Minoru SASAKIAssociate Professor
■Researcher basic information
Organization
- College of Engineering Department of Computer and Information Sciences
- Graduate School of Science and Engineering（Master's Program） Major in Computer and Information Sciences
- Graduate School of Science and Engineerin（Doctoral Program） Major in Society's Infrastructure Systems Science
- Faculty of Applied Science and Engineering Domain of Computer and Information Sciences
Career
External link
■Research activity information
Award
Paper
- 〔Major achievements〕Graph Based Text Classification Using a Word-Reduced Heterogeneous Graph
Hiromu Nakajima; Minoru Sasaki, Last
International Journal on Advances in Software, Dec. 2024, [Reviewed], [Invited] - 〔Major achievements〕Sense Tagged Example Generation using BERTScore
Hyuga Nagatomo; Minoru Sasaki, Last
Proceedings of the 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 10 Jul. 2024, [Reviewed] - 〔Major achievements〕Metaphor detection with additional auxiliary context
Takuya Hayashi; Minoru Sasaki, Last
Proceedings of the 16th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI), 10 Jul. 2024, [Reviewed] - 〔Major achievements〕Sentence-BERTと語義定義文を利用した語義間の類義判定手法
石井 佑樹,佐々木 稔, Last, 言語処理学会
自然言語処理, 15 Jun. 2024, [Reviewed] - 〔Major achievements〕Text Classification Based on the Heterogeneous Graph Considering the Relationships between Documents
Hiromu Nakajima; Minoru Sasaki, Last, Text classification is the task of estimating the genre of a document based on information such as word co-occurrence and frequency of occurrence. Text classification has been studied by various approaches. In this study, we focused on text classification using graph structure data. Conventional graph-based methods express relationships between words and relationships between words and documents as weights between nodes. Then, a graph neural network is used for learning. However, there is a problem that conventional methods are not able to represent the relationship between documents on the graph. In this paper, we propose a graph structure that considers the relationships between documents. In the proposed method, the cosine similarity of document vectors is set as weights between document nodes. This completes a graph that considers the relationship between documents. The graph is then input into a graph convolutional neural network for training. Therefore, the aim of this study is to improve the text classification performance of conventional methods by using this graph that considers the relationships between document nodes. In this study, we conducted evaluation experiments using five different corpora of English documents. The results showed that the proposed method outperformed the performance of the conventional method by up to 1.19%, indicating that the use of relationships between documents is effective. In addition, the proposed method was shown to be particularly effective in classifying long documents., MDPI AG
Big Data and Cognitive Computing, 13 Dec. 2023, [Reviewed] - 〔Major achievements〕Text Classification Using a Word-Reduced Graph
Hiromu Nakajima; Minoru Sasaki, Last, IARIA
Proceedings of The Twelfth International Conference on Data Analytics (DATA ANALYTICS 2023), 27 Sep. 2023, [Reviewed] - Analysis of Claim Expressions in Japanese Assembly Minutes Using Sentence Embeddings
Ryo Kato; Minoru Sasaki, Corresponding, International Association for the Development of the Information Society
Proceedings of the 21st International Conference e-Society 2023, 11 Mar. 2023, [Reviewed] - 〔Major achievements〕Generating Market Comments on Stock Price Fluctuations Using Technical Analysis Featu
Ibuki Sekino; Minoru Sasaki, Last, The International Academy, Research and Industry Association (IARIA)
International Journal on Advances in Intelligent Systems, Dec. 2022, [Reviewed], [Invited] - 〔Major achievements〕Text classification using a graph based on relationships between documents
Hiromu Nakajima; Minoru Sasaki, Last, Multidisciplinary Digital Publishing Institute (MDPI)
Proceedings of the 36st Pacific Asia Conference on Language, Information and Computation (PACLIC36), 22 Oct. 2022, [Reviewed] - Reputation analysis using key phrases and sentiment scores extracted from reviews
Yipu Huang; Minoru Sasaki; Kanako Komiya, Last
Proceedings of the 36st Pacific Asia Conference on Language, Information and Computation (PACLIC36), 22 Oct. 2022, [Reviewed] - 〔Major achievements〕Effective use of Japanese dictionary definition sentences in learning hierarchical embedding of dictionaries
