This paper describes our contribution in Opinion Target Extraction OTE and Sentiment Polarity sub tasks of SemEval 2015 ABSA task. A CRF model with IOB notation has been adopted for OTE with several groups of features including syntactic, lexical, semantic, sentiment lexicon features. Our submission for OTE is ranked fifth over twenty submissions. A Logistic Regression model with a weighting sc...

A new type of stochastic grammars is introduced for investigation: weakly restricted stochastic grammars. In this paper we will concentrate on the consistency problem. To nd conditions for stochastic grammars to be consistent, the theory of multitype Galton-Watson branching processes and generating functions is of central importance. The unrestricted stochastic grammar formalism generates the s...

We describe two new strategies to automatic bracketing of parallel corpora, with particular application to languages where prior grammar resources are scarce: (1) coarse bilingual grammars, and (2) unsupervised training of such grammars via EM (expectation-maximization). Both methods build upon a formalism we recently introduced called stochastic inversion transduction grammars. The first appro...

Parameter tuning is a key problem for statistical machine translation (SMT). Most popular parameter tuning algorithms for SMT are agnostic of decoding, resulting in parameters vulnerable to search errors in decoding. The recent research of “search-aware tuning” (Liu and Huang, 2014) addresses this problem by considering the partial derivations in every decoding step so that the promising ones a...

In this paper, we present a semantic role labeler (or chunker) that groups syntactic chunks (i.e. base phrases) into the arguments of a predicate. This is accomplished by casting the semantic labeling as the classification of syntactic chunks (e.g. NP-chunk, PP-chunk) into one of several classes such as the beginning of an argument (B-ARG), inside an argument (I-ARG) and outside an argument (O)...

This paper utilizes Yamcha, a SVM tool designed by Taku Kudo, to train an NP-chunking model for Chinese. In addition to IOB and two words surrounding the focused word, we experimented on new features and exploited unlabeled data from web pages to enhance the previous model. Our experiments with supervised learning indicate that our chosen feature sets outperform those reported in previous studi...

We present an efficient framework to estimate the rule probabilities for a hierarchical phrasebased statistical machine translation system from parallel data. In previous work, this was done with bilingual parsing. We use a more efficient approach splitting the bilingual parsing into two stages, which allows us to train a hierarchical translation model on larger tasks. Furthermore, we apply lea...

The most popular algorithms for the estimation of the probabilities of a context-free grammar are the Inside-Outside algorithm and the Viterbi algorithm, which are Maximum Likelihood approaches. The diierence between the logarithm of the likelihood of a string and the logarithm of the likelihood of the most probable parse of a string is upper bounded linearly by the length of the string and the...