acl acl2010 acl2010-94 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Hector-Hugo Franco-Penya
Abstract: ―Tree SRL system‖ is a Semantic Role Labelling supervised system based on a tree-distance algorithm and a simple k-NN implementation. The novelty of the system lies in comparing the sentences as tree structures with multiple relations instead of extracting vectors of features for each relation and classifying them. The system was tested with the English CoNLL-2009 shared task data set where 79% accuracy was obtained. 1
Reference: text
sentIndex sentText sentNum sentScore
1 ie Abstract ―Tree SRL system‖ is a Semantic Role Labelling supervised system based on a tree-distance algorithm and a simple k-NN implementation. [sent-4, score-0.061]
2 The novelty of the system lies in comparing the sentences as tree structures with multiple relations instead of extracting vectors of features for each relation and classifying them. [sent-5, score-0.526]
3 The system was tested with the English CoNLL-2009 shared task data set where 79% accuracy was obtained. [sent-6, score-0.111]
4 1 Introduction Semantic Role Labelling (SRL) is a natural language processing task which deals with semantic analysis at sentence-level. [sent-7, score-0.077]
5 SRL is the task of identifying arguments for a certain predicate and labelling them. [sent-8, score-0.712]
6 The arguments determine events such as ―who‖, ―whom‖, ―where‖, etc, with reference to one predicate. [sent-11, score-0.125]
7 The possible semantic roles are pre-defined for each predicate. [sent-12, score-0.134]
8 It adds a semantic layer to the Penn TreeBank (Marcus et al, 1994) and defines a set of semantic roles for each predicate. [sent-19, score-0.211]
9 It is difficult to define universal semantic roles for all predicates. [sent-20, score-0.134]
10 That is why PropBank defines a set of semantic roles for each possible sense of each predicate (frame) [See a sample of the frame ―raise‖ on the Figure 1 caption]. [sent-21, score-0.579]
11 The following table describes the number of sentences, sub-trees and labels contained in them, and the ratios of sub-trees per sentences and relations per sub-tree. [sent-28, score-0.197]
12 The four most frequent labels in the data set are: A1:35%, A0:20. [sent-31, score-0.133]
13 72% Propbank was originally built using constituent tree structures, but here only the dependency tree structure version was used. [sent-34, score-0.516]
14 Note that de- pendency tree structures have labels on the arrows. [sent-35, score-0.406]
15 The tree distance algorithm cannot work with these labelled arrows and so they are moved to the child node as an extra label. [sent-36, score-0.886]
16 The task performed by the Tree SRL system consists of labelling the relations (predicate arguments) which are assumed to be already identified. [sent-37, score-0.386]
17 3 Tree Distance The tree distance algorithm has already been applied to text entailment (Kouylekov & Magnini, 2005) and question answering (Punyakanok et al, 2004; Emms, 2006) with positive results. [sent-38, score-0.435]
18 The main contribution of this piece of work to the SRL field is the inclusion of the tree distance algorithm into an SRL system, working with tree structures in contrast to the classical ―feature extraction‖ and ―classification‖. [sent-39, score-0.625]
19 Kim et al (2009) developed a similar system for Information Extraction. [sent-40, score-0.137]
20 0c S20tu1d0e Antss Roecsieaatirconh f Woror Cksomhoppu,t paatgioensa 7l9 L–in8g4u,istics A two sentence sample, in a dependency tree repre sentation. [sent-43, score-0.292]
21 The square nod e represent the predicate that is going to be analyzed, (there can be multiple predicates in a single s entence). [sent-47, score-0.538]
22 Semi-dotted arrows between a square node and an ellipse node represent a semantic relati on. [sent-48, score-0.606]
23 This arrow has a semantic tag (A1, A2, A3 and A4). [sent-49, score-0.109]
24 The grey shadow contains all the nodes of the sub tre e for the ―rose‖ predicate. [sent-50, score-0.395]
25 The dotted double arrows between the nodes of bot h sentences represent the tree distance alignment for both sub-trees. [sent-51, score-0.696]
26 In this particular case every single node is matched. [sent-52, score-0.179]
27 The advantage of this algorithm is that its computational cost is low. [sent-54, score-0.152]
