Data Set Used
This paper describes Japanese textual entailment recognition systems for NTCIR-10 RITE2. The tasks that we participated in are the Japanese BC subtask and the Ex-amBC subtask. Our methods are based on some machine learning techniques with surface level, syntax and semantic features. We use two ontologies, the Japanese WordNet and Nihongo-Goi-Taikei, and… (More)
We participated in the extraction of complaint and diagnosis Task and the normalization of complaint and diagnosis Task of MedNLP2 in NTCIR11. In the extraction Task, we use CRF based Named Entity Recognition method. Moreover, we incorporate unsupervised features learned from raw corpus into CRF. We show such unsupervised features improve system performance.
We participated in the NTCIR-12 MedNLPDoc phenotyp-ing task. In this paper, we describe our approach for this task. The core part of our model is a similarity matrix model in which each element has a local similarity value between n-grams from a disease name and a medical record. We conduct an experiment to evaluate the effectiveness of our method. We… (More)
For clinical decision support systems designed to help physicians make diagnostic decisions, " disease similarity " data is highly valuable in that they allow continuous recommendation of diagnostic candidates. To build such a recommendation algorithm, this paper explores a method to measure disease similarity between diseases on a simplified disease… (More)
The CL team paticipated in the Fact Validation (FV) and System Validation (SV) subtasks in Japanese. This paper describes our systems with experimental results. In the Fact Validation subtask, a system is required to search the given documents for texts (t1) and judge the fact validity of the given statement (t2) based on the judgement of whether t1 entails… (More)