Evaluating the morphological compositionality of polarity
20389Authors
nan
Abstract
AbstractUnknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.
Keywords
Full Content
nan
Labeled Text
AbstractUnknown words are a challenge for any NLP task, including sentiment analysis. Here, we evaluate the extent to which sentiment polarity of complex words can be predicted based on their morphological make-up. We do this on German as it has very productive processes of derivation and compounding and many German hapax words, which are likely to bear sentiment, are morphologically complex. We present results of supervised classification experiments on new datasets with morphological parses and polarity annotations.
Publication Details
- Paper ID:
- 20389
- Year:
- 2017
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan