acl acl2010 acl2010-238 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Danilo Croce ; Cristina Giannone ; Paolo Annesi ; Roberto Basili
Abstract: Current Semantic Role Labeling technologies are based on inductive algorithms trained over large scale repositories of annotated examples. Frame-based systems currently make use of the FrameNet database but fail to show suitable generalization capabilities in out-of-domain scenarios. In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects. The large scale empirical study here discussed confirms that state-of-art accuracy can be obtained for out-of-domain evaluations.
Reference: text
sentIndex sentText sentNum sentScore
1 In this paper, a state-of-art system for frame-based SRL is extended through the encapsulation of a distributional model of semantic similarity. [sent-6, score-0.244]
2 The resulting argument classification model promotes a simpler feature space that limits the potential overfitting effects. [sent-7, score-0.311]
3 Building upon the so called frame semantic model (Fillmore, 1985), the Berkeley FrameNet project has developed a semantic lexicon for the core vocabulary of English, since 1997. [sent-11, score-0.364]
4 A frame is evoked in texts through the occurrence of its lexical units (LU), i. [sent-12, score-0.202]
5 predicate words such verbs, nouns, or adjectives, and specifies the participants and properties of the situation it describes, the so called frame elements (FEs). [sent-14, score-0.13]
6 frame elements) as they are grammatically realized in input sentences. [sent-17, score-0.13]
7 Some critical limitations have been outlined in literature, some of them independent from the underlying semantic paradigm. [sent-25, score-0.132]
8 More recently, the stateof-art frame-based semantic role labeling system discussed in (Johansson and Nugues, 2008b) re- ports a 19% drop in accuracy for the argument classification task when a different test domain is targeted (i. [sent-50, score-0.629]
9 Out-of-domain tests seem to suggest the models trained on BNC do not generalize well to novel grammatical and lexical phenomena. [sent-53, score-0.166]
10 , 2008), the major drawback is the poor generalization power affecting lexical features. [sent-55, score-0.144]
11 The above problems are particularly critical for frame-based shallow semantic parsing where, as opposed to more syntactic-oriented semantic labeling schemes (as Propbank (Palmer et al. [sent-57, score-0.258]
12 9% for the accuracy of the argument identification task is reported, it is due to the complexity in projecting frame element boundaries out from the depen- dency graph: more than 16% of the roles in the annotated material lack of a clear grammatical status. [sent-60, score-0.531]
13 A semi-supervised statistical model exploiting useful lexical information from unlabeled corpora is proposed. [sent-72, score-0.159]
14 Moreover, it generalizes lexical information about the annotated examples by applying a ge- ometrical model, in a Latent Semantic Analysis style, inspired by a distributional paradigm (Pado and Lapata, 2007). [sent-74, score-0.232]
15 As we will see, the accuracy reachable through a restricted feature space is still quite close to the state-of-art, but interestingly the performance drops in out-of-domain tests are avoided. [sent-75, score-0.132]
16 In the following, after discussing existing approaches to SRL (Section 2), a distributional approach is defined in Section 3. [sent-76, score-0.11]
17 2 discusses the proposed HMM-based treatment of joint inferences in argument classification. [sent-78, score-0.175]
18 SRL proceeds through two main steps: the localization of arguments in a sentence, called boundary detection (BD), and the as- signment of the proper role to the detected constituents, that is the argument classification, (AC) step. [sent-86, score-0.348]
19 , 2008) a SRL model over Propbank that effectively exploits the semantic argument frame as a joint structure, is presented. [sent-88, score-0.439]
20 It incorporates strong dependencies within a comprehensive statistical joint model with a rich set of features over multiple argument phrases. [sent-89, score-0.209]
21 First local models are applied to produce role labels over individual arguments, then the joint model is used to decide the entire argument sequence among the set of the n-best competing solutions. [sent-95, score-0.39]
22 In (Johansson and Nugues, 2008b) the impact of different grammatical representations on the task of frame-based shallow semantic parsing is studied and the poor lexical generalization problem is outlined. [sent-97, score-0.344]
