5: Iterate from Step2 to Step4 until a preset iteration limit is reached. [sent-78, score-0.401]
39 In Algorithm 1, dependency parsing candidates of different parsers can be mutually improved. [sent-79, score-0.619]
40 For example, given two parsers d1 and d2 with candidates H1 and H2, improvements on H1 enable d2 dtoa improve H2, a,n imd H1 ebmeneenftists o fnro Hm improved H2, manprdo so on. [sent-80, score-0.256]
41 We can see that a joint parser does not enlarge the search space of its baseline model, the only change is parse scoring. [sent-81, score-0.429]
42 By running a complete inference process, joint model can be applied to re-parsing all candidates explored by a parser. [sent-82, score-0.42]
43 105 Thus Step3 can be viewed as full-scale candidates reranking because the reranking scope is beyond the limited n-best output currently cached in Hk. [sent-83, score-0.282]
44 3 Joint Inference Features In this section we introduce the consensus-based feature functions fk,l (y, Hk (x)) introduced in equation (3). [sent-85, score-0.046]
45 The formu(lya,tiHon can be written as: fk,l(y,Hk(x)) =y′∈∑Hk(x)P(y′|dk)Il(y,y′) (4) where y is a dependency parse of x by using parser ds (s k), y′ is a dependency parse in Hk (x) and( P(y′ |dk) iys tihse posterior probability ionf dependency parse y′ parsed by parser dk given sentence x. [sent-86, score-1.683]
46 Il(y, y′) is a consensus measure defined on y and y′ using different feature functions. [sent-87, score-0.262]
47 Dependency parsing model P(y′ |dk) can be predicted by using the global lin|edar models (GLMs) (e. [sent-88, score-0.126]
48 Each headmodifier dependency (denoted as “edge”) is a tup∑le t =< h, m, h → m >, so Iedge(y, y′) = ∑t∈y δ(t,y′). [sent-93, score-0.286]
49 (2) sibling dependencies: Each sibling dependency (denoted as “sib”) is a tuple t =< i∑, h, m, h ← i → m >, so Isib(y, y′) = ∑t∈y δ(t,y′). [sent-94, score-0.458]
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