Building an Argument Search Engine for the Web

Wachsmuth, Henning; Potthast, Martin; Khatib, Khalid Al; Ajjour, Yamen; Puschmann, Jana; Qu, Jiani; Dorsch, Jonas; Morari, Viorel; Bevendorff, Janek; Stein, Benno

doi:10.18653/v1/w17-5106

Cited by 119 publications

(101 citation statements)

References 29 publications

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“…3 As part of the development of Speech by Crowd, 6.3k arguments were collected from contributors of various levels, and are released as part of this work. An important sub-task of such a service is the automatic assessment of argument quality, which has already shown its importance for prospective applications such as automated decision making (Bench-Capon et al, 2009), argument search (Wachsmuth et al, 2017b), and writing support (Stab and Gurevych, 2014). Identifying argument quality in the context of Speech by Crowd allows for the top-quality arguments to surface out of many contributions.…”

Section: Introductionmentioning

confidence: 99%

Automatic Argument Quality Assessment - New Datasets and Methods

Toledo¹,

Gretz²,

Cohen-Karlik³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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We explore the task of automatic assessment of argument quality. To that end, we actively collected 6.3k arguments, more than a factor of five compared to previously examined data. Each argument was explicitly and carefully annotated for its quality. In addition, 14k pairs of arguments were annotated independently, identifying the higher quality argument in each pair. In spite of the inherent subjective nature of the task, both annotation schemes led to surprisingly consistent results. We release the labeled datasets to the community. Furthermore, we suggest neural methods based on a recently released language model, for argument ranking as well as for argument-pair classification. In the former task, our results are comparable to state-of-the-art; in the latter task our results significantly outperform earlier methods. * These authors equally contributed to this work. 1 For more details:https://www.research. ibm.com/artificial-intelligence/ project-debater/live/

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Section: Introductionmentioning

confidence: 99%

Automatic Argument Quality Assessment - New Datasets and Methods

Toledo¹,

Gretz²,

Cohen-Karlik³

et al. 2019

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conferen

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“…Therefore, the authors came to the conclusion that certain argument components (backing, warrant) as introduced in [37], and other argumentation schemes are often only stated implicitly in common argumentation documents on the internet. In more recent work, argumentation schemes became simpler and more flexible [34,42]. This enables broader applicability and topic-dependent argument search across multiple text types.…”

Section: Related Workmentioning

confidence: 99%

TACAM: Topic And Context Aware Argument Mining

Fromm¹,

Faerman²,

Seidl³

2019

IEEE/WIC/ACM International Conference on Web Intelligence

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In this work we address the problem of argument search. The purpose of argument search is the distillation of pro and contra arguments for requested topics from large text corpora. In previous works, the usual approach is to use a standard search engine to extract text parts which are relevant to the given topic and subsequently use an argument recognition algorithm to select arguments from them. The main challenge in the argument recognition task, which is also known as argument mining, is that often sentences containing arguments are structurally similar to purely informative sentences without any stance about the topic. In fact, they only differ semantically. Most approaches use topic or search term information only for the first search step and therefore assume that arguments can be classified independently of a topic. We argue that topic information is crucial for argument mining, since the topic defines the semantic context of an argument. Precisely, we propose different models for the classification of arguments, which take information about a topic of an argument into account. Moreover, to enrich the context of a topic and to let models understand the context of the potential argument better, we integrate information from different external sources such as Knowledge Graphs or pre-trained NLP models. Our evaluation shows that considering topic information, especially in connection with external information, provides a significant performance boost for the argument mining task.

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“…The DiGAT tool [109] has been developed alongside with an annotation scheme and a graph-based inter-annotator agreement measure based on semantic similarity. Similarly to GraPAT, DiGAT also relies on graph structures for Tool Web UI Manual Annotation Arg Retrieval Arg Evaluation WebAnno [112] Yes Yes BRAT [102] Yes Yes GraPAT [105] Yes Yes DiGAT [109] Yes Yes MARGOT [113] Yes Yes Yes OVA+ [114] Yes Yes TOAST [115] Yes Yes Yes GATE Temware [108] Yes Yes Args [116] Yes Yes Yes ArgumenText [117] Yes Yes Yes Rationale [118] Yes Yes the annotation process, aiming at simple and accurate annotation of relations between entities in long text. The establishment of the TextCoop platform alongside the Dislog language is presented in [110].…”

Section: General-purpose Nlp Toolsmentioning

confidence: 99%

“…The existing tools can be classified into three categories, tools that aid the manual annotation process, general-purpose NLP tools and tools that offer an entire mechanism for argument search, retrieval and evaluation. It has to be underlined the evaluation of the argument differs to the different approaches, as in [113] the number of claims and premises are presented, in [115] the weight of the argument is calculated and in [116,117] the arguments are categorized as pro and con.…”

Section: Argument Search Retrieval and Automatic Annotationmentioning

confidence: 99%

The evolution of argumentation mining: From models to social media and emerging tools

Lytos

Λάγκας²,

Sarigiannidis

et al. 2019

Information Processing & Management

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Argumentation mining is a rising subject in the computational linguistics domain focusing on extracting structured arguments from natural text, often from unstructured or noisy text. The initial approaches on modeling arguments was aiming to identify a flawless argument on specific fields (Law, Scientific Papers) serving specific needs (completeness, effectiveness). With the emerge of Web 2.0 and the explosion in the use of social media both the diffusion of the data and the argument structure have changed. In this survey article, we bridge the gap between theoretical approaches of argumentation mining and pragmatic schemes that satisfy the needs of social media generated data, recognizing the need for adapting more flexible and expandable schemes, capable to adjust to the argumentation conditions that exist in social media. We review, compare, and classify existing approaches, techniques and tools, identifying the positive outcome of combining tasks and features, and eventually propose a conceptual architecture framework. The proposed theoretical framework is an argumentation mining scheme able to identify the distinct sub-tasks and capture the needs of social media text, revealing the need for adopting more flexible and extensible frameworks.

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Building an Argument Search Engine for the Web

Cited by 119 publications

References 29 publications

Automatic Argument Quality Assessment - New Datasets and Methods

Automatic Argument Quality Assessment - New Datasets and Methods

TACAM: Topic And Context Aware Argument Mining

The evolution of argumentation mining: From models to social media and emerging tools

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