Classifying Arguments by Scheme
Vanessa Wei Feng Graeme Hirst Department of Computer Science Department of Computer Science University of Toronto University of Toronto Toronto, ON, M5S 3G4, Canada Toronto, ON, M5S 3G4, Canada [email protected] [email protected]
Abstract Another premise is left implicit — “Adhering to those treaties and covenants is a means of realizing Argumentation schemes are structures or tem- survival of the entire world”. This proposition is an plates for various kinds of arguments. Given enthymeme of this argument. the text of an argument with premises and con- Our ultimate goal is to reconstruct the en- clusion identified, we classify it as an instance thymemes in an argument, because determining of one of five common schemes, using features specific to each scheme. We achieve accura- these unstated assumptions is an integral part of un- cies of 63–91% in one-against-others classifi- derstanding, supporting, or attacking an entire argu- cation and 80–94% in pairwise classification ment. Hence reconstructing enthymemes is an im- (baseline = 50% in both cases). portant problem in argument understanding. We be- lieve that first identifying the particular argumenta- tion scheme that an argument is using will help to 1 Introduction bridge the gap between stated and unstated proposi- tions in the argument, because each argumentation We investigate a new task in the computational anal- scheme is a relatively fixed “template” for arguing. ysis of arguments: the classification of arguments That is, given an argument, we first classify its ar- by the argumentation schemes that they use. An ar- gumentation scheme; then we fit the stated proposi- gumentation scheme, informally, is a framework or tions into the corresponding template; and from this structure for a (possibly defeasible) argument; we we infer the enthymemes. will give a more-formal definition and examples in Section 3. Our work is motivated by the need to de- In this paper, we present an argument scheme termine the unstated (or implicitly stated) premises classification system as a stage following argument that arguments written in natural language normally detection and proposition classification. First in Sec- draw on. Such premises are called enthymemes. tion 2 and Section 3, we introduce the background to our work, including related work in this field, For instance, the argument in Example 1 consists the two core concepts of argumentation schemes and of one explicit premise (the first sentence) and a con- scheme-sets, and the Araucaria dataset. In Section 4 clusion (the second sentence): and Section 5 we present our classification system, Example 1 [Premise:] The survival of the entire including the overall framework, data preprocessing, world is at stake. feature selection, and the experimental setups. In [Conclusion:] The treaties and covenants aiming the remaining section, we present the essential ap- for a world free of nuclear arsenals and other con- proaches to solve the leftover problems of this paper ventional and biological weapons of mass destruc- which we will study in our future work, and discuss tion should be adhered to scrupulously by all na- the experimental results, and potential directions for tions. future work.
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Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 987–996, Portland, Oregon, June 19-24, 2011. c 2011 Association for Computational Linguistics 2 Related work determine how the pieces fit together as an instance of an argumentation scheme. Argumentation has not received a great deal of at- tention in computational linguistics, although it has 3 Argumentation schemes, scheme-sets, been a topic of interest for many years. Cohen and annotation (1987) presented a computational model of argu- mentative discourse. Dick (1987; 1991a; 1991b) de- 3.1 Definition and examples veloped a representation for retrieval of judicial de- Argumentation schemes are structures or templates cisions by the structure of their legal argument — a for forms of arguments. The arguments need not be necessity for finding legal precedents independent of deductive or inductive; on the contrary, most argu- their domain. However, at that time no corpus of ar- mentation schemes are for presumptive or defeasible guments was available, so Dick’s system was purely arguments (Walton and Reed, 2002). For example, theoretical. Recently, the Araucaria project at Uni- argument from cause to effect is a commonly used versity of Dundee has developed a software tool for scheme in everyday arguments. A list of such argu- manual argument analysis, with a point-and-click in- mentation schemes is called a scheme-set. terface for users to reconstruct and diagram an ar- It has been shown that argumentation schemes gument (Reed and Rowe, 2004; Rowe and Reed, are useful in evaluating common arguments as falla- 2008). The project also maintains an online repos- cious or not (van Eemeren and Grootendorst, 1992). itory, called AraucariaDB, of marked-up naturally In order to judge the weakness of an argument, a set occurring arguments collected by annotators