acl acl2010 acl2010-30 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Milen Kouylekov ; Matteo Negri
Abstract: This paper presents a general-purpose open source package for recognizing Textual Entailment. The system implements a collection of algorithms, providing a configurable framework to quickly set up a working environment to experiment with the RTE task. Fast prototyping of new solutions is also allowed by the possibility to extend its modular architecture. We present the tool as a useful resource to approach the Textual Entailment problem, as an instrument for didactic purposes, and as an opportunity to create a collaborative environment to promote research in the field.
Reference: text
sentIndex sentText sentNum sentScore
1 An Open-Source Package for Recognizing Textual Entailment Milen Kouylekov and Matteo Negri FBK - Fondazione Bruno Kessler Via Sommarive 18, 38100 Povo (TN), Italy [ kouylekov ,negri ] @ fbk eu . [sent-1, score-0.169]
2 Abstract This paper presents a general-purpose open source package for recognizing Textual Entailment. [sent-2, score-0.14]
3 The system implements a collection of algorithms, providing a configurable framework to quickly set up a working environment to experiment with the RTE task. [sent-3, score-0.162]
4 Fast prototyping of new solutions is also allowed by the possibility to extend its modular architecture. [sent-4, score-0.135]
5 We present the tool as a useful resource to approach the Textual Entailment problem, as an instrument for didactic purposes, and as an opportunity to create a collaborative environment to promote research in the field. [sent-5, score-0.125]
6 On one side, current RTE technology might not be mature enough to provide reliable components for such integration. [sent-9, score-0.038]
7 On the other side, the lack of available tools makes experimentation with the task, and the fast prototyping of new solutions, particularly difficult. [sent-11, score-0.074]
8 We believe that RTE research would significantly benefit from such availability, since it would allow to quickly set up a working environment for experiments, encourage participation of newcomers, and eventually promote state of the art advances. [sent-13, score-0.187]
9 The main contribution of this paper is to present the latest release of EDITS (Edit Distance Textual Entailment Suite), a freely available, open source software package for recognizing Textual Entailment. [sent-14, score-0.206]
10 The system has been designed following three basic requirements: Modularity. [sent-15, score-0.049]
11 Modules can be composed through a configuration file, and extended as plug-ins according to individual requirements. [sent-17, score-0.107]
12 System’s workflow, the behavior of the basic components, and their IO formats are described in a comprehensive documentation available upon download. [sent-18, score-0.051]
13 In addition, both language dependent and language independent configurations are allowed by algorithms that manipulate different representations of the input data. [sent-21, score-0.154]
14 c 01200 S1y0s Atesmso Dcieamtioonns ftorart Cioonms,p puatagteiso 4n2a–l4 L7in,guistics Figure 1: Entailment Engine, main components and workflow Adaptability. [sent-29, score-0.069]
15 Modules can be tuned over training data to optimize performance along several dimensions (e. [sent-30, score-0.025]
16 overall Accuracy, Precision/Recall trade-off on YES and NO entailment judgements). [sent-32, score-0.425]
17 In addition, an optimization component based on genetic algorithms is available to automatically set parameters starting from a basic configuration. [sent-33, score-0.134]
18 The latest release of the package can be downloaded from http : / /edits fbk eu. [sent-36, score-0.236]
19 System Overview The EDITS package allows to: • • • Create an Entailment Engine (Figure 1) by defining nits E n btaasilimc components (i. [sent-39, score-0.111]
20 EDITS implements a distance-based framework which assumes that the probability of an entailment relation between a given T-H pair is inversely proportional to the distance between T and H (i. [sent-42, score-0.617]
21 Within this framework the system implements and harmonizes different approaches to distance computation, providing both edit distance algorithms, and similarity algorithms (see Section 3 . [sent-45, score-0.675]
