Author name cluster

Kota Tsubouchi

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3 papers

2 author rows

AAAI Conference 2020 Conference Paper

Word-Level Contextual Sentiment Analysis with Interpretability

Tomoki Ito
Kota Tsubouchi
Hiroki Sakaji
Tatsuo Yamashita
Kiyoshi Izumi

Word-level contextual sentiment analysis (WCSA) is an important task for mining reviews or opinions. When analyzing this type of sentiment in the industry, both the interpretability and practicality are often required. However, such a WCSA method has not been established. This study aims to develop a WCSA method with interpretability and practicality. To achieve this aim, we propose a novel neural network architecture called Sentiment Interpretable Neural Network (SINN). To realize this SINN practically, we propose a novel learning strategy called Lexical Initialization Learning (LEXIL). SINN is interpretable because it can extract word-level contextual sentiment through extracting word-level original sentiment and its local and global word-level contexts. Moreover, LEXIL can develop the SINN without any speciﬁc knowledge for context; therefore, this strategy is practical. Using real textual datasets, we experimentally demonstrate that the proposed LEXIL is effective for improving the interpretability of SINN and that the SINN features both the high WCSA ability and high interpretability.

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AAAI Conference 2019 Conference Paper

Spatiality Preservable Factored Poisson Regression for Large-Scale Fine-Grained GPS-Based Population Analysis

Masamichi Shimosaka
Yuta Hayakawa
Kota Tsubouchi

With the wide use of smartphones with Global Positioning System (GPS) sensors, the analysis of the population from GPS traces has been actively explored in the last decade. We propose herein a brand new population prediction model to capture the population trends in a fine-grained point of interest (POI) densely distributed over large areas and understand the relationship of each POI in terms of spatiality preservation. We propose a new framework, called Spatiality Preservable Factorized Regression (SPFR), to realize this model. The SPFR is inspired by the success of the recently proposed bilinear Poisson regression and the concept of multi-task learning with factorization approach and the graph proximity regularization. Given that the proposed model is written simply in terms of optimization, we achieve scalability using our model. The results of our empirical evaluation, which used a massive dataset of GPS logs in the Tokyo region over 32 M count logs, show that our model is comparable to the stateof-the-art methods in terms of capturing the population trend across meshes while retaining spatial preservation in finer mesh areas.

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ICRA Conference 2005 Conference Paper

Micro Manipulators for Intrauterine Fetal Surgery in an Open MRI

Kanako Harada
Kota Tsubouchi
Toshio Chiba
Masakatsu G. Fujie

We propose a new surgical robotic system for intrauterine fetal surgery in an Open MRI. The target disease of the fetal surgery is spina bifida or myelomeningocele that is incomplete closure in the spinal column and one of the common fetal diseases. In the proposed surgical process, the abdominal wall and uterine wall would not widely be opened but rather surgical instruments inserted through the small holes in both walls to perform minimally invasive surgery. In this paper, a prototype of the micro manipulator of diameter is 2. 4mm and bending radius 2. 45 mm is presented. The diameter and bending radius of this manipulator is one of the smallest ever developed among surgical robots to the best of the knowledge of the investigating authors. The mechanism of the manipulator includes two ball joints and is driven using four wires able to bend through 90 degrees in any direction. The features of the mechanism include a small diameter, small bending radius, ease of fabrication, high rigidity and applicability for other surgical applications. Although the manipulator is not yet MRI compatible, the feature of the prototype demonstrated the feasibility of robotic intrauterine fetal surgery.

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