acl acl2013 acl2013-192 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Roi Reichart ; Anna Korhonen
Abstract: Subcategorization frames (SCFs), selectional preferences (SPs) and verb classes capture related aspects of the predicateargument structure. We present the first unified framework for unsupervised learning of these three types of information. We show how to utilize Determinantal Point Processes (DPPs), elegant probabilistic models that are defined over the possible subsets of a given dataset and give higher probability mass to high quality and diverse subsets, for clustering. Our novel clustering algorithm constructs a joint SCF-DPP DPP kernel matrix and utilizes the efficient sampling algorithms of DPPs to cluster together verbs with similar SCFs and SPs. We evaluate the induced clusters in the context of the three tasks and show results that are superior to strong baselines for each 1.
Reference: text
sentIndex sentText sentNum sentScore
1 uk Abstract Subcategorization frames (SCFs), selectional preferences (SPs) and verb classes capture related aspects of the predicateargument structure. [sent-3, score-0.425]
2 We show how to utilize Determinantal Point Processes (DPPs), elegant probabilistic models that are defined over the possible subsets of a given dataset and give higher probability mass to high quality and diverse subsets, for clustering. [sent-5, score-0.148]
3 Our novel clustering algorithm constructs a joint SCF-DPP DPP kernel matrix and utilizes the efficient sampling algorithms of DPPs to cluster together verbs with similar SCFs and SPs. [sent-6, score-0.667]
4 We evaluate the induced clusters in the context of the three tasks and show results that are superior to strong baselines for each 1. [sent-7, score-0.156]
5 1 Introduction Verb classes (VCs), subcategorization frames (SCFs) and selectional preferences (SPs) capture different aspects of predicate-argument structure. [sent-8, score-0.366]
6 These three of types of information have proved useful for Natural Language Processing (NLP) 1The source code of the clustering algorithms and evaluation is submitted with this paper and will be made publicly available upon acceptance of the paper. [sent-10, score-0.138]
7 The task of SCF induction involves identifying the arguments of a verb lemma and generalizing about the frames (i. [sent-24, score-0.378]
8 For example, in (1), the verb ”show” takes the frame SUBJ-DOBJCCOMP (subject, direct object, and clausal complement). [sent-27, score-0.213]
9 (1) [A number of SCF acquisition papers]SUBJ [show]VERB [their readers]DOBJ [which features are most valuable for the acquisition process]CCOMP. [sent-28, score-0.224]
10 In sentence (2), for example, the verb ”show” takes the frame SUBJ-DOBJ. [sent-30, score-0.213]
11 Finally, VC induction involves clustering together verbs with similar meaning, reflected in similar SCFs and SPs. [sent-35, score-0.274]
12 O´ S ´eaghdha (2010) presented a “dual-topic” model for SPs that induces also verb clusters. [sent-41, score-0.192]
13 (2012) presented a joint model for inducing simple syntactic frames and VCs. [sent-44, score-0.155]
14 Our framework is based on Determinantal Point Processes (DPPs, (Kulesza, 2012; Kulesza and Taskar, 2012c)), elegant probabilistic models that are defined over the possible subsets of a given dataset and give higher probability mass to high quality and diverse subsets. [sent-50, score-0.148]
15 We first show how individual-task DPP kernel matrices can be naturally combined to construct a joint kernel. [sent-51, score-0.207]
16 We then introduce a novel clustering algorithm based on iterative DPP sampling which can (contrary to other probabilistic frameworks such as Markov random fields) be performed both accurately and efficiently. [sent-53, score-0.22]
17 We also contribute by evaluating the value of the clusters induced by our model for the acquisition of the three information types. [sent-55, score-0.268]
18 Our evaluation against a well-known VC gold standard shows that our clustering model outperforms the state-of-theart verb clustering algorithm of Sun and Korhonen (2009), in our setup where no manually created SCF or SP data is available. [sent-56, score-0.499]
19 2 Previous Work SCF acquisition Most current works induce SCFs from the output of an unlexicalized parser (i. [sent-58, score-0.139]
20 (2012) presented a Bayesian network model for syntactic frame induction that identifies SPs on argument types. [sent-68, score-0.141]
21 (2012) do not capture sets of arguments for verbs so are far simpler than traditional SCFs. [sent-70, score-0.145]
