Wyner further develops the concept of using argument mining as a way to assist manual analysis in Wyner, Peters, and Price , which describes the development of Argument Workbench, a tool designed to help the analyst reconstruct arguments from textual sources by highlighting a range of discourse indicators, topics used in the text, domain terminology, and speech act terminology.
The tool integrates with the DebateGraph software, 33 to allow the user to produce detailed argument graphs. Having considered the argumentative properties intrinsic to a text span, we now move on to look at identifying how a text span is used in the argument as a whole.
The work of Moens et al. In this case, the examples considered are extended using material from the ECHR; and accuracy of classifying sentences as argument increases to 0. Argument proposition classification is carried out using a maximum entropy model and support vector machine, with F-scores of 0. Again, this work inherits the shortcomings of the earlier research, as the same sentence can be a premise in one context and a conclusion in another. Such contextual restrictions can, however, also be an advantage, allowing, for example, comments on an article to be related to the original article based on their relation to it.
For example, the work of the IBM Debater project in context dependent evidence detection , which automatically detects evidence in Wikipedia articles supporting a given claim Rinott et al. Though it is obviously an oversimplification, it is also possible to reduce the complexity of the task of recognizing the stance of evidence toward claim into a binary classification.
This builds upon previous work in opinion mining as discussed in Section 2. This is carried out on a specially created corpus of user comments, manually annotated with arguments, using a classifier to predict the correct label from the set of five possible labels as shown in Table 2. The model uses textual entailment and semantic textual similarity features with the best models outperforming the baselines and giving a 0.
Although these results give a promising indication of the ability to determine how a comment relates to the argument being made, the topics studied are limited and the training data taken from procon. The ability to identify even such basic contextual properties offers the opportunity to inform the user and aid in both writing and understanding text.
This is again illustrated in Stab and Gurevych b , who aim to identify argument in essays and works toward the long-term goal of integrating argumentation classifiers into writing environments. Two classifiers are described. First, for identifying argument components, a multiclass classification is carried out with each clause classified as major claim, claim, premise, or non-argumentative.
This classifier is trained on a range of feature types, structural features for example the location and punctuation of the argument component , lexical features n -grams, verbs, adverbs, and modals , syntactic features, discourse indicators, and contextual features. Once the argument components have been identified, a second classifier is used to identify argumentative relations support or non-support. The features used are similar to those for classifying the components, but look at the pairings of clauses.
The presented approach achieves This work is further developed in Nguyen and Litman , where the same methodology and data set are used, but a Latent Dirichlet Allocation LDA Blei, Ng, and Jordan topic model is first generated to separate argument and domain keywords.
The output from the LDA algorithm is then post-processed using a minimal seeding of predefined argumentative words to determine argument and domain topics. The same features as Stab and Gurevych b are then used, replacing n -grams with unigrams of argument words, and numbers of argument and domain words. Using this updated feature set, the accuracy is improved for all of the argument component types: MajorClaim from 0. Although these results are promising, the relatively low numbers still highlight the difficulties in distinguishing between Claim and MajorClaim, due to the largely context dependent distinction between the two.
The categories from another theory of argumentation structure due to Toulmin , of Data Claim and Warrant, are similarly difficult to distinguish. Indeed, the theoretical impossibility of completely acontextual identification was explored from first principles by Freeman , who showed that under the appropriate circumstances, the difference between Data and Warrant dissolves.
With appropriate context, however, the distinction becomes operationally important and was the driver for the first shard task in argument mining, conducted at SEMEVAL by Habernal et al. The Argument Reasoning Comprehension Task required systems to use a given premise and conclusion to distinguish between two given alternative potential warrants there is further contextual information available too, with explicitly identified topic and background. Additional info: In , feminists gathered in Atlantic City to protest the Miss America pageant, calling it racist and sexist.
Is this beauty contest bad for women? Argument: Miss America gives honors and education scholarships. And since …, Miss America is good for women. The system should in this example choose option a. Human performance following brief training on this task is at 0.
Although these results seem extremely encouraging, Niven and Kao suggest that this result is entirely accounted for by exploitation of spurious statistical cues in the data set, and that by eliminating the major source of these cues, the maximum performance fell from just 3 points below the average untrained human baseline to essentially random. Niven and Kao counter these effects by the addition of adversarial examples, obtained by negating the claim and inverting the label for each datapoint.
Although the goal of argument mining is the extraction of argumentative structure from natural text, the availability of large quantities of appropriately annotated training data makes this challenging to carry out.