22 Each algorithm returns a normalized distance score (a number between 0 and 1). [sent-47, score-0.129]
23 At a training stage, distance scores calculated over annotated T-H pairs are used to estimate a threshold that best separates positive from negative examples. [sent-48, score-0.129]
24 The threshold, which is stored in a Model, is used at a test stage to assign an entailment judgement and a confidence score to each test pair. [sent-49, score-0.493]
25 In the creation of a distance Entailment Engine, algorithms are combined with cost schemes (see Section 3 . [sent-50, score-0.594]
26 3), and optional external knowledge represented as rules (see Section 3. [sent-52, score-0.035]
27 Besides the definition of a single Entailment Engine, a unique feature of EDITS is that it allows for the combination of multiple Entailment Engines in different ways (see Section 4. [sent-54, score-0.028]
28 Pre-defined basic components are already provided with EDITS, allowing to create a variety of entailment engines. [sent-56, score-0.488]
29 Fast prototyping of new solutions is also allowed by the possibility to extend the modular architecture of the system with new algorithms, cost schemes, rules, or plug-ins to new language processing components. [sent-57, score-0.363]
30 3 Basic Components This section overviews the main components of a distance Entailment Engine, namely: i) algorithms, iii) cost schemes, iii) the cost optimizer, and iv) entailment/contradiction rules. [sent-58, score-0.575]
31 1 Algorithms Algorithms are used to compute a distance score between T-H pairs. [sent-60, score-0.129]
32 EDITS provides a set of predefined algorithms, including edit distance algorithms, and similarity algorithms adapted to the proposed distance framework. [sent-61, score-0.694]
33 The choice of the available algorithms is motivated by their large use documented in RTE literature2. [sent-62, score-0.08]
34 Edit distance algorithms cast the RTE task as the problem of mapping the whole content of H into the content of T. [sent-63, score-0.209]
35 Mappings are performed as sequences of editing operations (i. [sent-64, score-0.084]
36 insertion, deletion, substitution of text portions) needed to transform T into H, where each edit operation has a cost associated with it. [sent-66, score-0.506]
37 The distance algorithms available in the current release of the system are: 2Detailed descriptions of all the systems participating in the TAC RTE Challenge are available at http :/ /www. [sent-67, score-0.272]
38 Similarity algorithms are adapted to the EDITS distance framework by transforming measures of the lexical/semantic similarity between T and H into distance measures. [sent-71, score-0.426]
39 These algorithms are also adapted to use the three edit operations to support overlap calculation, and define term weights. [sent-72, score-0.387]
40 2 Cost Schemes Cost schemes are used to define the cost of each edit operation. [sent-76, score-0.581]
41 Cost schemes are defined as XML files that explicitly associate a cost (a positive real number) to each edit operation applied to elements of T and H. [sent-77, score-0.683]
42 For instance, Tree Edit Distance will manipulate nodes in a dependency tree representation, whereas Token Edit Distance and similarity algorithms will manipulate words. [sent-79, score-0.23]
43 =B - substituting A swuibths tBit costs (2A0, Bif) =A2 a0nd if fB A are Bdif -fe sruebnts. [sent-81, score-0.066]
44 t In the distance-based framework adopted by EDITS, the interaction between algorithms and cost schemes plays a central role. [sent-82, score-0.44]
45 Given a T-H pair, in fact, the distance score returned by an algorithm directly depends on the cost of the operations applied to transform T into H (edit distance algorithms), or on the cost of mapping words in H with words in T (similarity algorithms). [sent-83, score-0.754]
46 Such interaction determines the overall behaviour of an Entailment Engine, since distance scores returned by the same algorithm with different cost schemes can be considerably different. [sent-84, score-0.521]
47 This allows users to define (and optimize, as explained in Section 3. [sent-85, score-0.051]
48 3) the cost schemes that best suit the RTE data they want to model3. [sent-86, score-0.36]
49 EDITS provides two predefined cost schemes: • • Simple Cost Scheme - the one shown in Figure 2, setting ficxheedm ceo -st ths efo orn eea schho ewdnit operation. [sent-87, score-0.251]
