1 |
Structured Sentiment Analysis as Dependency Graph Parsing ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Transfer and Multi-Task Learning for Noun-Noun Compound Interpretation ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
SDP 2014 & 2015: Broad Coverage Semantic Dependency Parsing ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Combining statistical machine translation and translation memories with domain adaptation
|
|
|
|
In: Läubli, Samuel; Fishel, Mark; Volk, Martin; Weibel, Manuela (2013). Combining statistical machine translation and translation memories with domain adaptation. In: NODALIDA 2013, Nordic Conference of Computational Linguistics, Oslo, Norway, 22 May 2013 - 24 May 2013, 331-341. (2013)
|
|
BASE
|
|
Show details
|
|
11 |
Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement
|
|
|
|
Abstract:
People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features.
|
|
Keyword:
004 Informatik
|
|
URL: https://madoc.bib.uni-mannheim.de/33246/ http://www.ep.liu.se/ecp/085/023/ecp1385023.pdf
|
|
BASE
|
|
Hide details
|
|
|
|