These moves can then be represented as an argument graph, with the nodes representing the propositions expressed in text segments and the edges between them representing different supporting and attacking moves. An agreement between untrained annotators is presented in Peldszus and Stede b. The annotators achieved moderate agreement for certain aspects of the argument graph e.
The annotation process assigns a list of labels to each segment based on different levels. Peldszus tests a range of classifiers to automatically classify role, typegen, and type. The results show that an SVM classifier generally performs best on the most complex labels, suggesting that it deals well with the lower frequencies with which these occur.
The results on the microtext corpus are encouraging, but the artificial nature of its construction means that such results may not generalize well to unrestricted text.
In this section we move on from looking at the identification of clausal properties to the identification of inter-clausal relations. Identifying relations between pairs of propositions is a more complex and nuanced task than identifying the roles that an individual proposition may take.
It is one thing to know, for example, that a given proposition is a premise; much more challenging is to determine also for which conclusion or conclusions it serves as premise.
Approaches to identifying these relations either build upon the prior classification of individual clauses, or aim to extract relations directly. Palau and Moens build upon their classification of each argument sentence as either premise, or conclusion using a context-free grammar, produced by grouping manually derived rules.
This context-free grammar is used to determine the internal structure of each individual argument. The accuracy of classifying sentences as argument or non-argument is 0. The target is specified by a position relative identifier with a numerical offset identifying the targeted segment relative from the position of the current segment. Again the results for identifying the target of a relation maximum F-score of 0.
This same microtext corpus is used in Peldszus and Stede , who look at identifying conflict relations by examining the texts for occurrences of counter-considerations e. Although the work discussed thus far in this section builds upon previous identification of component roles before identifying relations, Cabrio and Villata propose an approach to detect arguments and discover their relationships directly by building on existing work in textual entailment TE Dagan, Glickman, and Magnini In this case, the T-H pair is a pair of arguments expressed by two different users in a dialogue on a certain topic and the TE system returns a judgment entailment or contradiction on the argument pair.
A data set of T-H pairs is created using manually selected topics from Debatepedia, 36 which provides pre-annotated arguments pro or con , and following the criteria defined and by the organizers of the Recognizing Textual Entailment challenge.
The pairs collected for the test set concern completely new topics, never seen by the system, and are provided in their unlabeled form as input. The system uses different approaches to distance computation, providing both edit distance algorithms cost of the edit operations [insert, delete, etc. Each algorithm returns a normalized distance score between 0 and 1. During training, distance scores are used to calculate a threshold that separates entailment from contradiction.
Although these numbers are quite low, this is an interesting result, suggesting that the relationship between topics in an argument gives more of a clue as to how the components relate than does the way in which those components are expressed.
This is carried through in several later works that look at relations between topics and semantic similarity between propositions.
Nguyen and Litman argue that looking at the content of such pairings to determine relationships does not make full use of the information available. They propose an approach that makes use of contextual features extracted from surrounding sentences of source and target components as well as from general topic information. Experimental results show that using both general topic information and features of surrounding sentences are effective, but that predicting an argumentative relation will benefit most from combining these two sets of features.
The machine learning approaches to argument mining discussed so far in this section have all used supervised learning to perform classification; however, unsupervised learning has also been applied to the task. In Lawrence et al. The intuition is that if a proposition is similar to its predecessor then there exists some argumentative link between them, whereas if there is low similarity between a proposition and its predecessor, the author is going back to address a previously made point and, in this case, the proposition is compared to all those preceding it to determine whether they should be connected.
This assumes that the argument is built up as a tree structure in a depth-first manner, where an individual point is pursued fully before returning to address the previous issues. No evidence is given by Niculae et al. It should also be noted that what is being identified here is merely that an inference relationship exists between two propositions, with no indication of the directionality of this inference.
This same approach is implemented in Lawrence and Reed , where the use of LDA topic models is replaced by using WordNet 39 to determine the semantic similarity between propositions. This change is required to overcome the difficulties in generating a topic model when the text being considered is only a short span, such as an online comment or blog post.
The results are comparable to those achieved using LDA, with precision of 0. In this case the thresholds are adjusted to increase precision at the expense of recall, as the output from this method is combined with a range of other approaches to determine the final structure, and as such the failure of this approach to identify all of the connections can be compensated for by the other techniques. A similar approach of assuming a relationship between argument components, if they refer to the same concepts or entities, is used by AFAlpha Carstens, Toni, and Evripidou , which represents customer reviews as trees of arguments, where a child—parent relationship between two sentences is determined if they refer to the same concepts, with the child being the sentence that has been posted later.
