Argumentation mining aims at automatically extracting the premises-claim discourse structures in natural language texts. There is a great demand for argumentation corpora for customer reviews. However, due to the controversial nature of the argumentation annotation task, there exist very few large-scale argumentation corpora for customer reviews. In this work, we novelly use the crowdsourcing technique to collect argumentation annotations in Chinese hotel reviews. As the first Chinese argumentation dataset, our corpus includes 4814 argument component annotations and 411 argument relation annotations, and its annotations qualities are comparable to some widely used argumentation corpora in other languages.
A comprehensive review on argumentation corpora is beyond the scope of this paper; good overviews can be found in e.g. @cite_18 @cite_15 @cite_5 . Here we only review argumentation corpora constructed from customer reviews. @cite_2 built the corpus, consisting of 2.1k hotel reviews posted on Tripadvisor.com. Instead of directly labelling arguments, they annotate and sentiments polarities. A statement is ''. They designed a rule-based tool to segment statements, and employed crowdsourcing to annotate the sentiment and (e.g. location, services, facilities) in each statement. Results suggested that crowdsourcing workers can reliably identify the sentiments of statements (approval rate 72.8 (rejection rate 43.3 view ArguAna as an intermediate resource for building argumentation corpora, because each argument usually contains several statements (e.g. one positive negative statement serving as the claim, and several neutral statements serving as premises). Thus, ArguAna cannot be directly used for training argumentation mining techniques.
@cite_5 @cite_18 @cite_2 @cite_15
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The analysis of user reviews has become critical in research and industry, as user reviews increasingly impact the reputation of products and services. Many review texts comprise an involved argumentation with facts and opinions on different product features or aspects. Therefore, classifying sentiment polarity does not suffice to capture a review's impact. We claim that an argumentation analysis is needed, including opinion summarization, sentiment score prediction, and others. Since existing language resources to drive such research are missing, we have designed the ArguAna TripAdvisor corpus, which compiles 2,100 manually annotated hotel reviews balanced with respect to the reviews' sentiment scores. Each review text is segmented into facts, positive, and negative opinions, while all hotel aspects and amenities are marked. In this paper, we present the design and a first study of the corpus. We reveal patterns of local sentiment that correlate with sentiment scores, thereby defining a promising starting point for an effective argumentation analysis. Abstract Documents in computer-readable form can be used to provide information about other documents, i.e. those they cite. To do this efficiently requires procedures for computer recognition of citing statements. This is not easy, especially for multi-sentence citing statements. Computer recognition procedures have been developed which are accurate to the following extent: 73 of the words in statements selected by computer procedures as being citing statements are words which are correctly attributable to the corresponding documents. The retrieval effectiveness of computer-recognized citing statements was tested in the following way. First, for eight retrieval requests in inorganiic chemistry, average recall by search of Chemical Abstracts Service indexing and Chemical Abstracts abstract text words was found to be 50 . Words from citing statements referring to the papers to be retrieved were then added to the index terms and abstract words as additional access points, and searching was repeated. Average recall increased to 70 . Only words from citing statements published within a year of the cited papers were used. The retrieval effect of citing statement words alone (published within a year) without index or abstract terms was the following: average recall was 40 . When just the words of the titles of the cited papers were added to those citing statement words, average recall increased to 50 . The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature. This paper presents preliminary work on identification of argumentation schemes, i.e., identifying premises, conclusion and name of argumentation scheme, in arguments for scientific claims in genetics research articles. The goal is to develop annotation guidelines for creating corpora for argumentation mining research. This paper gives the specification of ten semantically distinct argumentation schemes based on analysis of argumentation in several journal articles. In addition, it presents an empirical study on readers ability to recognize some of the argumentation schemes.