Yuki Ishii; Minoru Sasaki, Last
Proceedings of the 36st Pacific Asia Conference on Language, Information and Computation (PACLIC36), 22 Oct. 2022, [Reviewed] - 〔Major achievements〕Effectiveness analysis of word sense disambiguation using example of word senses from WordNet
Hiroshi Sekiya; Minoru Sasaki, Last
Proceedings of the 36st Pacific Asia Conference on Language, Information and Computation (PACLIC36), 20 Oct. 2022, [Reviewed] - 〔Major achievements〕Semi-supervised Word Sense Disambiguation Using Semantic Similarities between Examples
Rie Yatabe; Minoru Sasaki, Last, 情報処理学会
情報処理学会論文誌, 15 Oct. 2021, [Reviewed] - Generating Market Comments on Stock Price Fluctuations Using Neural Networks
Ibuki Sekino; Minoru Sasaki, Corresponding
Proceedings of the Thirteenth International Conference on Information, Process, and Knowledge Management (eKnow2021), 18 Jul. 2021, [Reviewed] - Predicting the Approval or Disapproval of each Faction in a Local Assembly Using a Rule-based Approach
Ryo Kato; Minoru Sasaki, Corresponding
Proceedings of the Thirteenth International Conference on Information, Process, and Knowledge Management (eKnow2021), 18 Jul. 2021, [Reviewed] - Japanese Word Sense Disambiguation Using Gloss Information of a Japanese Dictionary
Hiroki Okemoto; Minoru Sasaki, Corresponding
Proceedings of the Thirteenth International Conference on Information, Process, and Knowledge Management (eKnow2021), 18 Jul. 2021, [Reviewed] - Extraction of Causal Relationships across Multiple Sentences from Securities Reports
Takero Aniya; Minoru Sasaki, Corresponding
Proceedings of the Thirteenth International Conference on Information, Process, and Knowledge Management (eKnow2021), 18 Jul. 2021, [Reviewed] - Person Name Extraction from TV program Using Pre-trained Language Model and News Headlines
Kazuki Oda; Minoru Sasaki, Corresponding
Proceedings of the 12th International Conference on E-Service and Knowledge Management (ESKM 2021), 14 Jul. 2021, [Reviewed] - Extraction of News Articles Related to Stock Price Fluctuation Using Sentiment Expression
Kazuto Tanaka; Minoru Sasaki, Corresponding
Proceedings of the Thirteenth International Conference on Creative Content Technologies,(CONTENT2021), 18 Apr. 2021, [Reviewed] - Semi-supervised Word Sense Disambiguation Using Example Similarity Graph
Rie Yatabe; Minoru Sasaki, Corresponding
Proceedings of the 14th Workshop on Graph-Based Natural Language Processing (TextGraphs-14), 13 Dec. 2020, [Reviewed] - Word Sense Disambiguation Using Graph-based Semi-supervised Learning
Rie Yatabe; Minoru Sasaki, Corresponding
Proceedings of The Fourteenth International Conference on Advances in Semantic Processing (SEMAPRO2020), 25 Oct. 2020, [Reviewed] - 概念辞書の類義語と分散表現を利用した教師なし all-words WSD
鈴木類; 古宮嘉那子; 浅原正幸; 佐々木稔; 新納浩幸, Corresponding, 言語処理学会
自然言語処理, 15 Jun. 2019, [Reviewed] - Composing Word Vectors for Japanese Compound Words Using Dependency Relations
Kanako Komiya; Takumi Seitou; Minoru Sasaki; Hiroyuki Shinnou
Cicling 2019, 11 Apr. 2019, [Reviewed] - Active Learning to Select Unlabeled Examples with Effective Features for Document Classification
Minoru Sasaki
The 10th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING2019)., 11 Apr. 2019, [Reviewed] - Auto-extraction of Influential Keywords Included in Financial News Headlines
Masahiro Miyoshi; Wenkai Shi; Yui Hosoki; Junichi Eguchi; Minoru Sasaki; Tomoya Suzuki, Corresponding
Proceedings of the 2019 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing (NCSP'19), 04 Mar. 2019, [Reviewed] - Fine-tuning for Named Entity Recognition Using Part-of-Speech Tagging
Masaya Suzuki; Kanako Komiya; Minoru Sasaki and Hiroyuki Shinnou, Corresponding
The 32th Pacific Asia Conference on Language, Information and Computation (PACLIC32), 01 Dec. 2018, [Reviewed] - Word Embeddings of Monosemous Words in Dictionary for Word Sense Disambiguation
Minoru Sasaki
Proceedings of The Twelfth International Conference on Advances in Semantic Processing (SEMAPRO 2018), 19 Nov. 2018, [Reviewed] - Stance Classification Using Political Parties in Tokyo Metropolitan Assembly Minutes
Yasutomo Kimura and Minoru Sasaki, Corresponding
Proceedings of The Seventh International Conference on Data Analytics (DATA ANALYTICS 2018), 19 Nov. 2018, [Reviewed] - Multi-Domain Word Embeddings for Semantic Relation Analysis among Domains
Minoru Sasaki
