1,766 research outputs found
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
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
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