world- of critical questions are asked according to the par- wide, which can be used as an experimental corpus ticular scheme that the argument is using, and the for automatic argumentation analysis (for details see argument is regarded as valid if it matches all the Section 3.2). requirements imposed by the scheme. Recent work on argument interpretation includes Walton’s set of 65 argumentation schemes (Wal- that of George, Zukerman, and Nieman (2007), who ton et al., 2008) is one of the best-developed scheme- interpret constructed-example arguments (not natu- sets in argumentation theory. The five schemes de- rally occurring text) as Bayesian networks. Other fined in Table 1 are the most commonly used ones, contemporary research has looked at the automatic and they are the focus of the scheme classification detection of arguments in text and the classification system that we will describe in this paper. of premises and conclusions. The work closest to ours is perhaps that of Mochales and Moens (2007; 3.2 Araucaria dataset 2008; 2009a; 2009b). In their early work, they fo- One of the challenges for automatic argumentation cused on automatic detection of arguments in legal analysis is that suitable annotated corpora are still texts. With each sentence represented as a vector of very rare, in spite of work by many researchers. shallow features, they trained a multinomial na¨ıve In the work described here, we use the Araucaria Bayes classifier and a maximum entropy model on database1, an online repository of arguments, as our the Araucaria corpus, and obtained a best average experimental dataset. Araucaria includes approxi- accuracy of 73.75%. In their follow-up work, they mately 660 manually annotated arguments from var- trained a support vector machine to further classify ious sources, such as newspapers and court cases, each argumentative clause into a premise or a con- and keeps growing. Although Araucaria has sev- clusion, with an F1 measure of 68.12% and 74.07% eral limitations, such as rather small size and low respectively. In addition, their context-free grammar agreement among annotators2, it is nonetheless one for argumentation structure parsing obtained around of the best argumentative corpora available to date. 60% accuracy. Our work is “downstream” from that of Mochales 1http://araucaria.computing.dundee.ac.uk/doku.php# and Moens. Assuming the eventual success of their, araucaria argumentation corpus 2The developers of Araucaria did not report on inter- or others’, research program on detecting and clas- annotator agreement, probably because some arguments are an- sifying the components of an argument, we seek to notated by only one commentator.
988 Argument from example are annotated as “missing = yes”. Premise: In this particular case, the individual a Example 2 Example of argument markup from has property F and also property G. Araucaria Conclusion: Therefore, generally, if x has prop-
989 TEXT Detecting thermore, we remove all enthymemes that have been argumentative text inserted by the annotator and ignore any argument with a missing conclusion, since the input to our pro- ARGUMENTATIVE SEGMENT posed classifier, as depicted in Figure 1, cannot have any access to unstated argumentative propositions. Premise / conclusion The resulting preprocessed dataset is composed of classifier 393 arguments, of which 149, 106, 53, 44, and 41 respectively belong to the five schemes in the order PREMISE #1 CONCLUSION shown in Table 1. PREMISE #2 4.3 Feature selection Scheme classifier The features used in our work fall into two cat- egories: general features and scheme-specific fea- ARGUMENTATION SCHEME tures. 4.3.1 General features Argument template fitter General features are applicable to arguments belong- ing to any of the five schemes (shown in Table 2). CONSTRUCTED ENTHYMEME For the features conLoc, premLoc, gap, and lenRat, we have two versions, differing in terms of their basic measurement unit: sentence-based Figure 1: Overall framework of this research. and token-based. The final feature, type, indicates whether the premises contribute to the conclusion overall framework: its input is the extracted con- in a linked or convergent order. A linked argument clusion and premise(s) determined by an argument (LA) is one that has two or more inter-dependent detector, followed by a premise / conclusion classi- premise propositions, all of which are necessary to fier, given an unknown text as the input to the entire make the conclusion valid, whereas in a conver- system. And the portion below the dashed round- gent argument (CA) exactly one premise proposi- rectangle represents our long-term goal — to recon- tion is sufficient to do so. Since it is observed that struct the implicit premise(s) in an argument, given there exists a strong correlation between type and its argumentation scheme and its explicit conclusion the particular scheme employed while arguing, we and premise(s) as input. Since argument detection believe type can be a good indicator of argumenta- and classification are not the topic of this paper, we tion scheme. However, although this feature is