Proceedings of The Fourth Asia Pacific Corpus Linguistics Conference (APCLC 2018), 17 Sep. 2018, [Reviewed] - Domain Adaptation using Word Embeddings for Word Sense Disambiguation
Kanako Komiya; Minoru Sasaki; Hiroyuki Shinnou; Manabu Okumura, Corresponding, 言語処理学会
Journal of Natural Language Processing, 15 Sep. 2018, [Reviewed] - Detecting Unknown Word Senses in Contemporary Japanese Dictionary from Corpus of Historical Japanese
Aya Tanabe; Kanako Komiya; Masayuki Asahara; Minoru Sasaki; Hiroyuki Shinnou, Corresponding
The 8th Conference of Japanese Association for Digital Humanities (JADH2018), 11 Sep. 2018, [Reviewed] - Comparison of Methods to Annotate Named Entity Corpora
Kanako Komiya; Masaya Suzuki; Tomoya Iwakura; Minoru Sasaki; Hiroyuki Shinnou, Corresponding, Association for Computing Machinery
Transactions on Asian and Low-Resource Language Information Processing, Aug. 2018, [Reviewed] - All-words Word Sense Disambiguation Using Concept Embeddings
Rui Suzuki; Kanako Komiya; Masayuki Asahara; Minoru Sasaki; Hiroyuki Shinnou, Corresponding
Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC2018), 08 May 2018, [Reviewed] - Distributed Representation vs. Context Vector: Comparison of Features for Japanese Onomatopoeia Classification
Kanako Komiya; Minoru Sasaki; Hiroyuki Shinnou, Corresponding
Proceedings of the 9th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING2018), 20 Mar. 2018, [Reviewed] - Domain Adaptation for Document Classification by Alternately Using Semi-supervised Learning and Feature Weighted Learning
Hiroyuki Shinnou; Kanako Komiya; Minoru Sasaki, In this paper, we propose a new unsupervised domain adaptation method for document classification. We address the problem of domain adaptation for document classification where the source and target domains do not differ significantly and there is no labeled data in the target domain. In this case, we can use conventional semi-supervised learning. Thus, we use the naive Bayes-based expectation-maximization method (NBEM) which is very effective for document classification. However, NBEM does not utilize the difference between a source domain and a target domain. We combine NBEM with the feature weighted method for domain adaptation, referred to as “self-training feature weight” (STFW). Our proposed method alternately uses NBEM and STFW to gradually improve document classification precision for a target domain. This method significantly outperforms the conventional unsupervised methods for domain adaptation., Springer Verlag
Communications in Computer and Information Science, 2018, [Reviewed] - nwjc2vec:国語研日本語ウェブコーパスから構築した単語の分散表現データ
新納浩幸; 浅原正幸; 古宮嘉那子; 佐々木稔, Corresponding, 言語処理学会
自然言語処理, 15 Dec. 2017, [Reviewed] - Japanese all-words WSD system using the Kyoto Text Analysis ToolKit
Hiroyuki Shinnou; Kanako Komiya; Minoru Sasaki; Shinsuke Mori, Corresponding
Proceedings of the 31th Pacific Asia Conference on Language, Information and Computation (PACLIC-31), 16 Nov. 2017, [Reviewed] - Cross-lingual Product Recommendation System Using Collaborative Filtering
Kanako Komiya; Minoru Sasaki; Hiroyuki Shinnou; Yoshiyuki Kotani, Corresponding,We developed a cross-lingual recommender system using collaborative filtering with English-Japanese translation pairs of product names to help non-Japanese buyers who speak English when they are visiting Japanese shopping websites. Customer purchase histories at an English shopping site and those at another Japanese shopping site were used for the experiments. Two experiments were conducted to evaluate the system. They were (1) two-fold cross validation where half of the translation pairs were masked and (2) experiments where all of the translation pairs were used. The precisions, recalls, and mean reciprocal ranks (MRRs) of the system were evaluated to assess the general performance of the recommender system in the first set of experiments. We investigated the effect formatting the translation pairs and the performance according to the type of feature value of the vectors (binary versus rating values). In contrast, the kind of items that were recommended in a more realistic scenario were shown in the second experiment. The results reveal that masked items were found more efficiently than when the bestseller recommender system was used and, further, that items only on the Japanese site that seemed to be related to the buyers' interests could be found by the system in the more realistic scenario.