22 Current approaches to SCF acquisition suffer from lack of semantic information which is needed to guide the purely syntax-driven acquisition process. [sent-71, score-0.224]
23 SP acquisition Considerable research has been conducted on SP acquisition, with a variety of unsupervised models proposed for this task that use no hand-crafted information during training. [sent-74, score-0.145]
24 Previous unsupervised VC acquisition approaches clustered a variety of linguistic features using different (e. [sent-86, score-0.145]
25 The linguistic features included SCFs and SPs, but these were induced separately and then feeded as features to the clustering algorithm. [sent-93, score-0.192]
26 Our framework combines together SCF-motivated and SP-motivated kernel matrices , and uses the joint kernel to induce verb clusters which are likely to be highly relevant for both tasks. [sent-94, score-0.618]
27 Importantly, no manual or automatic system for SCF or SP acquisition has been utilized when constructing the kernel matrices, we only consider features extracted from the output of an unlexicalized parser. [sent-95, score-0.236]
28 (2012) presented a joint unsupervised model of SCF and SP acquisition based on non-negative tensor factorization. [sent-104, score-0.225]
29 Our idea is to utilize DPPs for verb clustering that informs both SCF and SP acquisition. [sent-110, score-0.296]
30 Individual task DPP kernels represent (i) the quality of a data point (verb) as its average featurebased similarity with the other points in the data set and (ii) the divergence between a pair of points as the inverse similarity between them. [sent-114, score-0.166]
31 The high quality and diverse subsets sampled from the DPP model are considered good cluster seeds as they are likely to be relatively uniformly spread and to provide good coverage of the data set. [sent-116, score-0.26]
32 The algorithm induces an hierarchical clustering, which is particularly suitable for semantic tasks, where a set of clusters that share a parent consists of pure members (i. [sent-117, score-0.164]
33 most of the points in each cluster member belong to the same gold cluster) and together provide good coverage of the verb space. [sent-119, score-0.369]
34 1), we discuss the construction of the joint DPP kernel, given a kernel for each individual task, In section 3. [sent-121, score-0.162]
35 2 Constructing a Joint Kernel Matrix DPPs are particularly suitable for joint modeling as they come with various simple and intuitive ways to combine individual model kernel matrices into a joint kernel. [sent-145, score-0.245]
36 This stems from the fact that every positive-semidefinite matrix forms a legal DPP kernel (equation 1). [sent-146, score-0.174]
37 2 In practice we construct the joint kernel in the following way. [sent-149, score-0.162]
38 d PL2 nd etwfinoe Dd by L, P2 = A2TA2, we construct the joint kernel L12: L12 = L1L2L2L1 Where C A1TA1AT2A2. [sent-152, score-0.162]
39 Features Our algorithm builds two DPP kernel matrices (the GenKernelMatrix function), in which the rows and columns correspond to the verbs in the data set, such that the (i, j)-th entry corresponds to verbs number iand j. [sent-156, score-0.375]
40 Following equations 2 and 3 one matrix is built for SCF and one for SP, and they are then combined into the 2Note that we do not take a product of the individual models but only of their kernel matrices. [sent-157, score-0.174]
41 865 joint kernel matrix (the GenJointMat function) following equation 5. [sent-161, score-0.212]
42 Each kernel matrix requires a proper feature representation φ and quality score q. [sent-162, score-0.21]
43 In both kernels we represent a verb by the counts of the grammatical relations (GRs) it participates in. [sent-163, score-0.192]
44 In the SCF kernel a GR is represented by the GR type and the POS tags of the verb and its arguments. [sent-164, score-0.282]
45 In the SP kernels the GRs are represented by the POS tags of the verb and its arguments as well as by the argument head word. [sent-165, score-0.287]
46 The quality score qi of the i-th verb is the average similarity of this verb with the other verbs in the dataset. [sent-167, score-0.471]
47 Cluster set construction In its while loop, the algorithm iteratively generates fixed-size cluster sets such that each data point belongs to exactly one cluster in one set. [sent-168, score-0.36]
48 These cluster sets form the leaf level of the tree in Figure (1). [sent-169, score-0.146]
49 It does so by extracting the T highest probability K-point samples from a set of M subsets, each of which sampled from the joint DPP model, and clustering them by the cluster procedure. [sent-170, score-0.356]