Carstens and Toni continue this line of work in Carstens and Toni , focusing on the determination of argumentative relations, and foregoing the decision on whether an isolated piece of text is an argument or not. This focus is based on the observation that the relation to other text is exactly what describes the argumentative function of a particular text span. The paper mentions a number of use cases, describing a method of evaluating claims, by giving a gauge of what proportion of a text argues for or against them.
The important role played by similarity is also exploited by Gemechu and Reed , who borrow notions of aspect, target concept, and opinion from opinion mining, and use these to decompose ADUs down into finer-grained components, and then use similarity measures between these components to identify argument relations.
Such decompositional argument mining not only performs well on diverse single-author arguments outperforming the techniques of Peldszus and Stede on their Microtext corpus, and of Stab and Gurevych on their AAEC corpus but also on arguments situated in dialogue albeit at lower levels of performance: F1 ranging from 0.
Finally, Wachsmuth, Syed, and Stein highlight an interesting link between similarity and argumentative relations. To some extent, this result captures the intuition that argumentative relations occur where something different is being said about the same topic.
The ability to successfully extract premises and conclusions is built upon in Feng and Hirst , which presents the first step in the long-term goal of a method to reconstruct enthymemes, by first, classifying to an argumentation scheme Walton, Reed, and Macagno , then fitting the propositions to the template, and finally, inferring the enthymemes.
For the first step of fitting one of the top five most commonly occurring argumentation schemes to a predetermined argument structure, accuracies of 0. As in Moens et al. The AUs using the top five most common argumentation schemes are then selected, and a classifier is trained on both features specific to each individual scheme and a range of general linguistic features in order to obtain the scheme.
Although these results are promising, and suggest that identifying scheme instances is an achievable task, they do rely on the prior identification of premises and conclusions, as well as the basic structure that they represent.
Although this approach does not identify the roles of individual propositions in the scheme, knowing what type of scheme links a set of propositions is both a useful task in its own right and offers potential for subsequent processing to determine proposition types for each scheme component. This is a substantially easier task once the scheme type is known. Another approach to identifying the occurrence of schemes is given in Lawrence and Reed , where, rather than considering features of the schemes as a whole, the individual scheme components are identified and then grouped together into a scheme instance.
In this case, only two schemes Expert Opinion and Positive Consequences are considered and classifiers trained to identify their individual component premises and conclusion.
By considering the features of the individual types of these components, F-scores between 0. The approach followed by Feng and Hirst is similar in nature to the first steps suggested by Walton , where a six-stage approach to identifying arguments and their schemes is proposed.
The first of these stages is the identification of the arguments occurring in a piece of text; this is followed by identification of specific known argumentation schemes. Walton, however, points out that beyond this initial identification there are likely to be issues differentiating between similar schemes and suggests the development of a corpus of borderline cases to address the issue.
As Walton points out, the automatic identification of argumentation schemes remains a major challenge. As discussed in Section 3. For example, as part of the rule-based tool for semi-automatic identification of argumentative sections in text presented in Wyner et al.
Similarly, Green lists ten custom argumentation schemes targeted at genetics research articles. Green b then explores how argumentation schemes in this domain can be implemented as logic programs in Prolog and used to extract individual arguments. Regardless of the theoretical backdrop, schemes generally introduce as much complexity as they do opportunity from annotation through to automated analysis.
To pick an example from a substantially different theoretical approach, Musi, Ghosh, and Muresan present a novel set of guidelines for the annotation of argument schemes based on the Argumentum Model of Topics Rigotti and Morasso This framework offers a hierarchical taxonomy of argument schemes based on linguistic criteria that are distinctive and applicable to a broad range of contexts, aiming to overcome the challenges in annotating a broad range of schemes.
With the data currently available, the ontologically rich information available in argumentation schemes has been demonstrated to be a powerful component of a robust approach to argument mining. Collaboration among analysts as well as the further development of tools supporting argumentation schemes is essential to growing the data sets required to improve on these techniques.
Clear annotation guidelines and the development of custom argumentation schemes for specific domains will hopefully result in a rapid growth in the material available and further increase the effectiveness of schematic classification. Whereas some of the previously mentioned argument mining techniques have worked with data that is dialogical in nature, such as user comments and online discussion forums, none of these have focused on using the unique features of dialogue to aid in the automatic analysis process, producing an analysis that captures both the argumentative and dialogical structure.