, 言語処理学会
自然言語処理, 15 Sep. 2017, [Reviewed] - Domain Adaptation for Document Classification by Alternately Using Semi-supervised Learning and Feature Weighted Learning
Hiroyuki Shinnou; Kanako Komiya; Minoru Sasaki, Corresponding
Proceedings of the 15th International Conference of the Pacific Association for Computational Linguistics (PACLING2017), 16 Aug. 2017, [Reviewed] - Word Sense Disambiguation Based On Global Co-Occurrence Information Using Non-Negative Matrix Factorization
Minoru Sasaki, Lead, Symbiosis Group LLC
Journal of Computer Science Applications and Information Technology, 24 Jul. 2017, [Reviewed] - Domain Adaptation for Word Sense Disambiguation using Word Embeddings
Kanako Komiya; Shota Suzuki; Minoru Sasaki; Hiroyuki Shinnou; Manabu Okumura, Corresponding
Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing, 17 Apr. 2017, [Reviewed] - Comparison of Annotating Methods in Named Entity Extraction
Masaya SUZUKI; Kanako KOMIYA; Tomoya IWAKURA; Minoru SASAKI; Hiroyuki SHINNOU, 会議名: 言語資源活用ワークショップ2016, 開催地: 国立国語研究所, 会期: 2017年3月7日-8日, 主催: 国立国語研究所 コーパス開発センター
本稿では, 非専門家による固有表現抽出のタスクとしてのアノテーションを題材に, ふたつの手法について比較を行った. ひとつは既存の固有表現抽出器によるアノテーション結果に対し, 人手で修正を行う手法であり, もうひとつは人手で一からアノテーションを行う手法である. 実験には現代日本語書き言葉均衡コーパス(BCCWJ) を利用し, 手法ごとに1 テキストに対し2 人の非専門家を割り当てて, アノテーションを行った. 評価には, アノテーションにかかる時間, 一致率, Gold Standard との比較による正解率, それぞれの手法で作成されたコーパスを訓練事例とした場合の正解率を利用し, ジャンルごと, 及び, 全ジャンルのマイクロ平均とマクロ平均を算出した. 本実験の結果から, 全ジャンルのマイクロ平均とマクロ平均で比較した場合には既存のアノテーション結果を用いた手法の方が良い結果となるが, 既存の固有表現抽出器の訓練事例から離れたジャンルで同様に比較した場合には人手でアノテーションを行う手法の方が良い結果となることが明らかになった.
source:http://pj.ninjal.ac.jp/corpus_center/lrw2016.html
identifier:茨城大学
identifier:茨城大学
identifier:富士通研究所
identifier:茨城大学
identifier:茨城大学, 国立国語研究所
言語資源活用ワークショップ発表論文集 = Proceedings of Language Resources Workshop, 2017 - Supervised Word Sense Disambiguation with Sentences Similarities from Context Word Embeddings
Shoma Yamaki; Hiroyuki Shinnou; Kanako Komiya and Minoru Sasaki, Corresponding
The 30th Pacific Asia Conference on Language, Information and Computation (PACLIC-30), 28 Oct. 2016, [Reviewed] - Word Sense Disambiguation Using Active Learning with Pseudo Examples
Minoru Sasaki; Katsumune Terauchi; Kanako Komiya; Hiroyuki Shinnou, Lead
The Tenth International Conference on Advances in Semantic Processing (SEMAPRO 2016), 09 Oct. 2016, [Reviewed] - Comparison of Annotating Methods for Named Entity Corpora
Kanako Komiya; Masaya Suzuki; Tomoya Iwakura; Minoru Sasaki; Hiroyuki Shinnou, Corresponding, The Association for Computer Linguistics
the 10th Linguistic Annotation Workshop held in conjunction with ACL 2016 (LAW-X 2016), 11 Aug. 2016, [Reviewed] - Estimating Concept Embeddings from Their Child Concepts
Tatsuya Oono; Kanako Komiya; Minoru Sasaki; Hiroyuki Shinnou, We estimate the concept embeddings, which are distributed representations of the concepts, in a concept dictionary from the concept embeddings of their child concepts. The concept dictionaries represent the systematic classification of the concepts of nouns and verbs in a way and the concept embeddings represent the meanings of the concepts. This paper investigates what kind of composition calculation can represent the relation between the concepts and their child concepts in a concept dictionary, which is a hierarchical relationship that humans assume. We examined four methods to estimate the concept embeddings and investigated three size of dimensions. The experiments revealed that the best method was the simple summation of concept embeddings of the child concepts and the similarities increased when the vector size decreased. We also examined that whether the similarities between the actual and estimated concept embed dings will increase when we restricted the concepts to calculate the similarities by the minimum number or percentage of their children's concept embeddings. However, the experiments revealed that they decreased if the concepts were restricted., IEEE
2016 FIFTH ICT INTERNATIONAL STUDENT PROJECT CONFERENCE (ICT-ISPC), 2016, [Reviewed] - Selecting Training Data for Unsupervised Domain Adaptation in Word Sense Disambiguation