50 The cluster procedure first seeds a K-cluster set with the highest probability sample. [sent-172, score-0.146]
51 Then, it gradually extends the clusters by iteratively mapping the samples, in decreasing order of probability, to the existing clusters (the m1Mapping function). [sent-173, score-0.204]
52 Mapping is done by attaching every point in the mapped subset to its closet cluster, where the distance between a point and the cluster is the maximum over the distances between the point and each of the points in the cluster. [sent-174, score-0.294]
53 Based on the DPP properties, the higher the probability of a sampled subset, the more likely it is to consist of distinct points that provide a good coverage of the verb set. [sent-176, score-0.186]
54 By iteratively extending the clusters with high probability subsets, we thus expect each cluster set to consist of clusters that demonstrate these properties. [sent-177, score-0.35]
55 The iterative DPP-samples clustering (the While loop) generates the lowest level of the tree, by dividing the data set into cluster sets, each of which consists of K clusters. [sent-185, score-0.284]
56 Each point in the data set belongs to exactly one cluster in exactly one set. [sent-186, score-0.186]
57 The agglomerative clustering then iteratively combines cluster sets such that in each iteration two sets are combined to one set with K clusters. [sent-187, score-0.388]
58 Agglomerative Clustering Finally, the AgglomerativeClustering function builds a hierarchy of cluster sets, by iteratively combining cluster set pairs. [sent-188, score-0.292]
59 In each iteration it computes the similarity between any such pair, defined to be the lowest similarity between their cluster members, which is in turn defined to be the lowest cosine similarity between their point members. [sent-189, score-0.231]
60 The most similar cluster sets are combined such that each of the clusters in one set is mapped to its most similar cluster in the other set. [sent-190, score-0.394]
61 In this step the algorithm generates data partitions at different granularity levels from finest (from the iterative sampling step) to the coarsest set (generated by the last agglomerative clustering iteration and consisting of exactly K clusters). [sent-191, score-0.324]
62 4 Evaluation Data sets and gold standards We evaluated the SCFs and verb clusters on gold standard datasets. [sent-193, score-0.334]
63 3 SCF types per verb) obtained by annotating 250 corpus occurrences per verb with the SCF types of (de Cruys et al. [sent-197, score-0.158]
64 Where a verb has more than one VerbNet class, we assign it to the one supported by the highest number of member verbs. [sent-200, score-0.158]
65 R915 R5B Table 2: Performance of the Corpus Statistics SP baseline (non-filtered, NF) as well as for three filtering methods: frequency based (filter-baseline, B), DPP-cluster based (DPP) and AC cluster based (AC). [sent-238, score-0.19]
66 ficient representation of each class, we collected from VerbNet the verbs for which at least one of the possible classes is represented in the 183 verbs set by at least one and at most seven verbs. [sent-243, score-0.178]
67 Since 176 out of the 183 initial verbs are represented in this corpus, our final gold standard consists of 34 classes containing 277 verbs, ofwhich 176 have SCF gold standard and has been evaluated for this task. [sent-247, score-0.163]
68 Clustering Evaluation We first evaluate the quality of the clusters induced by our algorithm (DPP-cluster) compared to the gold standard VCs (table 1). [sent-249, score-0.257]
69 We also compare to the state-of-the-art spectral clustering method of Sun and Korhonen (2009) where our 4Importantly, the kernel matrix L used in the agglomerative clustering process is also used by AC. [sent-251, score-0.539]
70 kernel matrix is used for the distance between data points (SC) 5. [sent-252, score-0.202]
71 We evaluated the unified cluster set induced in each iteration of our algorithm and of the AC baseline and induced the same number of clusters as in each iteration of our algorithm using the SC baseline. [sent-253, score-0.552]
72 Since the number of clusters in each iteration is not an argument for our algorithm or for the AC baseline, the number of clusters slightly differ between the two. [sent-254, score-0.316]
73 We do this by gathering the GR combinations for each of the verbs in our gold standard, assuming they are frames and gathering their frequencies. [sent-275, score-0.243]
