Learn More
Protein-protein interaction (PPI) extraction is an important and widely researched task in the biomedical natural language processing (BioNLP) field. Kernel-based machine learning methods have been used widely to extract PPI automatically, and several kernels focusing on different parts of sentence structure have been published for the PPI task. In this(More)
We address the problem of joint part-of-speech (POS) tagging and dependency parsing in Chinese. In Chinese, some POS tags are often hard to disambiguate without considering longrange syntactic information. Also, the traditional pipeline approach to POS tagging and dependency parsing may suffer from the problem of error propagation. In this paper, we propose(More)
This paper introduces an overview of the RITE (Recognizing Inference in TExt) task in NTCIR-9. We evaluate systems that automatically recognize entailment, paraphrase, and contradiction between two texts written in Japanese, Simplified Chinese, or Traditional Chinese. The task consists of four subtasks: Binary classification of entailment (BC); Multi-class(More)
Because of the importance of proteinprotein interaction (PPI) extraction from text, many corpora have been proposed with slightly differing definitions of proteins and PPI. Since no single corpus is large enough to saturate a machine learning system, it is necessary to learn from multiple different corpora. In this paper, we propose a solution to this(More)
We describe probabilistic models for a chart generator based on HPSG. Within the research field of parsing with lexicalized grammars such as HPSG, recent developments have achieved efficient estimation of probabilistic models and high-speed parsing guided by probabilistic models. The focus of this paper is to show that two essential techniques – model(More)
Probabilistic modeling of lexicalized grammars is difficult because these grammars exploit complicated data structures, such as typed feature structures. This prevents us from applying common methods of probabilistic modeling in which a complete structure is divided into substructures under the assumption of statistical independence among sub-structures.(More)
We define broad-coverage semantic dependency parsing (SDP) as the task of recovering sentence-internal predicate–argument relationships for all content words, i.e. the semantic structure constituting the relational core of sentence meaning. 1 Background and Motivation Syntactic dependency parsing has seen great advances in the past decade, in part owing to(More)
This paper shows that the performance of history-based models can be significantly improved by performing lookahead in the state space when making each classification decision. Instead of simply using the best action output by the classifier, we determine the best action by looking into possible sequences of future actions and evaluating the final states(More)
This paper describes an overview of RITE-2 (Recognizing Inference in TExt) task in NTCIR-10. We evaluated systems that automatically recognize semantic relations between sentences such as paraphrase, entailment, contradiction in Japanese, Simplified Chinese and Traditional Chinese. The tasks in RITE-2 are Binary Classification of entailment (BC Subtask),(More)