50 IDF Cost Scheme - insertion and deletion costs Cfoors a Swcohredm w are snsete rtotio othne inndve rdseel edtioocnument frequency of w (IDF(w)). [sent-88, score-0.142]
51 The substitution cost is set to 0 if a word w1 from T and a word w2 from H are the same, and IDF(w1)+IDF(w2) otherwise. [sent-89, score-0.251]
52 3For instance, when dealing with T-H pairs composed by texts that are much longer than the hypotheses (as in the RTE5 Campaign), setting low deletion costs avoids penalization to short Hs fully contained in the Ts. [sent-90, score-0.112]
53 44 In the creation of new cost schemes, users can express edit operation costs, and conditions over the A and B elements, using a meta-language based on a lisp-like syntax (e. [sent-91, score-0.535]
54 The system also provides functions to access data stored in hash files. [sent-94, score-0.089]
55 For example, the IDF Cost Scheme accesses the IDF values of the most frequent 100K English words (calculated on the Brown Corpus) stored in a file distributed with the system. [sent-95, score-0.149]
56 Users can create new hash files to collect statistics about words in other languages, or other information to be used inside the cost scheme. [sent-96, score-0.263]
57 3 Cost Optimizer A cost optimizer is used to adapt cost schemes (either those provided with the system, or new ones defined by the user) to specific datasets. [sent-98, score-0.668]
58 The optimizer is based on cost adaptation through genetic algorithms, as proposed in (Mehdad, 2009). [sent-99, score-0.337]
59 To this aim, cost schemes can be parametrized by externalizing as parameters the edit operations costs. [sent-100, score-0.637]
60 The optimizer iterates over training data using different values ofthese parameters until on optimal set is found (i. [sent-101, score-0.104]
61 lexical, syntactic, semantic) about the probability of entailment or contradiction between elements of T and H. [sent-107, score-0.49]
62 Rules are invoked by cost schemes to influence the cost of substitutions between elements of T and H. [sent-108, score-0.601]
63 Typically, the cost of the substitution between two elements A and B is inversely proportional to the probability that A entails B . [sent-109, score-0.317]
64 Rules are stored in XML files called Rule Repositories, with the format shown in Figure 3. [sent-110, score-0.068]
65 Each rule consists of three parts: i) a left-hand side, ii) a right-hand side, iii) a probability that the left-hand side entails (or contradicts) the righthand side. [sent-111, score-0.027]
66 EDITS provides three predefined sets of lexical entailment rules acquired from lexical resources widely used in RTE: WordNet4 , Lin’s word similarity dictionaries5 , and VerbOcean6 . [sent-112, score-0.565]
67 Figure 3: Example of XML Rule Repository 4 Using the System This section provides basic information about the use of EDITS, which can be run with commands in a Unix Shell. [sent-123, score-0.025]
68 A complete guide to all the parameters of the main script is available as HTML documentation downloadable with the package. [sent-124, score-0.026]
69 1 Input The input of the system is an entailment corpus represented in the EDITS Text Annotation Format (ETAF), a simple XML internal annotation format. [sent-126, score-0.449]
70 ETAF is used to represent both the input T-H pairs, and the entailment and contradiction rules. [sent-127, score-0.453]
71 Plug-ins for several widely used annotation tools (including TreeTagger, Stanford Parser, and OpenNLP) can be downloaded from the system’s website. [sent-129, score-0.032]
72 2 Configuration The creation of an Entailment Engine is done by defining its basic components (algorithms, cost schemes, optimizer, and rules) through an XML configuration file. [sent-133, score-0.399]
73 The configuration file is divided in modules, each having a set of options. [sent-134, score-0.219]
74 Adding external knowledge to an entailment engine can be done by extending the configuration file with a reference to a rules file (e. [sent-136, score-0.892]
75 3 Training and Test Given a configuration file and an RTE corpus annotated in ETAF, the user can run the training procedure to learn a model. [sent-141, score-0.219]
76 overall Accuracy, Precision/Recall trade-off on YES and/or NO entailment judgements). [sent-144, score-0.425]