74 To remedy this imbalance, we apply a cluster based filtering method on top of the maximum- recall frequency filter. [sent-286, score-0.226]
75 This filter excludes a candidate frame from a verb’s lexicon only if it meets the frequency filter criterion and appears in no more than N other members of the cluster of the verb in question. [sent-287, score-0.391]
76 The filter utilizes the clustering produced by the seventh to last iteration of DPPcluster that contains seven clusters with approximately 30 members each. [sent-288, score-0.285]
77 Table 3 clearly demonstrates that cluster based filtering (DPP-cluster and AC) is the only method that provides a good balance between the recall and the precision of the SCF lexicon. [sent-291, score-0.258]
78 Moreover, the lexicon induced by this method includes a substantially higher number of frames per verb compared to the other filtering methods. [sent-292, score-0.405]
79 This clearly demonstrates that the clustering serves to provide SCF acquisition with semantic information needed for improved performance. [sent-294, score-0.25]
80 All methods except from cluster based filtering (DPP-cluster and AC) induce lexicons with strong recall/precision imbalance. [sent-324, score-0.217]
81 Cluster based filtering keeps a larger number of frames in the lexicon compared to the frequency thresholding baseline, while keeping similar F-score levels. [sent-325, score-0.193]
82 We therefore propose to measure both aspects of the SP task by computing both the recall and the precision between the list of possible arguments a verb can take according to the model and the corresponding test corpus list 9. [sent-330, score-0.282]
83 We evaluate the value of our clustering for SP acquisition in the particularly challenging scenario of domain adaptation. [sent-331, score-0.25]
84 Using the verb clusters we create a filtered version of the BNC argument lexicon which includes in the noun argument list of a verb only those nouns that appear in the BNC as arguments of that verb and of one of its cluster members. [sent-337, score-0.888]
85 Table10 02− presents the results for verbs with up to 200, 600 and 1000 noun arguments in the training data. [sent-345, score-0.145]
86 In all cases, the relative error reduction of the DPP cluster filter is substantially higher than that of the frequency baseline. [sent-346, score-0.174]
87 Note that for this task the baseline AC clusters are of low quality which is reflects by an error reduction ratio of up to 0. [sent-347, score-0.166]
88 5 Conclusions and Future Work In this paper we have presented the first unified framework for the induction of verb clusters, subcategorization frames and selectional preferences from corpus data. [sent-349, score-0.621]
89 Our key idea is to cluster together verbs with similar SCFs and SPs and to use the resulting clusters for SCF and SP induction. [sent-350, score-0.337]
90 To implement our idea we presented a novel method which involves constructing a product DPP model for SCFs and SPs and introduced a new algorithm that utilizes the efficient DPP sampling algorithms to cluster together verbs with similar SCFs and SPs. [sent-351, score-0.317]
91 The induced clusters performed well in evaluation against a VerbNet -based gold standard and proved useful in improving the quality of SCFs and SPs over strong baselines. [sent-352, score-0.229]
92 Not only the acquisition of different types information (syntactic and semantic) can support and inform each other, but also a unified framework can be useful for NLP tasks and applications which require rich information about predicate-argument structure. [sent-355, score-0.162]
93 IRASubcat, a highly customizable, language independent tool for the acquisition of verbal subcategorization information from corpus. [sent-363, score-0.24]
94 Inferring semantic roles using subcategorization frames and maximum entropy model. [sent-382, score-0.245]
95 Unsupervised acquisition of verb subcategorization frames from shallow-parsed corpora. [sent-505, score-0.515]
96 Which are the best features for automatic verb classification. [sent-513, score-0.158]
97 A system for large-scale acquisition of verbal, nominal and adjectival subcategorization frames from corpora. [sent-550, score-0.357]
98 Combining EM training and the MDL principle for an automatic verb classification incorporating selectional preferences. [sent-576, score-0.239]
99 Experiments on the automatic induction of german semantic verb classes. [sent-580, score-0.205]
100 Unsupervised and constrained dirichlet process mixture models for verb clustering. [sent-612, score-0.158]
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