For example, although Pallotta et al. Similarly, there is a large body of work studying the nature of dialogue both in terms of dialogue modeling, which captures the nature and rules of a dialogue, and dialogue management, which takes a more participant-oriented viewpoint in determining what dialogical moves to make Traum However, there is currently little work that puts these models to work enhancing argument mining techniques.
In this section we discuss formalizations of dialogue protocols and then move on to cover the work that has been done to apply this knowledge to argument mining. Such dialogue games have been developed to capture a range of more structured conversations, for example, to facilitate the generation of mathematical proofs Pease et al. These structures can then be used to allow for mixed initiative argumentation Snaith, Lawrence, and Reed , where a combination of human users and software agents representing the arguments made by other people can take part in the same conversation, using retrieval-based methods to select the most relevant response Le, Nguyen, and Nguyen In such scenarios, the contributions of human participants can be interpreted by virtue of their dialogical connections to the discourse, allowing a small step toward mining argument structure from natural language.
Although formally structured dialogues can be captured and exploited in this way, many real world dialogues follow only very limited rules and the challenge of identifying the argumentative structure in free form discussion is complex. However, even very informal dialogues nevertheless provide additional data beyond that available in monologue, which can be used to help constrain the task. Among other such features, Budzynska et al. Illocutionary forces are the speech act type of utterances.
Their automatic recognition in Illocutionary Structure Parsing Budzynska et al. The preliminary results reported in Budzynska et al. Al Khatib et al. As a first step toward determining the best possible move for a participant in a deliberative discussion, Al Khatib et al. Although the classifier achieves low F-scores for Socializing, Recommending an act, and Asking a question, these are the categories with the smallest number of examples in the data set to draw from—83, , and turns, respectively.
These results are encouraging and suggest that with more data, further improvements could be expected. Dialogue transitions, on the other hand, connect together dialogical moves. In Inference Anchoring Theory Budzynska et al. Budzynska et al. There are no results yet reported testing this hypothesis. In much the same way that argumentation schemes capture common patterns of reasoning, rhetorical figures capture common patterns of speech. Although not as implicitly related to argumentative structure as argument schemes, rhetorical figures and argumentation are closely linked.
Fahnestock makes a compelling case for the conception of rhetorical figures as couplings of linguistic form and function. She demonstrates this claim for a specific group of figures related to organization.
Just as study in rhetoric has emphasized the connection to argumentation, similarly there is an emergence of work in argument mining that is considering rhetorical moves. Alliheedi, Mercer, and Cohen , for example, aim to develop a framework to analyze argumentation structure in biochemistry procedures by developing an automated rhetorical move analysis platform.
Harris et al. It is claimed in this work that many figures are formal patterns that algorithms can detect through surface analysis, illustrating this with an example from John F. Ask what you can do for your country. For computational purposes, patterns of form are much easier to detect than conceptual ones Gawryjolek, Di Marco, and Harris ; Dubremetz and Nivre The first work to directly connect rhetorical figure detection to argument mining appears in Lawrence, Visser, and Reed , where the connection between eight rhetorical figures, the forms of which are relatively easy to identify computationally, and their corresponding argumentation structure is explored.
Such instances of figures complement multiple levels of argument mining tasks, reinforcing the move away from a traditional pipeline to a more holistic approach. Although the work on connecting rhetorical figures to argument structure is still at an early stage, it is an example of a technique that works on multiple argumentative levels, complementing existing, more focused approaches.
The task requires identification of a wide range of phenomena, including logical fallacies, 43 techniques appealing to emotions, loaded language, and more San Martino et al. The recent rapid growth in argument mining shows that there is an increasing demand for the automated extraction of deeper meaning from the vast amounts of data that we currently produce.
Although techniques in opinion mining are able to tell us what people are thinking, we also need to be able to say why they hold those opinions. There is substantial commercial opportunity here as businesses increasingly want to build on the data that they gather in order to know more about the thoughts and behaviors of their customers, and it is unsurprising that many of the large players in the field are engaging, most visibly to date, IBM.
One of the first challenges faced by argument mining is the lack of consistently annotated argument data. Much recent work has focused on producing annotation guidelines targeted at specific domains e.
