Abstract
Attitude prediction strives to determine whether an opinion holder is positive or negative towards a given target. We cast this problem as a lexicon engineering task in the context of deep linguistic grammar formalisms such as LFG or HPSG. Moreover, we demonstrate that attitude prediction can be accomplished solely through unification of lexical feature structures. It is thus possible to use our model without altering existing grammars, only the lexicon needs to be adapted. In this paper, we also show how our model can be combined with dependency parsers. This makes our model independent of the availability of deep grammars, only unification as a processing mean is needed.
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Notes
- 1.
An up arrow inserts a feature into the feature structure defined by the equation.
- 2.
Available from https://pub.cl.uzh.ch/projects/opinion/lrec_data.txt.
- 3.
We also use the parser in [10].
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Klenner, M. (2017). An Unification-Based Model for Attitude Prediction. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_39
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DOI: https://doi.org/10.1007/978-3-319-64206-2_39
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