28 The optimal matching depends on the defined atomic cost of matching two nodes. [sent-55, score-0.535]
29 4 Tree SRL system architecture For the training and testing data set, all possible sub-trees were extracted. [sent-56, score-0.105]
30 Then, using the tree distance algorithm, the test sub-trees are labelled using the training ones. [sent-58, score-0.595]
31 Finally, the predicted labels get assembled on the original sentence where the test sub-tree came from. [sent-59, score-0.181]
32 A sub-tree extracted from a sentence, contains a predicate node, all its argument nodes and all the ancestors up to the first common ancestor of all nodes. [sent-61, score-0.63]
33 ), the sub-tree extracted from the above sentence will contain the nodes: ―a1‖, ―a2‖, ―p‖, all ancestors of ―a1‖,‖a2‖ and ―p‖ up to the first common one, in this case node ―u‖, which is also included in the sub-tree. [sent-65, score-0.227]
34 All of the white nodes are not included in the sub-tree. [sent-66, score-0.158]
35 5 Labelling Suppose that in Figure 1, the bottom sentence is the query, where the grey shadow contains the sub-tree to be labelled and the top sentence contains the sub-tree sample chosen to label the query. [sent-68, score-0.556]
36 Then, an alignment between the sample sub-tree and the query sub-tree suggests labelling the query sub-tree with A1, A2 and A3, where the first two labels are right but the last label, A4, is predicted as A3, so it is wrong. [sent-69, score-0.668]
37 Input: A sub-tree to be labelled Input: list of alignments sorted by ascending tree distance Output: labelled sub-tree foreach argument(a) in T do foreach alignment (ali) in the sorted list do if there is a semantic relation (ali. [sent-70, score-1.738]
38 function(a)) Then break loop; end end label relation p-a with the label of the relation (ali. [sent-72, score-0.482]
39 ali is an alignment between the sub-tree that has to be labelled and a sub-tree in the training dataset. [sent-76, score-0.341]
40 However, if the whole query is labelled using a single answer sample, the prediction is guaranteed to be consistent (no repeated argument labels). [sent-80, score-0.433]
41 Some possible ways to label the semantic relation using a sorted list of alignments (with each sub-tree of the training data set) is discussed ahead. [sent-81, score-0.421]
42 Each sub-tree contains one predicate and several semantic relations, one for each argument node. [sent-82, score-0.501]
43 1 Treating relations independently In this sub-section, the neighbouring sub-trees for one relation of a sub-tree T refers to the near81 est sub-trees with which the match with T produces a match between two predicate nodes and two argument nodes. [sent-84, score-0.893]
44 A label from the nearest neighbour(s) can be transferred to T for labelling the relation. [sent-85, score-0.486]
45 The current implementation (Approach A), described in more detail in Figure 4, labels a relation using the first nearest neighbour from a list ordered by ascending tree distance. [sent-86, score-0.749]
46 If there are several nearest neighbours, the first one on the list is used. [sent-87, score-0.166]
47 This is a naive implementation of the k-NN algorithm where in case of multiple nearest neighbours only one is used and the others get ignored. [sent-88, score-0.215]
48 A way to make it deterministic can be by extending the parameter ―k‖ in case of multiple cases at the same distance or a tie in the voting (Approach B). [sent-91, score-0.167]
49 2 Treating relations dependently In this section, a sample refers to a sub-tree containing all arguments and its labels. [sent-93, score-0.263]
50 Some strategies can lead to non-consistent structures (core argument labels cannot appear twice in the same sub-tree). [sent-95, score-0.28]
51 It does not have any mechanism to keep the consistency of the whole predicate structure. [sent-97, score-0.394]
52 Another way is to find a sample that contains enough information to label the whole sub-tree (Approach C). [sent-98, score-0.216]
53 The limitation of this model is that the required sample may not exist or the tree distance may be very high, making those samples poor predictors. [sent-100, score-0.491]