23 The argument classification 238 component is thus shown to be heavily domaindependent whereas the inclusion of grammatical function features is just able to mitigate this sensitivity. [sent-105, score-0.298]
24 , 2008), it is suggested that lexical features are domain specific and their suitable generalization is not achieved. [sent-107, score-0.144]
25 The lack of suitable lexical information is also discussed in (F ¨urstenau and Lapata, 2009) through an approach aiming to support the creation of novel annotated resources. [sent-108, score-0.161]
26 1 The role of Lexical Semantic Information It is widely accepted that lexical information (as features directly derived from word forms) is crucial for training accurate systems in a number of NLP tasks. [sent-112, score-0.171]
27 In (Goldberg and Elhadad, 2009), a different strategy to incorporate lexical features into classification models is proposed. [sent-117, score-0.134]
28 The suggested perspective here is that effective semantic knowledge can be collected from sources exter- nal to the annotated corpora (very large unannotated corpora or on manually constructed lexical resources) rather than learned from the raw lexical counts of the annotated corpus. [sent-121, score-0.344]
29 In particular, (Collobert and Weston, 2008) proposes an embedding of lexical information using Wikipedia as source, and exploiting the resulting language model within the multitask learning process. [sent-125, score-0.149]
30 The idea of (Collobert and Weston, 2008) to obtain an embedding of lexical information by acquiring a language model from unlabeled data is an interesting approach to the problem of performance degradation in out-of-domain tests, as already pursued by (Deschacht and Moens, 2009). [sent-126, score-0.159]
31 The extensive use of unlabeled texts allows to achieve a significant level of lexical generalization that seems better capitalize the smaller annotated data sets. [sent-127, score-0.328]
32 3 A Distributional Model for Argument Classification High quality lexical information is crucial for robust open-domain SRL, as semantic generalization highly depends on lexical information. [sent-128, score-0.316]
33 SPEAKER (1) MEDIUM (2) In sentence (1), for example, President Kennedy is the grammatical subject of the verb say and this justifies its role of SPEAKER. [sent-132, score-0.16]
34 However, syntax does not entirely characterize argument seman- tics. [sent-133, score-0.175]
35 Second, we improve the lexical semantic information available to the learning algorithm. [sent-140, score-0.172]
36 The proposed ”minimalistic” approach will consider only two independent features: • the semantic head (h) of a role, as it can bthee eo sbesmeravnetdic cin h tehaed grammatical set,ru acstu irte. [sent-141, score-0.198]
37 t nIng (2), the semantic head report is connected to the LU stated through the subject (SBJ) relation. [sent-144, score-0.137]
38 In the rest ofthe section the distributional model for the argument classification step is presented. [sent-145, score-0.381]
39 A lexicalized model for individual semantic roles is first defined in order to achieve robust semantic classification local to each argument. [sent-146, score-0.433]
40 Then a Hidden Markov Model is introduced in order to exploit the local probability estimators, sensitive to lexical similarity, as well as the global information on the entire argument sequence. [sent-147, score-0.329]
41 1 Distributional Local Models As the classification of semantic roles is strictly related to the lexical meaning of argument heads, we adopt a distributional perspective, where the meaning is described by the set of textual contexts in which words appear. [sent-149, score-0.574]
42 In distributional models, words are thus represented through vectors built over these observable contexts: similar vectors suggest semantic relatedness as a function of the distance between two words, capturing paradigmatic (e. [sent-150, score-0.314]
43 In the argument classification task, the similarity between two argument heads h1 and h2 observed in FrameNet can be computed over h→1 and The model for a given frame element FEk is built around the semantic heads h observed in the role FEk: → →h2. [sent-160, score-1.027]
44 These LSA vectors →h express the individual annotated examples as they are immerse in the LSA TSMRPOoEleDAI,CKUFEMRkc C78543621lu: s{ fphacbteroi ,ntsgvchielrdtoa}fng,uprsethw,ifmoaupernt,hypeodtr,suiclmp}otrahsen ,pmaievrhdu,snleti}. [sent-162, score-0.118]