Kanako Komiya; Minoru Sasaki; Hiroyuki Shinnou; Yoshiyuki Kotani; Manabu Okumura, This paper describes a method of domain adaptation, which involves adapting a classifier developed from source to target data. We automatically select the training data set that is suitable for the target data from the whole source data of multiple domains. This is unsupervised domain adaptation for Japanese word sense disambiguation (WSD). Experiments revealed that the accuracies of WSD improved when we automatically selected the training data set using two criteria, the degree of confidence and the leave-one-out (LOO)-bound score, compared with when the classifier was trained with all the data., SPRINGER INT PUBLISHING AG
PRICAI 2016: TRENDS IN ARTIFICIAL INTELLIGENCE, 2016, [Reviewed] - クラスタリングを利用した語義曖昧性解消の誤り原因のタイプ分け
新納浩幸; 村田真樹; 白井清昭; 福本文代; 藤田早苗; 佐々木稔; 古宮嘉那子; 乾孝司, Corresponding, As a first step of word sense disambiguation (WSD) errors analysis, generally we need investigate the causes of errors and classify them. For this purpose, seven analysts classified the error data for analysis from their unique standpoints. Next, we attempted to merge the results from the analyses. However, merging these results through discussions was difficult because the results differed significantly. Therefore, we used a clustering method for a certain level of automatic merger. Consequently, we classified WSD errors into nine types, and it turned out that the three main types of errors covers 90% of the total WSD errors. Moreover, we showed that the merged error types represented seven results and was standardized by defining the similarity between two classifications and comparing it with each analysis result., The Association for Natural Language Processing
自然言語処理, Dec. 2015, [Reviewed] - Learning under Covariate Shift for Domain Adaptation for Word Sense Disambiguation
Hiroyuki Shinnou; Kanako Komiya; Minoru Sasaki, Corresponding
The 29th Pacific Asia Conference on Language, Information and Computation (PACLIC-29), 01 Nov. 2015, [Reviewed] - Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
Kazuhei Kouno; Hiroyuki Shinnou; Minoru Sasaki; Kanako Komiya, Corresponding
The 29th Pacific Asia Conference on Language, Information and Computation (PACLIC-29), 01 Nov. 2015, [Reviewed] - Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification
Hiroyuki Shinnou; Liying Xiao; Minoru Sasaki; Kanako Komiya, Corresponding
The 29th Pacific Asia Conference on Language, Information and Computation (PACLIC-29), 01 Nov. 2015, [Reviewed] - Surrounding Word Sense Model for Japanese All-words Word Sense Disambiguation
Kanako Komiya; Yuto Sasaki; Hajime Morita; Minoru Sasaki; Hiroyuki Shinnou; Yoshiyuki Kotani, Corresponding
The 29th Pacific Asia Conference on Language, Information and Computation (PACLIC-29), 30 Oct. 2015, [Reviewed] - Domain Adaptation with Filtering for Named Entity Extraction of Japanese Anime-Related Words
Kanako KOMIYA; Daichi EDAMURA; Ryuta TAMURA; Minoru SASAKI; Hiroyuki SHINNOU; Yoshiyuki KOTANI, Corresponding
Recent Advances in Natural Language Processing (RANLP2015), 07 Sep. 2015, [Reviewed] - Active Learning to Remove Source Instances for Domain Adaptation for Word Sense Disambiguation
Hiroyuki Shinnou; Yoshiyuki Onodera; Minoru Sasaki and Kanako Komiya, Corresponding
Pacific Association of Computational Linguistics (PACLING2015), 20 May 2015, [Reviewed] - 共変量シフト下の学習による語義曖昧性解消の教師なし領域適応
新納浩幸; 佐々木稔, Corresponding, In this paper, we apply the learning under covariate shift to the problem of unsupervised domain adaptation for word sense disambiguation (WSD). This learning is a type of weighted learning method, in which the probability density ratio w(x) = PT(x)/PS(x) is used as the weight of an instance. However, w(x) tends to be small in WSD tasks. In order to address this problem, we calculate w(x) by estimating PT(x) and PS(x), where PS(x) is estimating by regarding the corpus combining the source domain corpus and target domain corpus as the source domain corpus. In the experiment, we use three domains -OC (Yahoo! Chiebukuro), PB (books) and PN (news papers)- in BCCWJ, and 16 target words provided by the Japanese WSD task in SemEval-2. For calculating w(x), we also use uLSIF, which directly estimates w(x) without estimating PT(x) or PS(x). Moreover, we use the "p power" method and the "relative probability density ratio" method to boost the obtained probability density ratio. These experiments prove our method to be effective., The Association for Natural Language Processing
自然言語処理, Sep. 2014, [Reviewed] - Word Sense Disambiguation Based on Semi-automatically Constructed Collocation Dictionary
Minoru Sasaki; Kanako Komiya; Hiroyuki Shinnou, Lead