54 The implemented method (Approach A) indirectly attempts to find a training sample sub-tree which contains labels for all the arguments of the predicate. [sent-101, score-0.366]
55 It is expected for tree distances to be smaller than other sub-trees that do not have information to label all the desired relations. [sent-102, score-0.373]
56 The system tries to get a consistent structure using a simple algorithm. [sent-103, score-0.061]
57 Only in the case when using the nearest tree does not lead to labelling the whole structure, labels are predicted using multiple samples, thereby, risking the structure consistency. [sent-104, score-0.862]
58 Future implementations will rank possible candidate labels for each relation (probably using multiple samples). [sent-105, score-0.261]
59 A ―joint scoring algorithm‖, which is commonly used (Marquez et al, 2008), can be applied for consistency checking after finding the rank probability for all the argument labels for the same predicate (Approach D). [sent-106, score-0.588]
60 6 Experiments: the matching cost The cost of matching two nodes is crucial to the performance of the system. [sent-107, score-0.654]
61 Different atomic measures (ways to measure the cost of matching two nodes) that were tested are explained ahead. [sent-108, score-0.439]
62 Results for experiments using these atomic measures are given in Table 2. [sent-109, score-0.191]
63 1 Binary system For Binary system, the atomic cost of matching two nodes is one if label POS or dependency relations are different, otherwise the cost is zero. [sent-111, score-1.047]
64 The atomic cost of inserting or deleting a node is always one. [sent-112, score-0.605]
65 2 Ternary system The next intuitive measure is how the system would perform in case of a ternary cost (ternary system). [sent-115, score-0.423]
66 The atomic cost is half if POS or dependency relation is different, one if POS and dependency relation are different or zero in all other case. [sent-116, score-0.657]
67 For this system, Table 2 shows a very similar accuracy to the binary one. [sent-117, score-0.092]
68 3 Hamming system The atomic cost of matching two nodes is the sum of the following sub costs: 0. [sent-119, score-0.796]
69 25 if one node is a predicate but the other is not or if both nodes are predicates but with different lemma. [sent-126, score-0.745]
70 Note that the sum of all costs cannot be greater than one. [sent-128, score-0.091]
71 4 Predicate match system The analysis of results for the previous systems shows that the accuracy is higher for the subtrees that are labelled using sub-trees with the same predicate node. [sent-130, score-0.743]
72 Consequently, this strategy attempts to force the predicate to be the same. [sent-131, score-0.36]
73 In this system, the atomic cost of matching two nodes is the sum of the following sub costs: 82 0. [sent-132, score-0.735]
74 if one is a predicate and the other node is not or both nodes are predicates but with different lemma. [sent-136, score-0.745]
75 5 Complex system This strategy attempts to improve the accuracy by adding an extra label to the argument nodes and using it. [sent-139, score-0.506]
76 The atomic cost of matching two nodes is the sum of the following sub costs: 0. [sent-140, score-0.735]
77 1 for each different label (dependency relation or POS or lemma). [sent-141, score-0.194]
78 1 for each pair of different labels (dependency relation or POS or lemma). [sent-143, score-0.222]
79 4 if one node is a predicate and the other is not. [sent-145, score-0.505]
80 4 if both nodes are predicates and lemma is different. [sent-147, score-0.312]
81 2 if one node is marked as an argument and the other is not or one node is marked as a predicate and the other is not. [sent-148, score-0.782]
82 The atomic cost of deleting or inserting a node is: two if the node is an argument or predicate node and one in any other case. [sent-149, score-1.387]
83 The validation data set is added to the training data set when the system is labelling the evalua- tion data set. [sent-151, score-0.322]
84 o2p8m%ent Accuracy is measured as the percentage of semantic labels correctly predicted. [sent-155, score-0.21]
85 The implementation of the Tree SRL system takes several days to run a single experiment. [sent-156, score-0.061]
86 It makes non viable the idea of using the development data set for adjusting parameters and that is why, for the last three systems (Hamming, Predicate Match and Complex), the accuracy over the development data set is not measured. [sent-157, score-0.097]
