11,341 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
Using distributional similarity to organise biomedical terminology
We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy
Multi-Target Prediction: A Unifying View on Problems and Methods
Multi-target prediction (MTP) is concerned with the simultaneous prediction
of multiple target variables of diverse type. Due to its enormous application
potential, it has developed into an active and rapidly expanding research field
that combines several subfields of machine learning, including multivariate
regression, multi-label classification, multi-task learning, dyadic prediction,
zero-shot learning, network inference, and matrix completion. In this paper, we
present a unifying view on MTP problems and methods. First, we formally discuss
commonalities and differences between existing MTP problems. To this end, we
introduce a general framework that covers the above subfields as special cases.
As a second contribution, we provide a structured overview of MTP methods. This
is accomplished by identifying a number of key properties, which distinguish
such methods and determine their suitability for different types of problems.
Finally, we also discuss a few challenges for future research
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