Proceedings of the Eighth International Conference on Advances in Semantic Processing(SEMAPRO2014), 26 Aug. 2014, [Reviewed] - Domain Adaptation for Word Sense Disambiguation under the Problem of Covariate Shift
Hiroyuki Shinnou; Minoru Sasaki, Corresponding
Journal of Natural Language Processing, 13 Mar. 2014, [Reviewed] - Latent Semantic Word Sense Disambiguation Using Global Co-occurrence Information
Minoru Sasaki
Third International Conference on Natural Language Processing (NLP-2014), 21 Feb. 2014, [Reviewed] - Domain Adaptation for Word Sense Disambiguation using k-Nearest Neighbor Algorithm and Topic Model
Hiroyuki Shinnou; Minoru Sasaki, Corresponding, 言語処理学会
Journal of Natural Language Processing, 13 Dec. 2013, [Reviewed] - 外れ値検出手法を利用した新語義の検出
新納浩幸; 佐々木稔, Corresponding
自然言語処理, Dec. 2012, [Reviewed] - Word Sense Disambiguation Based on Distance Metric Learning from Training Documents
Minoru Sasaki and Hiroyuki Shinnou, Corresponding
The Sixth International Conference on Advances in Semantic Processing (SEMAPRO2012), 25 Sep. 2012, [Reviewed] - Detection of Peculiar Word Sense by Distance Metric Learning with Labeled Examples
Minoru Sasaki; Hiroyuki Shinnou, For natural language processing on machines, resolving such peculiar usages would be particularly useful in constructing a dictionary and dataset for word sense disambiguation. Hence, it is necessary to develop a method to detect such peculiar examples of a target word from a corpus. Note that, hereinafter, we define a peculiar example as an instance in which the target word or phrase has a new meaning. In this paper, we proposed a new peculiar example detection method using distance metric learning from labeled example pairs. In this method, first, distance metric learning is performed by large margin nearest neighbor classification for the training data, and new training data points are generated using the distance metric in the original space. Then, peculiar examples are extracted using the local outlier factor, which is a density-based outlier detection method, from the updated training and test data. The efficiency of the proposed method was evaluated on an artificial dataset and the Semeval-2010 Japanese WSD task dataset. The results showed that the proposed method has the highest number of properly detected instances and the highest F-measure value. This shows that the label information of training data is effective for density-based peculiar example detection. Moreover, an experiment on outlier detection using a classification method such as SVM showed that it is difficult to apply the classification method to outlier detection., EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
LREC 2012 - EIGHTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2012, [Reviewed] - Detection of Peculiar Examples using LOF and One Class SVM
Hiroyuki Shinnou; Minoru Sasaki, This paper proposes the method to detect peculiar examples of the target word from a corpus. The peculiar example is regarded as an outlier in the given example set. Therefore we can apply many methods proposed in the data mining domain to our task. In this paper, we propose the method to combine the density based method, Local Outlier Factor (LOF), and One Class SVM, which are representative outlier detection methods in the data mining domain. In the experiment, we use the Whitepaper text in BCCWJ as the corpus, and 10 noun words as target words. Our method improved precision and recall of LOF and One Class SVM. And we show that our method can detect new meanings by using the noun 'midori (green)'. The main reason of un-detections and wrong detection is that similarity measure of two examples is inadequacy. In future, we must improve it., EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
LREC 2010 - SEVENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, 2010, [Reviewed] - Document Clustering Using Semantic Relationship Between Target Documents and Related Documents