avail- assume here that the input conclusion and premise(s) able to us because it is included in the Araucaria an- have already been retrieved, segmented, and classi- notations, its value cannot be obtained from raw text fied, as for example by the methods of Mochales and as easily as other features mentioned above; but it is Moens (see Section 2 above). And the scheme tem- possible that we will in the future be able to deter- plate fitter is the topic of our on-going work. mine it automatically by taking advantage of some scheme-independent cues such as the discourse re- 4.2 Data preprocessing lation between the conclusion and the premises. From all arguments in Araucaria, we first ex- tract those annotated in accordance with Walton’s 4.3.2 Scheme-specific features scheme-set. Then we break each complex AU Scheme-specific features are different for each node into several simple AUs where no conclusion scheme, since each scheme has its own cue phrases or premise proposition nodes have embedded AU or patterns. The features for each scheme are shown nodes. From these generated simple arguments, we in Table 3 (for complete lists of features see Feng extract those whose scheme falls into one of the five (2010)). In our experiments in Section 5 below, all most frequent schemes as described in Table 1. Fur- these features are computed for all arguments; but
990 conLoc: the location (in token or sentence) of the Argument from example conclusion in the text. 8 keywords and phrases including for example, premLoc: the location (in token or sentence) of such as, for instance, etc.; 3 punctuation cues: “:”, the first premise proposition. “;”, and “—”. conFirst : whether the conclusion appears before Argument from cause to effect the first premise proposition. 22 keywords and simple cue phrases including re- gap: the interval (in token or sentence) between sult, related to, lead to, etc.; 10 causal and non- the conclusion and the first premise proposi- causal relation patterns extracted from WordNet tion. (Girju, 2003). lenRat: the ratio of the length (in token or sen- tence) of the premise(s) to that of the conclu- Practical reasoning sion. 28 keywords and phrases including want, aim, ob- numPrem: the number of explicit premise propo- jective, etc.; 4 modal verbs: should, could, must, sitions (PROP nodes) in the argument. and need; 4 patterns including imperatives and in- type: type of argumentation structure, i.e., linked finitives indicating the goal of the speaker. or convergent. Argument from consequences The counts of positive and negative propositions Table 2: List of general features. in the conclusion and premises, calculated from the General Inquirer2. the features for any particular scheme are used only Argument from verbal classification when it is the subject of a particular task. For ex- The maximal similarity between the central word ample, when we classify argument from example pairs extracted from the conclusion and the in a one-against-others setup, we use the scheme- premise; the counts of copula, expletive, and neg- specific features of that scheme for all arguments; ative modifier dependency relations returned by when we classify argument from example against the Stanford parser3 in the conclusion and the argument from cause to effect, we use the scheme- premise. specific features of those two schemes. 2 http://www.wjh.harvard.edu/∼inquirer/ For the first three schemes (argument from ex- 3 http://nlp.stanford.edu/software/lex-parser.shtml ample, argument from cause to effect, and practi- cal reasoning), the scheme-specific features are se- Table 3: List of scheme-specific features. lected cue phrases or patterns that are believed to be indicative of each scheme. Since these cue phrases and patterns have differing qualities in terms of their Here mi is the number of scheme-specific cue precision and recall, we do not treat them all equally. phrases designed for schemei; P (schemei|cpk) is the For each cue phrase or pattern, we compute “confi- prior probability that the argument A actually be- dence”, the degree of belief that the argument of in- longs to schemei, given that some particular cue terest belongs to a particular scheme, using the dis- phrase cpk is found in A; dik is a value indicat- tribution characteristics of the cue phrase or pattern ing whether cpk is found in A; and the normaliza- in the corpus, as described below. tion factor N is the number of scheme-specific cue For each argument A, a vector CV = {c1, c2, c3} phrase patterns designed for schemei with at least is added to its feature set, where each ci indicates one support (at least one of the arguments belonging the “confidence” of the existence of the specific fea- to schemei contains that cue phrase). There are two tures associated with each of the first three schemes, ways to calculate dik, Boolean and count: in Boolean schemei. This is defined in Equation 1: mode, dik is treated as 1 if A matches cpk; in count mode, d equals to the number of times A matches m ik 1 Xi c = (P (scheme |cp ) · d ) (1) cpk; and in both modes, dik is treated as 0 if cpk is i N i k ik A k=1 not found in .
