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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
ACL
2003
15 years 5 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
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...
NIPS
2003
15 years 5 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