Tetsuya Nasukawa


2023

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A Simple Yet Strong Domain-Agnostic De-bias Method for Zero-Shot Sentiment Classification
Yang Zhao | Tetsuya Nasukawa | Masayasu Muraoka | Bishwaranjan Bhattacharjee
Findings of the Association for Computational Linguistics: ACL 2023

Zero-shot prompt-based learning has made much progress in sentiment analysis, and considerable effort has been dedicated to designing high-performing prompt templates. However, two problems exist; First, large language models are often biased to their pre-training data, leading to poor performance in prompt templates that models have rarely seen. Second, in order to adapt to different domains, re-designing prompt templates is usually required, which is time-consuming and inefficient. To remedy both shortcomings, we propose a simple yet strong data construction method to de-bias a given prompt template, yielding a large performance improvement in sentiment analysis tasks across different domains, pre-trained language models, and prompt templates. Also, we demonstrate the advantage of using domain-agnostic generic responses over the in-domain ground-truth data.

2020

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Interactive Construction of User-Centric Dictionary for Text Analytics
Ryosuke Kohita | Issei Yoshida | Hiroshi Kanayama | Tetsuya Nasukawa
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a methodology to construct a term dictionary for text analytics through an interactive process between a human and a machine, which helps the creation of flexible dictionaries with precise granularity required in typical text analysis. This paper introduces the first formulation of interactive dictionary construction to address this issue. To optimize the interaction, we propose a new algorithm that effectively captures an analyst’s intention starting from only a small number of sample terms. Along with the algorithm, we also design an automatic evaluation framework that provides a systematic assessment of any interactive method for the dictionary creation task. Experiments using real scenario based corpora and dictionaries show that our algorithm outperforms baseline methods, and works even with a small number of interactions.

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Visual Objects As Context: Exploiting Visual Objects for Lexical Entailment
Masayasu Muraoka | Tetsuya Nasukawa | Bishwaranjan Bhattacharjee
Findings of the Association for Computational Linguistics: EMNLP 2020

We propose a new word representation method derived from visual objects in associated images to tackle the lexical entailment task. Although it has been shown that the Distributional Informativeness Hypothesis (DIH) holds on text, in which the DIH assumes that a context surrounding a hyponym is more informative than that of a hypernym, it has never been tested on visual objects. Since our perception is tightly associated with language, it is meaningful to explore whether the DIH holds on visual objects. To this end, we consider visual objects as the context of a word and represent a word as a bag of visual objects found in images associated with the word. This allows us to test the feasibility of the visual DIH. To better distinguish word pairs in a hypernym relation from other relations such as co-hypernyms, we also propose a new measurable function that takes into account both the difference in the generality of meaning and similarity of meaning between words. Our experimental results show that the DIH holds on visual objects and that the proposed method combined with the proposed function outperforms existing unsupervised representation methods.

2016

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Personality Estimation from Japanese Text
Koichi Kamijo | Tetsuya Nasukawa | Hideya Kitamura
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)

We created a model to estimate personality trait from authors’ text written in Japanese and measured its performance by conducting surveys and analyzing the Twitter data of 1,630 users. We used the Big Five personality traits for personality trait estimation. Our approach is a combination of category- and Word2Vec-based approaches. For the category-based element, we added several unique Japanese categories along with the ones regularly used in the English model, and for the Word2Vec-based element, we used a model called GloVe. We found that some of the newly added categories have a stronger correlation with personality traits than other categories do and that the combination of the category- and Word2Vec-based approaches improves the accuracy of the personality trait estimation compared with the case of using just one of them.

2010

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Robust Measurement and Comparison of Context Similarity for Finding Translation Pairs
Daniel Andrade | Tetsuya Nasukawa | Junichi Tsujii
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

2008

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Textual Demand Analysis: Detection of Users’ Wants and Needs from Opinions
Hiroshi Kanayama | Tetsuya Nasukawa
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

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Automatic Identification of Important Segments and Expressions for Mining of Business-Oriented Conversations at Contact Centers
Hironori Takeuchi | L Venkata Subramaniam | Tetsuya Nasukawa | Shourya Roy
Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)

2006

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Fully Automatic Lexicon Expansion for Domain-oriented Sentiment Analysis
Hiroshi Kanayama | Tetsuya Nasukawa
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

2004

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Deeper Sentiment Analysis Using Machine Translation Technology
Hiroshi Kanayama | Tetsuya Nasukawa | Hideo Watanabe
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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Term Aggregation: Mining Synonymous Expressions using Personal Stylistic Variations
Akiko Murakami | Tetsuya Nasukawa
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

2000

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Layout and Language: Integrating Spatial and Linguistic Knowledge for Layout Understanding Tasks
Matthew Hurst | Tetsuya Nasukawa
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

1997

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Generating natural sentences by using shallow discourse information
Shiho Ogina | Tetsuya Nasukawa
Proceedings of the 7th Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1996

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Full-text processing: improving a practical NLP system based on surface information within the context
Tetsuya Nasukawa
COLING 1996 Volume 2: The 16th International Conference on Computational Linguistics

1995

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Robust Parsing Based on Discourse Information: Completing partial parses of ill-formed sentences on the basis of discourse information
Tetsuya Nasukawa
33rd Annual Meeting of the Association for Computational Linguistics

1994

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Robust Method of Pronoun Resolution Using Full-Text Information
Tetsuya Nasukawa
COLING 1994 Volume 2: The 15th International Conference on Computational Linguistics

1993

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Discourse Constraint in Computer Manuals
Tetsuya Nasukawa
Proceedings of the Fifth Conference on Theoretical and Methodological Issues in Machine Translation of Natural Languages

1992

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Shalt2- a Symmetric Machine Translation System with Conceptual Transfer
Koichi Takeda | Naohiko Uramoto | Tetsuya Nasukawa | Taijiro Tsutsumi
COLING 1992 Volume 3: The 14th International Conference on Computational Linguistics