45 sr Table 1: Clusters of semantic heads in the Sub j position for the frame STATEMENT with σ = 0. [sent-163, score-0.356]
46 Moreover, given FEk, a model for each individual syntactic relation r (i. [sent-165, score-0.11]
47 As the LSA vectors →h are available for the se- −F − →Ek mantic heads h, a vector representation for the role FEk can be obtained from the annotated data. [sent-173, score-0.308]
48 However, one single vector is a too simplistic representation given the rich nature of semantic roles FEk. [sent-174, score-0.155]
49 QT is a generalization of k-mean where a variable number of clusters can be obtained. [sent-178, score-0.158]
50 Given a syntactic relation r, denotes the clusters derived by QT HrFEk CrFEk HrFEk. [sent-182, score-0.127]
51 ect Eora →hc , c computed as the geometric centroid of its semantic heads h ∈ c. [sent-184, score-0.226]
52 l of every frame elements FEk: it consists of centroids with c ⊆ Each c represents FEk through a sietth ocf ⊆ ⊆sim Hilar heads, as role fillers observed in FrameNet. [sent-186, score-0.229]
53 Table 1 represents clusters for the heads of the STATEMENT frame. [sent-187, score-0.212]
54 In argument classification we assume that the evoking predicate word for the frame F in an input sentence s is known. [sent-188, score-0.367]
55 , (rn, hn)} where the heads hi are i n{ (trhe syntactic relation)} ri whietrhe eth teh underlying lexical unit of F. [sent-193, score-0.383]
56 Given the syntactic relation r, the clusters c ∈ whose centroid vector c~ ris, tchloesce rlu tsote r~hs are s Celected. [sent-195, score-0.127]
57 ckj ∈ and h is the usual CrFEk, cosine similarity: simcos(h,ckj) =k−→h−→h k · k−→c→−ckkjjk Then, through a k-nearest neighbours (k-NN) strategy within Dr,h, the m clusters ckj most simi- set D(rm,h). [sent-198, score-0.206]
58 1 the preference estimation for the incoming head h = professor connected to a LU by the Sub j relation is shown. [sent-200, score-0.123]
59 Clusters for the heads in Table 1 are also reported. [sent-201, score-0.126]
60 First, in the set of clusters whose similarity with professor is higher than a threshold τ the m = 5 most similar clusters are selected. [sent-202, score-0.217]
61 5 to the source (r, h) arguments, by rejecting cases only when no information about the head h is available from the unlabeled corpus or no example of relation r for the role FEk is available from the annotated corpus. [sent-210, score-0.28]
62 If the head h has never been met in the unlabeled corpus or the high grammatical ambiguity of the sentence does not allow to locate it reliably, Eq. [sent-214, score-0.151]
63 A more robust argument preference function for all arguments (ri, hi) ∈ s of the frame F is thus given by: prob(FEk|ri, hi) = λ1prob(FEk|ri, λ2prob(FEk|ri) hi) + + λ3prob(FEk) (6) where weights λ1, λ2, λ3 can be heuristically assigned or estimated from the training set2. [sent-220, score-0.379]
64 6 defines roles preferences local to individual arguments (ri, hi). [sent-226, score-0.246]
65 However, an argument frame is a joint structure, with strong dependencies between arguments. [sent-227, score-0.305]
66 We thus propose to model the reranking phase (RR) as a HMM sequence labeling task. [sent-228, score-0.156]
67 6 is available, the best frame element sequence for the entire sentence s can be selected by defining the function θ(·) that maps arguments (ri, hi) ∈ s eto f nfractmioen e θle(·m)e tnhatst FEk: FE(k1,. [sent-237, score-0.204]
68 241 Figure 1: A k-NN approach to the role classification for hi = professor Notice that different transfer functions θ(·) are usually possible. [sent-250, score-0.308]
69 On the other hand, transition probabilities model role sequences and support the expectations about argument frames of a sentence. [sent-275, score-0.349]
70 Again the emission probability can be rewritten as: P(ri,hi|FEθ(i)) =P(FEθ(Pi)(|rFi,Ehθi()i) P)(ri,hi) (10) Since P(ri, hi) does not depend on the role labeling, maximizing Eq. [sent-282, score-0.148]
71 4 Empirical Analysis The aim of the evaluation is to measure the reachable accuracy of the simple model proposed and to compare its impact over in-domain and out-ofdomain semantic role labeling tasks. [sent-296, score-0.429]
72 In particular, we will evaluate the argument classification (AC) task in Section 4. [sent-297, score-0.237]
73 Vectors representing semantic heads have h~ been computed according to the ”dependencybased” vector space discussed in (Pado and Lapata, 2007). [sent-315, score-0.265]
