Most data-driven dependency parsing approaches assume that sentence structure is represented as trees. Although trees have several desirable properties from both computational and...
It is difficult to identify sentence importance from a single point of view. In this paper, we propose a learning-based approach to combine various sentence features. They are cat...
Self-training has been shown capable of improving on state-of-the-art parser performance (McClosky et al., 2006) despite the conventional wisdom on the matter and several studies ...
In this paper, an extension of a dimensionality reduction algorithm called NONNEGATIVE MATRIX FACTORIZATION is presented that combines both `bag of words' data and syntactic ...
We determine the subjectivity of word senses. To avoid costly annotation, we evaluate how useful existing resources established in opinion mining are for this task. We show that r...
Bracketing Transduction Grammar (BTG) is a natural choice for effective integration of desired linguistic knowledge into statistical machine translation (SMT). In this paper, we p...
We propose a new integrated approach based on Markov logic networks (MLNs), an effective combination of probabilistic graphical models and firstorder logic for statistical relatio...
In this paper, we explore a conceptual resource for Chinese nominal phrases, which allows multi-dependency and distinction between dependency and the corresponding exact relation....