77 The output of the training phase is a model: a zip file that contains the learned threshold, the configuration file, the cost scheme, and the entailment/contradiction rules used to calculate the threshold. [sent-146, score-0.458]
78 The explicit availability of all this information in the model allows users to share, replicate and modify experiments7. [sent-147, score-0.077]
79 Given a model and an un-annotated RTE corpus as input, the test procedure produces a file containing for each pair: i) the decision of the system (YES, NO), ii) the confidence of the decision, iii) the entailment score, iv) the sequence of edit operations made to calculate the entailment score. [sent-148, score-1.263]
80 This can be done by grouping their definitions as sub-modules in the configuration file. [sent-151, score-0.107]
81 EDITS allows users to define customized combination strategies, or to use two predefined combination modalities provided with the package, 7Our policy is to publish online the models we use for participation in the RTE Challenges. [sent-152, score-0.22]
82 We encourage other users of EDITS to do the same, thus creating a collaborative environment, allow new users to quickly modify working configurations, and replicate results. [sent-153, score-0.215]
83 The two modalities combine in different ways the entailment scores produced by multiple independent engines, and return a final decision for each T-H pair. [sent-155, score-0.456]
84 Linear Combination returns an overall entailment score as the weighted sum of the entailment scores returned by each engine: ! [sent-156, score-0.882]
85 i=0 In this formula, weighti is an ad-hoc weight parameter for each entailment engine. [sent-159, score-0.463]
86 Optimal weight parameters can be determined using the same optimization strategy used to optimize the cost schemes, as described in Section 3 . [sent-160, score-0.229]
87 Classifier Combination is similar to the approach proposed in (Malakasiotis and Androutsopoulos, 2007), and is based on using the entailment scores returned by each engine as features to train a classifier (see Figure 4). [sent-162, score-0.558]
88 By default the plug-in uses an SVM classifier, but other Weka algorithms can be specified as options in the configuration file. [sent-164, score-0.187]
89 The following configuration file describes a combination of two engines (i. [sent-165, score-0.305]
90 nz/ml/weka 9A linear combination can be easily obtained by changing the alias of the highest-level module (“weka”) into “linear”. [sent-173, score-0.028]
91 5 Experiments with EDITS To give an idea of the potentialities of the EDITS package in terms of flexibility and adaptability, this section reports some results achieved in RTE-related tasks by previous versions of the tool. [sent-175, score-0.073]
92 As regards the RTE Challenges, in the last years EDITS has been used to participate both in the PASCAL/TAC RTE Campaigns for the English language (Mehdad et al. [sent-177, score-0.029]
93 In the “Search” task (which consists in finding all the sentences that entail a given H in a given set of documents about a topic) the same configuration achieved an F1 of 33. [sent-183, score-0.107]
94 To promote the use of EDITS and ease experimentation, the complete models used to produce each submitted run can be downloaded with the system. [sent-188, score-0.08]
95 An improved model obtained with the current release of EDITS, and trained over RTE-5 data (61. [sent-189, score-0.039]
96 As regards application-oriented integrations, EDITS has been successfully used as a core component in a Restricted-Domain Question Answering system within the EU-Funded QALL-ME Project10 . [sent-191, score-0.053]
97 In recognizing 14 relations relevant in the CINEMA domain present in a collection of spoken English requests, the system 10http://qallme. [sent-193, score-0.091]
98 6 Conclusion We have presented the first open source package for recognizing Textual Entailment. [sent-197, score-0.14]
99 The system offers a modular, flexible, and adaptable working environment to experiment with the task. [sent-198, score-0.102]
100 In addition, the availability of pre-defined system configurations, tested in the past Evaluation Campaigns, represents a first contribution to set up a collaborative environment, and promote advances in RTE research. [sent-199, score-0.102]
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