991 For argument from consequences, since the arguer ness of each scheme’s specific features. has an obvious preference for some particular con- sequence, sentiment orientation can be a good in- Pairwise classification A pairwise classifier is dicator for this scheme, which is quantified by the constructed for each of the ten possible pairings counts of positive and negative propositions in the of the five schemes, using the general features and conclusion and premise. the scheme-specific features of the two schemes in For argument from verbal classification, there ex- the pair. For each of the ten classifiers, the train- ists a hypernymy-like relation between some pair of ing dataset is divided equally into arguments be- scheme propositions (entities, concepts, or actions) located longing to 1 and arguments belonging to scheme scheme scheme in the conclusion and the premise respectively. The 2, where 1 and 2 are two dif- existence of such a relation is quantified by the max- ferent schemes among the five. Only features asso- scheme scheme imal Jiang-Conrath Similarity (Jiang and Conrath, ciated with 1 and 2 are used. 1997) between the “central word” pairs extracted 5.2 Evaluation from the conclusion and the premise. We parse each sentence of the argument with the Stanford depen- We experiment with different combinations of gen- dency parser, and a word or phrase is considered to eral features and scheme-specific features (discussed be a central word if it is the dependent or governor of in Section 4.3). To evaluate each experiment, we several particular dependency relations, which basi- use the average accuracy over 10 pools of randomly 6 cally represents the attribute or the action of an en- sampled data (each with baseline at 50% ) with 10- tity in a sentence, or the entity itself. For example, fold cross-validation. if a word or phrase is the dependent of the depen- 6 Results dency relation agent, it is therefore considered as a “central word”. In addition, an arguer tends to use We first present the best average accuracy (BAA) of several particular syntactic structures (copula, exple- each classification setup. Then we demonstrate the tive, and negative modifier) when using this scheme, impact of the feature type (convergent or linked ar- which can be quantified by the counts of those spe- gument) on BAAs for different classification setups, cial relations in the conclusion and the premise(s). since we believe type is strongly correlated with the particular argumentation scheme and its value is 5 Experiments the only one directly retrieved from the annotations 5.1 Training of the training corpus. For more details, see Feng (2010). We experiment with two kinds of classification: one- against-others and pairwise. We build a pruned 6.1 BAAs of each classification setup C4.5 decision tree (Quinlan, 1993) for each different classification setup, implemented by Weka Toolkit target scheme BAA dik base type 5 3.6 (Hall et al., 2009). example 90.6 count token yes One-against-others classification A one-against- cause 70.4 Boolean token no others classifier is constructed for each of the five / count most frequent schemes, using the general features reasoning 90.8 count sentence yes and the scheme-specific features for the scheme of consequences 62.9 – sentence yes interest. For each classifier, there are two possi- classification 63.2 – token yes ble outcomes: target scheme and other; 50% of the training dataset is arguments associated with tar- Table 4: Best average accuracies (BAAs) (%) of one- against-others classification. get scheme, while the rest is arguments of all the other schemes, which are treated as other. One- against-other classification thus tests the effective- 6We also experiment with using general features only, but the results are consistently below or around the sampling base- 5http://cs.waikato.ac.nz/ml/weka line of 50%; therefore, we do not use them as a baseline here.
992 example cause reason- conse- performance (64.0%) only for the pair argument ing quences from consequences and argument from verbal clas- cause 80.6 sification; perhaps not coincidentally, these are the reasoning 93.1 94.2 two schemes for which performance was poorest in consequences 86.9 86.7 97.9 the one-against-others task. classification 86.0 85.6 98.3 64.2 6.2 Impact of type on classification accuracy Table 5: Best average accuracies (BAAs) (%) of pairwise As we can see from Table 6, for one-against-others classification. classifications, incorporating type into the feature vectors improves classification accuracy in most Table 4 presents the best average accuracies of cases: the only exception is that the best average ac- one-against-others classification for each of the five curacy of one-against-others classification between schemes. The subsequent three columns list the argument from cause to effect and others is obtained particular strategies of features incorporation under without involving type into the feature vector — which those BAAs are achieved (the complete set of but the difference is negligible, i.e., 0.5 percent- possible choices is given in Section 4.3.): age points with respect to