5 research outputs found
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Research on the structure of dialogue has been hampered for years because
large dialogue corpora have not been available. This has impacted the dialogue
research community's ability to develop better theories, as well as good off
the shelf tools for dialogue processing. Happily, an increasing amount of
information and opinion exchange occur in natural dialogue in online forums,
where people share their opinions about a vast range of topics. In particular
we are interested in rejection in dialogue, also called disagreement and
denial, where the size of available dialogue corpora, for the first time,
offers an opportunity to empirically test theoretical accounts of the
expression and inference of rejection in dialogue. In this paper, we test
whether topic-independent features motivated by theoretical predictions can be
used to recognize rejection in online forums in a topic independent way. Our
results show that our theoretically motivated features achieve 66% accuracy, an
improvement over a unigram baseline of an absolute 6%.Comment: @inproceedings{Misra2013TopicII, title={Topic Independent
Identification of Agreement and Disagreement in Social Media Dialogue},
author={Amita Misra and Marilyn A. Walker}, booktitle={SIGDIAL Conference},
year={2013}
Variable importance for each species distribution model.
<p>Variable importance for each species distribution model.</p
The response of waterbuck to (a) Soil Adjusted Vegetation Index (SAVI), (b) distance from roads, (c) distance from settlements, (d) distance from water, (e) distance from nearest sighting and (f) fire frequency.
<p>Dotted lines represent the logistic threshold of equal training sensitivity and specificity.</p
Location of the Gonarezhou National Park (GNP) in south-eastern Zimbabwe showing presence-only location data for five grazer species.
<p>Location of the Gonarezhou National Park (GNP) in south-eastern Zimbabwe showing presence-only location data for five grazer species.</p
Pearson correlation matrix for predictor variables.
<p>Pearson correlation matrix for predictor variables.</p