74 1, allows to generalize lexical information: similar heads within the latent semantic space are built from the annotated examples and they allow to predict the behavior of new unseen words as found in the test sentences. [sent-327, score-0.348]
75 704 Table 3: Accuracy on Arg classification tasks wrt different clustering policies 1then many singleton clusters are promoted (i. [sent-343, score-0.203]
76 We measured the performance on the argument classification tasks of different models obtained by combing different choices of σ with Eq. [sent-347, score-0.237]
77 2 Argument Classification Accuracy In these experiments we evaluate the quality of the argument classification step against the lexical knowledge acquired from unlabeled texts and the reranking step. [sent-366, score-0.426]
78 The accuracy reachable on the gold standard argument boundaries has been compared across several experimental settings. [sent-367, score-0.274]
79 Table 4 reports the accuracy results obtained over the three corpora (defined as in Table 2): the accuracy scores are averaged over different values of m in Eq. [sent-404, score-0.12]
80 Notice how the positive impact of the backoff models and the HMM reranking policy is similarly reflected by all the collections. [sent-414, score-0.16]
81 This seems to confirm the hypothesis that the model is able to properly generalize the required lexical information across different domains. [sent-423, score-0.152]
82 It is interesting to outline that the individual stages of the proposed model play different roles in the different domains, as Table 4 suggests. [sent-424, score-0.124]
83 Although the positive contributions of the individual processing stages are uniformly confirmed, some differences can be outlined: • The beneficial impact of the lexical informatTiohne (i. [sent-425, score-0.146]
84 The ANC domain seems not to significantly benefit when the distributional model (Eq. [sent-428, score-0.19]
85 5 depends both from the evidence gathered in the corpus about lexical heads h as well as about the relation r. [sent-431, score-0.239]
86 The different syntactic style of ANC seems thus the main responsible of the poor impact of distributional information, as it is often unapplicable to ANC test cases. [sent-440, score-0.195]
87 244 lections seems characterized by a lower level of complexity (see for example the accuracy of the Local prior model, that is about 51% as for the ANC). [sent-443, score-0.139]
88 The BNC-FN test collection seems the most complex one, and the impact of the lexical information brought by the distributional model is here maximal. [sent-445, score-0.301]
89 This is mainly due to the coherence between the distributions of lexical and grammatical phenomena in the test and training data. [sent-446, score-0.133]
90 • The role of HMM reranking is an effective way rtoo compensate errors ning t ihse nloc eaflf argument classifications for all the three domains. [sent-447, score-0.37]
91 It seems that the HMM model well captures some information on the global semantic structure of a sentence: this is helpful in cases where errors in the grammatical recognition (of individual arguments or at sentence level) are more frequent and afflict the local distributional model. [sent-454, score-0.542]
92 in the NTI and ANC data sets), the higher seems the impact of the reranking phase. [sent-457, score-0.149]
93 + 5 + Conclusions In this paper, a distributional approach for acquiring a semi-supervised model of argument classification (AC) preferences has been proposed. [sent-491, score-0.416]
94 It aims at improving the generalization capability of the inductive SRL approach by reducing the complexity of the employed grammatical features and through a distributional representation of lexical features. [sent-492, score-0.315]
95 The model seems to capitalize from simple methods of lexical modeling (i. [sent-495, score-0.187]
96 the estimation of lexico-grammatical preferences through distributional analysis over unlabeled data), estimation (through syntactic or lexical back-off where necessary) and reranking. [sent-497, score-0.27]
97 Semi-supervised semantic role labeling using the latent words language model. [sent-539, score-0.257]
98 On the role of lexical features in sequence labeling. [sent-566, score-0.171]
99 The effect of syntactic representation on semantic role labeling. [sent-583, score-0.199]
100 The proposition bank: A corpus annotated with semantic roles. [sent-612, score-0.15]
wordName wordTfidf (topN-words)
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