Minoru Sasaki; Hiroyuki Shinnou, Document clustering is one of the most major techniques to group documents automatically. This technique is to divide a given set of documents into a certain number of clusters automatically. In this technique, the first step is 'feature extraction' from documents. As a feature used in the conventional methods, we frequently use a set of words that contains nouns and verbs. Although words are used as features in a generic clustering framework, some previous research proposes the clustering method using the other features based on vector space model such as kernel methods and adaptive sprinkling. However, in previous research of document clustering, the method of appending new feature vectors obtained by using relationship between the existing documents and other documents has not been reported yet. So, we propose a new method for clustering documents using the relationship between the existing documents and other documents to acquire the more useful clusters for users. Our method can expand features of document similarities as semantic relationships by using relevant documents that user is interested in, like semi-supervised clustering. To evaluate the efficiency of this system, we made experiments on clustering newsgroup documents by using our method and by using the dimension reduction method based on the singular value decomposition. As the results of these experiments, we found that (i) it is effective for document clustering to combine the similarity matrix with the original matrix, and (ii) low similarity values cause adverse effect to the clustering performance when we use all the similarity value. Moreover, the proposed method is more effective for the document clustering in comparison with the clustering through the dimensionality reduction., IARIA XPS PRESS
SEMAPRO 2010: THE FOURTH INTERNATIONAL CONFERENCE ON ADVANCES IN SEMANTIC PROCESSING, 2010, [Reviewed] - Hierarchical Classification of Web Sites into Web Directory
Minoru Sasaki
The Third International Conference on Advances in Semantic Processing (SEMAPRO2009), 15 Oct. 2009, [Reviewed] - A Fast Retrieval Algorithm for the Earth Mover's Distance Using EMD Lower Bounds and the Priority Queue
Masami Shishibori; Daichi Koizumi; Kenji Kita, Earth Mover's Distance (EMD) is a distance measure between two distributions, and has been widely used in multimedia information retrieval systems, especially content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. Its computations, however, is too expensive to use in large-scale databases. In order to achieve the efficient computation of the EMD during query processing, we have developed "fastEMD", a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and demonstrates the efficiency in extensive experiments., IEEE
IEEE NLP-KE 2009: PROCEEDINGS OF INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING, 2009, [Reviewed] - A fast retrieval algorithm for the Earth Mover's Distance using EMD lower bounds
Masami Shishibori; Satoru Tsuge; Zhang Le; Minoru Sasaki; Yoshiki Uemura; Kenji Kita, Earth Mover's Distance (EMD) is a distance measure between two distributions, and have been widely used in multimedia information retrieval systems, especially content-based image retrieval systems. When the EMD is applied to image problems based on color or texture, the EMD reflects the human perceptual similarities. Its computations, however, is too expensive to use in large-scale databases. In order to achieve the efficient computation of the EMD during query processing, we have developed "fastEMD", a library for high-speed feature-based similarity retrievals in large databases. This paper introduces techniques that are used in the implementation of the fastEMD and demonstrates the efficiency in extensive experiments., IEEE
PROCEEDINGS OF THE 2008 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION, 2008, [Reviewed] - Ping-pong Document Clustering using NMF and Linkage-Based Refinement
Hiroyuki Shinnou; Minoru Sasaki, This paper proposes a ping-pong document clustering method using NMF and the linkage based refinement alternately, in order to improve the clustering result of NMF. The use of NMF in the ping-pong strategy can be expected effective for document clustering. However, NMF in the ping-pong strategy often worsens performance because NMF often fails to improve the clustering result given as the initial values. Our method handles this problem with the stop condition of the ping-pong process. In the experiment, we compared our method with the k-means and NMF by using 16 document data sets. Our method improved the clustering result of NMF significantly., EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, 2008, [Reviewed] - Spectral Clustering for a Large Data Set by Reducing the Similarity Matrix Size
