799 research outputs found
Using Argument-based Features to Predict and Analyse Review Helpfulness
We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusions ratios, are good indicators of helpful reviews. To
validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
and investigate the effectiveness of several combinations of argument-based
features. Experiments suggest that, when being used together with the
argument-based features, the state-of-the-art baseline features can enjoy a
performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201
Using Argument-based Features to Predict and Analyse Review Helpfulness
We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusions ratios, are good indicators of helpful reviews. To
validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
and investigate the effectiveness of several combinations of argument-based
features. Experiments suggest that, when being used together with the
argument-based features, the state-of-the-art baseline features can enjoy a
performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201
Joint RNN Model for Argument Component Boundary Detection
Argument Component Boundary Detection (ACBD) is an important sub-task in
argumentation mining; it aims at identifying the word sequences that constitute
argument components, and is usually considered as the first sub-task in the
argumentation mining pipeline. Existing ACBD methods heavily depend on
task-specific knowledge, and require considerable human efforts on
feature-engineering. To tackle these problems, in this work, we formulate ACBD
as a sequence labeling problem and propose a variety of Recurrent Neural
Network (RNN) based methods, which do not use domain specific or handcrafted
features beyond the relative position of the sentence in the document. In
particular, we propose a novel joint RNN model that can predict whether
sentences are argumentative or not, and use the predicted results to more
precisely detect the argument component boundaries. We evaluate our techniques
on two corpora from two different genres; results suggest that our joint RNN
model obtain the state-of-the-art performance on both datasets.Comment: 6 pages, 3 figures, submitted to IEEE SMC 201
Annotating topics, stance, argumentativeness and claims in Dutch social media comments : a pilot study
One of the major challenges currently facing the field of argumentation mining is the lack of
consensus on how to analyse argumentative user-generated texts such as online comments. The
theoretical motivations underlying the annotation guidelines used to generate labelled corpora
rarely include motivation for the use of a particular theoretical basis. This pilot study reports on
the annotation of a corpus of 100 Dutch user comments made in response to politically-themed
news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement
scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim
labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions
underlying the design of our guidelines. Our analysis of the annotations signal the importance of
adjusting our guidelines to include allowances for missing context information and defining the
concept of argumentativeness in connection with stance. Our annotated corpus and associated
guidelines are made publicly available
Discourse markers in diplomatic setting: Ministerial dialogue between Australia and Indonesia
This descriptive research discusses the use of discourse markers in a diplomatic setting between the governments of Indonesia and Australia during a Joint Press Conference between Indonesia-Australia Foreign and Defence Ministers. The particular aims of this research are to identify and analyze forms of discourse markers employed by the representatives of each government and describe the most frequent discourse markers used by these representatives. The data were collected from the transcript of the Joint Press Conference between Indonesia and Australia Foreign and Defence Ministers (2+2) Dialogue. The data are classified based on the typology of discourse markers and analyzed to identify their function within the diplomatic discourse. The data are input into the AntConc corpus analysis toolkit for analysis. The results show that the Foreign and Defence Ministers of Indonesia employed three forms of discourse markers, namely textual discourse marker, interpersonal discourse marker, and cognitive discourse marker, whereas the Foreign and Defence Ministers of Australia only applied textual discourse marker and cognitive discourse marker. Both representatives employed textual discourse markers more frequently than other forms of discourse markers. Discourse markers partially control how meaning is constructed by showing turns between speakers, joining concepts, displaying attitude, and finally, controlling communication. By understanding the discourse markers in ministerial dialogues, spectators can learn to find clues in the change of direction in their talks to better understand the conversation that affects the policies and citizens of both countries involved
Automatic Detection and Classification of Argument Components using Multi-task Deep Neural Network
International audienceIn this article we propose a novel method for automatically extracting and classifying argument components from raw texts. We introduce a multi-task deep learning framework exploiting weight parameters trained on auxiliary simple tasks, such as Part-Of-Speech tagging or chunking, in order to solve more complex tasks that require a fine-grained understanding of natural language. Interestingly, our results show that the use of advanced deep learning techniques framed in a multi-task setting enables competing with state-of-the-art systems that depend on handcrafted features
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