the average difference. Type also has a relatively small impact on argument • d : Boolean or count — the strategy of com- ik from verbal classification (2.6 points), compared to bining scheme-specific cue phrases or patterns its impact on argument from example (22.3 points), using either Boolean or count for d . ik practical reasoning (8.1 points), and argument from • base: sentence or token — the basic unit of ap- consequences (7.5 points), in terms of the maximal plying location- or length-related general fea- differences. tures. Similarly, for pairwise classifications, as shown • type: yes or no — whether type (convergent or in Table 7, type has significant impact on BAAs, es- linked argument) is incorporated into the fea- pecially on the pairs of practical reasoning versus ture set. argument from cause to effect (17.4 points), prac- tical reasoning versus argument from example (22.6 As Table 4 shows, one-against-others classifica- points), and argument from verbal classification ver- tion achieves high accuracy for argument from ex- sus argument from example (20.2 points), in terms ample and practical reasoning: 90.6% and 90.8%. of the maximal differences; but it has a relatively The BAA of argument from cause to effect is only small impact on argument from consequences ver- just over 70%. However, with the last two schemes sus argument from cause to effect (0.8 point), and (argument from consequences and argument from argument from verbal classification versus argument verbal classification), accuracy is only in the low from consequences (1.1 points), in terms of average 60s; there is little improvement of our system over differences. the majority baseline of 50%. This is probably due at least partly to the fact that these schemes do not 7 Future Work have such obvious cue phrases or patterns as the other three schemes which therefore may require In future work, we will look at automatically clas- more world knowledge encoded, and also because sifying type (i.e., whether an argument is linked or the available training data for each is relatively small convergent), as type is the only feature directly re- (44 and 41 instances, respectively). The BAA for trieved from annotations in the training corpus that each scheme is achieved with inconsistent choices has a strong impact on improving classification ac- of base and dik, but the accuracies that resulted from curacies. different choices vary only by very little. Automatically classifying type will not be easy, Table 5 shows that our system is able to correctly because sometimes it is subjective to say whether a differentiate between most of the different scheme premise is sufficient by itself to support the conclu- pairs, with accuracies as high as 98%. It has poor sion or not, especially when the argument is about
993 target scheme BAA-t BAA-no t max diff min diff avg diff example 90.6 71.6 22.3 10.6 14.7 cause 70.4 70.9 −0.5 −0.6 −0.5 reasoning 90.8 83.2 8.1 7.5 7.7 consequences 62.9 61.9 7.5 −0.6 4.2 classification 63.2 60.7 2.6 0.4 2.0
Table 6: Accuracy (%) with and without type in one-against-others classification. BAA-t is best average accuracy with type, and BAA-no t is best average accuracy without type. max diff, min diff, and avg diff are maximal, minimal, and average differences between each experimental setup with type and without type while the remaining conditions are the same.
scheme1 scheme2 BAA-t BAA-no t max diff min diff avg diff cause example 80.6 69.7 10.9 7.1 8.7 reasoning example 93.1 73.1 22.8 19.1 20.1 reasoning cause 94.2 80.5 17.4 8.7 13.9 consequences example 86.9 76.0 13.8 6.9 10.1 consequences cause 87.7 86.7 3.8 −1.5 −0.1 consequences reasoning 97.9 97.9 10.6 0.0 0.8 classification example 86.0 74.6 20.2 3.7 7.1 classification cause 85.6 76.8 9.0 3.7 7.1 classification reasoning 98.3 89.3 8.9 4.2 8.3 classification consequences 64.0 60.0 6.5 −1.3 1.1
Table 7: Accuracy (%) with and without type in pairwise classification. Column headings have the same meanings as in Table 6. personal opinions or judgments. So for this task, tasks in information retrieval. This can be best ex- we will initially focus on arguments that are (or at plained by the argument in Example 1, which uses least seem to be) empirical or objective rather than the particular argumentation scheme practical rea- value-based. It will also be non-trivial to deter- soning. mine whether an argument is convergent or linked We want to fit the Premise and the Conclusion of — whether the premises are independent of one an- this argument into the Major premise and the Con- other or not. Cue words and discourse relations be- clusion slots of the definition of practical reasoning tween the premises and the conclusion will be one (see Table 1), and construct the following conceptual helpful factor; for example, besides generally flags mapping relations: an independent premise. And one premise may be regarded as linked to another if either would become 1. Survival of the entire world −→ a goal G an enthymeme if deleted; but determining this in the 2. Adhering to the treaties and covenants aiming general case, without circularity, will be difficult. for a world free of nuclear arsenals and other We will also work on the argument template fitter, conventional and biological weapons of mass which is the final component in our overall frame- destruction −→ action A work. The task of the argument template fitter is to map each explicitly stated conclusion and premise Thereby we will be able to reconstruct the missing into the corresponding position in its scheme tem- Minor premise — the enthymeme in this argument: plate and to extract the information necessary for en- Carrying out adhering to the treaties and thymeme reconstruction. Here we propose a syntax- covenants aiming for a world free of nuclear based approach for this stage, which is similar to arsenals and other conventional and biological