The volume of data, particularly data annotated at the most fine grained level, is still far below what would be required to apply many of the techniques previously discussed in a domain independent manner. Attempts are being made to overcome this lack of data, including the use of crowdsourced annotation Ghosh et al.
As these efforts combine with increasing attention to manual analysis, the volume of data available should increase rapidly. Schulz et al. Even in cases where there is a greater volume of data, conflicting notions of argument are often problematic. In a qualitative analysis of six different, widely used, argument data sets, Daxenberger et al. These results clearly highlight the need for greater effort in building a framework in which argument mining tasks are carried out, covering all aspects from agreement on the argument theoretical concepts being identified, through to uniform presentation of results and data.
A related problem is verifiability and reproducibility of results: As a young field, argument mining does not yet benefit from uniformly publicly available algorithms and codebases that would encourage incremental advance.
Argument mining techniques have been successfully developed to extract details of the argumentative structure expressed within a piece of text, focusing on different levels of argumentative complexity as the domain and task require. For each task, we have considered work carried out using a broad range of techniques, including statistical and linguistic methods. We have presented a hierarchy of task types based on increasing argumentative complexity. First looking at the identification of argument components and the determination of their boundaries, we have then moved on to consider the role of individual clauses both intrinsic, such as whether the clause is reported speech, and contextual, such as whether the clause is the conclusion to an argument.
The success of these techniques and the development of techniques for analyzing dialogical argument offers hope that techniques can be developed for automatically identifying complex illocutionary structures and the argumentative structures they build.
We have also seen how these techniques can be combined, tying together statistical identification of basic structure and linguistic markers and identifying scheme components. In so doing, the resulting argument structures offer a more complete analysis of the text than any of these methods provide on their own. Argument mining remains profoundly challenging, and traditional methods on their own seem to need to be complemented by stronger, knowledge-driven analysis and processing.
However, the pieces required to successfully automate the process of turning unstructured data into structured argument are starting to take shape. As the volume of analyzed argument continues to increase, and existing techniques are further developed and brought together, rapid progress can be expected.
F-score refers to the equally weighted harmonic mean of the precision and recall measured for a system. When the system is applied to several sets of data, the micro-average F-score is obtained by first summing up the individual true positives, false positives, and false negatives and then calculating precision and recall using these figures, whereas the macro-average F-score is calculated by averaging the precision and recall of the system on the individual sets van Rijsbergen The precision is calculated based upon a user study where the participants are asked to confirm if an issue is controversial; as such, recall is not reported.
An interpretation of kappa values is offered by Landis and Koch , who describe values between 0. There are many classes of examples that do not fit the binary model well—situations, such as elections, with more than two candidates; political configurations in which factions within parties express extreme positions, etc.
Available at www. Of course, referring to logical fallacies as propaganda techniques is highly controversial, not least because the boundary between fallacies and schemes is such a fine one Walton Hamblin and Groarke, Tindale, and Fisher represent a good introduction to the literature on fallacies. Sign In or Create an Account. Advanced Search. User Tools. Sign In. Skip Nav Destination Article Navigation. Close mobile search navigation Article navigation.
Volume 45, Issue 4. Previous Article Next Article. Foundational Areas and Techniques. Manual Argument Analysis. Argument Data. Argument Mining: Automating Argument Analysis. Identifying Argument Components. Automatic Identification of Clausal Properties. Automatic Identification of Relational Properties. Article Navigation.
January 01 This Site. Google Scholar. Chris Reed Chris Reed. Author and Article Information. John Lawrence. Chris Reed. Received: August 02 Revision Received: August 11 Accepted: September 15 Online Issn: Computational Linguistics 45 4 : — Article history Received:. Revision Received:. Cite Icon Cite. Figure 1. View large Download slide. Figure 2. Minor Premise: E asserts that proposition A is true false. Conclusion: A is true false. Expertise Question: How credible is E as an expert source?
Opinion Question: What did E assert that implies A? Trustworthiness Question: Is E personally reliable as a source? Consistency Question: Is A consistent with what other experts assert? It includes topic annotations, response characterizations, and stance. Organized by research sub-fields. View Large. Figure 3. The tasks and levels of complexity in argument mining techniques. Figure 4. Description: Comment…. Figure 5. Internet argument corpus 2. Transferring knowledge from discourse to arguments: A case study with scientific abstracts.
Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics.
Enhancing natural language search in meeting data with visual meeting overviews. What works and what does not: Classifier and feature analysis for argument mining.
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