Hiroshi Kanayama

TL;DR: This paper proposes an unsupervised lexicon building method for the detection of polar clauses, which convey positive or negative aspects in a specific domain, and its method is robust for corpora in diverse domains and for the size of the initial lexicon.

TL;DR: The task and evaluation methodology is defined, how the data sets were prepared, report and analyze the main results, and a brief categorization of the different approaches of the participating systems are provided.

TL;DR: This paper used a high-coverage dictionary in an EM style training algorithm over monolingual corpora in two languages and achieved state-of-the-art performance on bilingual lexicon induction task exceeding models using large bilingual corpora.

TL;DR: An overview of the RITE (Recognizing Inference in TExt) task in NTCIR-9 is introduced and how to built the test collection, evaluation metrics, and evaluation results of the submitted runs are described.

TL;DR: This work generates candidate answers without regard to type, and for each candidate, it employs a variety of sources and strategies to judge whether the candidate has the desired type, which provides a set of type coercion scores forEach candidate answer.