994 weapons of mass destruction is a means of real- tificial Intelligence and Law, pages 106–115, Boston, izing survival of the entire world. May. Judith Dick. 1991a. A Conceptual, Case-relation Repre- 8 Conclusion sentation of Text for Intelligent Retrieval. Ph.D. thesis, Faculty of Library and Information Science, Univer- The argumentation scheme classification system that sity of Toronto, April. we have presented in this paper introduces a new Judith Dick. 1991b. Representation of legal text for con- task in research on argumentation. To the best of ceptual retrieval. In Proceedings, Third International Conference on Artificial Intelligence and Law, pages our knowledge, this is the first attempt to classify 244–252, Oxford, June. argumentation schemes. Vanessa Wei Feng. 2010. Classifying argu- In our experiments, we have focused on the five ments by scheme. Technical report, Depart- most frequently used schemes in Walton’s scheme- ment of Computer Science, University of Toronto, set, and conducted two kinds of classification: in November. http://ftp.cs.toronto.edu/pub/ one-against-others classification, we achieved over gh/Feng-MSc-2010.pdf. 90% best average accuracies for two schemes, with Sarah George, Ingrid Zukerman, and Michael Niemann. other three schemes in the 60s to 70s; and in pair- 2007. Inferences, suppositions and explanatory exten- sions in argument interpretation. User Modeling and wise classification, we obtained 80% to 90% best User-Adapted Interaction, 17(5):439–474. average accuracies for most scheme pairs. The poor Roxana Girju. 2003. Automatic detection of causal re- performance of our classification system on other lations for question answering. In Proceedings of the experimental setups is partly due to the lack of train- ACL 2003 Workshop on Multilingual Summarization ing examples or to insufficient world knowledge. and Question Answering, pages 76–83, Morristown, Completion of our scheme classification system NJ, USA. Association for Computational Linguistics. will be a step towards our ultimate goal of recon- Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. structing the enthymemes in an argument by the pro- 2009. The WEKA data mining software: an update. cedure depicted in Figure 1. Because of the signifi- SIGKDD Explorations Newsletter, 11(1):10–18. cance of enthymemes in reasoning and arguing, this Jay J. Jiang and David W. Conrath. 1997. Semantic is crucial to the goal of understanding arguments. similarity based on corpus statistics and lexical taxon- But given the still-premature state of research of ar- omy. In International Conference Research on Com- gumentation in computational linguistics, there are putational Linguistics (ROCLING X), pages 19–33. many practical issues to deal with first, such as the Joel Katzav and Chris Reed. 2004. On argumentation construction of richer training corpora and improve- schemes and the natural classification of arguments. Argumentation, 18(2):239–259. ment of the performance of each step in the proce- Raquel Mochales and Marie-Francine Moens. 2008. dure. Study on the structure of argumentation in case law. In Proceedings of the 2008 Conference on Legal Knowl- Acknowledgments edge and Information Systems, pages 11–20, Amster- dam, The Netherlands. IOS Press. This work was financially supported by the Natu- Raquel Mochales and Marie-Francine Moens. 2009a. ral Sciences and Engineering Research Council of Argumentation mining: the detection, classification Canada and by the University of Toronto. We are and structure of arguments in text. In ICAIL ’09: Pro- grateful to Suzanne Stevenson for helpful comments ceedings of the 12th International Conference on Arti- and suggestions. ficial Intelligence and Law, pages 98–107, New York, NY, USA. ACM. Raquel Mochales and Marie-Francine Moens. 2009b. References Automatic argumentation detection and its role in law and the semantic web. In Proceedings of the 2009 Robin Cohen. 1987. Analyzing the structure of ar- Conference on Law, Ontologies and the Semantic Web, gumentative discourse. Computational Linguistics, pages 115–129, Amsterdam, The Netherlands. IOS 13(1–2):11–24. Press. Judith Dick. 1987. Conceptual retrieval and case law. Marie-Francine Moens, Erik Boiy, Raquel Mochales In Proceedings, First International Conference on Ar- Palau, and Chris Reed. 2007. Automatic detection
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