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» Improving Language Models by Clustering Training Sentences
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COLING
2010
14 years 11 months ago
Semantic Role Features for Machine Translation
We propose semantic role features for a Tree-to-String transducer to model the reordering/deletion of source-side semantic roles. These semantic features, as well as the Tree-to-S...
Ding Liu, Daniel Gildea
136
Voted
ACL
2003
15 years 6 months ago
Unsupervised Learning of Arabic Stemming Using a Parallel Corpus
This paper presents an unsupervised learning approach to building a non-English (Arabic) stemmer. The stemming model is based on statistical machine translation and it uses an Eng...
Monica Rogati, J. Scott McCarley, Yiming Yang
139
Voted
CICLING
2008
Springer
15 years 6 months ago
Semantic and Syntactic Features for Dutch Coreference Resolution
We investigate the effect of encoding additional semantic and syntactic information sources in a classification-based machine learning approach to the task of coreference resolutio...
Iris Hendrickx, Véronique Hoste, Walter Dae...
168
Voted
NIPS
2003
15 years 6 months ago
Unsupervised Context Sensitive Language Acquisition from a Large Corpus
We describe a pattern acquisition algorithm that learns, in an unsupervised fashion, a streamlined representation of linguistic structures from a plain natural-language corpus. Th...
Zach Solan, David Horn, Eytan Ruppin, Shimon Edelm...
EMNLP
2010
15 years 2 months ago
Multi-Level Structured Models for Document-Level Sentiment Classification
In this paper, we investigate structured models for document-level sentiment classification. When predicting the sentiment of a subjective document (e.g., as positive or negative)...
Ainur Yessenalina, Yisong Yue, Claire Cardie