87 If the complexity gets increased (Ternary), the number of cases having the multiple nearest sub-trees gets reduced. [sent-160, score-0.159]
88 The output of the system only contains five per cent of inconsistent structures (Binary and Ternary), which is lower than expected. [sent-162, score-0.149]
89 Higher accuracy for the relations where a sub-tree is labelled using a sub-tree sample which has the same predicate node. [sent-166, score-0.757]
90 This resulted in low accuracy for they predicted labels due to multiple nearest neighbours. [sent-170, score-0.39]
91 It is surprising that the hamming measure reaches higher accuracy than the ―predicate match‖, which uses more information, and is also surprising that the accuracies for ―Hamming‖, ―Predicate Match‖ and ―Complex‖ systems are very similar. [sent-171, score-0.224]
92 (Binary system) The accuracy results for multiple languages suggest that the size of the corpora has a strong influence on the results of the system performance. [sent-174, score-0.15]
93 This system does not identify argu- ments and does not perform predicate sense disambiguation. [sent-176, score-0.387]
94 83 8 Conclusion The tree distance algorithm has been applied successfully to build a SRL system. [sent-177, score-0.352]
95 Future work will focus on improving the performance of the system by: a) trying to extend the sub-trees which will contain more contextual information, b) using different approaches to label semantic relations discussed in Section 5. [sent-178, score-0.307]
96 Also, the system will be expanded to identify arguments using a tree distance algorithm. [sent-179, score-0.538]
97 Evaluating the task of identifying the arguments and labelling the relations separately will assist in determining which systems to combine to create an hybrid system with better performance. [sent-180, score-0.511]
98 The CoNLL-2009 shared task: syntactic and semantic dependencies in multiple languages. [sent-191, score-0.116]
99 Fast algorithms for the unit cost editing distance between trees. [sent-239, score-0.328]
100 Simple fast algorithms for the editing distance between trees and related problems. [sent-251, score-0.176]
wordName wordTfidf (topN-words)
[('predicate', 0.326), ('labelling', 0.261), ('labelled', 0.243), ('srl', 0.243), ('tree', 0.224), ('atomic', 0.191), ('foreach', 0.185), ('node', 0.179), ('nodes', 0.158), ('cost', 0.152), ('ternary', 0.149), ('labels', 0.133), ('distance', 0.128), ('arguments', 0.125), ('nearest', 0.12), ('arrows', 0.112), ('hamming', 0.112), ('emms', 0.111), ('label', 0.105), ('sub', 0.103), ('argument', 0.098), ('matching', 0.096), ('rquez', 0.089), ('relation', 0.089), ('shasha', 0.084), ('predicates', 0.082), ('semantic', 0.077), ('al', 0.076), ('minvalue', 0.074), ('shadow', 0.074), ('trinity', 0.074), ('sample', 0.074), ('ascending', 0.072), ('lemma', 0.072), ('dependency', 0.068), ('kaizhong', 0.065), ('neighbour', 0.065), ('samples', 0.065), ('relations', 0.064), ('match', 0.063), ('system', 0.061), ('usa', 0.061), ('kouylekov', 0.06), ('grey', 0.06), ('square', 0.059), ('roles', 0.057), ('costs', 0.056), ('neighbours', 0.056), ('ali', 0.056), ('query', 0.055), ('sorted', 0.054), ('dublin', 0.053), ('propbank', 0.052), ('accuracy', 0.05), ('alignments', 0.05), ('pos', 0.05), ('entailment', 0.05), ('structures', 0.049), ('editing', 0.048), ('thirteenth', 0.048), ('traversal', 0.048), ('ancestors', 0.048), ('predicted', 0.048), ('end', 0.047), ('adjusting', 0.047), ('list', 0.046), ('punyakanok', 0.045), ('dennis', 0.045), ('frame', 0.045), ('testing', 0.044), ('distances', 0.044), ('inserting', 0.042), ('binary', 0.042), ('alignment', 0.042), ('deleting', 0.041), ('haji', 0.04), ('delete', 0.04), ('multiple', 0.039), ('inconsistent', 0.039), ('martin', 0.038), ('whole', 0.037), ('college', 0.037), ('sum', 0.035), ('edit', 0.034), ('post', 0.034), ('role', 0.034), ('attempts', 0.034), ('answering', 0.033), ('treating', 0.033), ('jan', 0.033), ('bot', 0.032), ('nod', 0.032), ('arrow', 0.032), ('daniele', 0.032), ('neighbouring', 0.032), ('pighin', 0.032), ('rising', 0.032), ('surprising', 0.031), ('consistency', 0.031), ('zhang', 0.031)]
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