Hiroyuki Shinnou; Minoru Sasaki, Spectral clustering is a powerful clustering method for document data set. However, spectral clustering needs to solve an eigenvalue problem of the matrix converted from the similarity matrix corresponding to the data set. Therefore, it is not practical to use spectral clustering for a large data set. To overcome this problem, we propose the method to reduce the similarity matrix size. First, using k-means, we obtain a clustering result for the given data set. From each cluster, we pick up some data, which are near to the central of the cluster. We take these data as one data. We call these data set as "committee." Data except for committees remain one data. For these data, we construct the similarity matrix. Definitely, the size of this similarity matrix is reduced so much that we can perform spectral clustering using the reduced similarity matrix, EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, 2008, [Reviewed] - Division of Example Sentences Based on the Meaning of a Target Word Using Semi-supervised Clustering
Hiroyuki Shinnou; Minoru Sasaki, In this paper, we describe a system that divides example sentences (data set) into clusters, based on the meaning of the target word, using a semi-supervised clustering technique. In this task, the estimation of the cluster number (the number of the meaning) is critical. Our system primarily concentrates on this aspect. First, a user assigns the system an initial cluster number for the target word. The system then performs general clustering on the data set to obtain small clusters. Next, using constraints given by the user, the system integrates these clusters to obtain the final clustering result. Our system performs this entire procedure with high precision and requiring only a few constraints. In the experiment, we tested the system for 12 Japanese nouns used in the SENSEVAL2 Japanese dictionary task. The experiment proved the effectiveness of our system. In the future, we will improve sentence similarity measurements., EUROPEAN LANGUAGE RESOURCES ASSOC-ELRA
SIXTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION, LREC 2008, 2008 - Ensemble document clustering using weighted hypergraph generated by NMF
Hiroyuki Shinnou; Minoru Sasaki, Corresponding, 言語処理学会
Journal of Natural Language Processing, 10 Oct. 2007, [Reviewed] - Ensemble Document Clustering Using Weighted Hypergraph Generated by NMF
Hiroyuki Shinnou; Minoru Sasaki, Corresponding
Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics Companion Volume Proceedings of the Demo and Poster Sessions, 25 Jun. 2007, [Reviewed] - Refinement of Document Clustering by Using NMF
Hiroyuki Shinnou; Minoru Sasaki, In this paper, we use non-negative matrix factorization (NMF) to refine the document clustering results. NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method. First we perform min-max cut (Mcut), which is a powerful spectral clustering method, and then refine the result via NMF. Finally we should obtain an accurate clustering result. However, NMF often fails to improve the given clustering result. To overcome this problem, we use the Mcut object function to stop the iteration of NMF., KOREAN SOC LANGUAGE & INFORMATION-KSLI
PACLIC 21: THE 21ST PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION, PROCEEDINGS, 2007 - Spam detector using text clustering
M Sasaki; H Shinnou, We propose a new spam detection technique using the text clustering based on vector space model. Our method computes disjoint clusters automatically using a spherical k-means algorithm for all spam/non-spam mails and obtains centroid vectors of the clusters for extracting the cluster description. For each centroid vectors, the label('spam' or 'non-spam') is assigned by calculating the number of spam email in the cluster When new mail arrives, the cosine similarity between the new mail vector and centroid vector is calculated. Finally, the label of the most relevant cluster is assigned to the new mail. By using our method, we can extract many kinds of topics in spam/non-spam email and detect the spam email efficiently. In this paper, we describe the our spam detection system and show the result of our experiments using the Ling-Spam test collection., IEEE COMPUTER SOC
2005 International Conference on Cyberworlds, Proceedings, 2005 - Semi-supervised learning by Fuzzy clustering and Ensemble learning
Hiroyuki Shinnou; Minoru Sasaki, Corresponding
4th international conference on Language Resources and Evaluation (LREC2004), May 2004, [Reviewed] - Information Retrieval System using Latent Contextual Relevance
Minoru Sasaki; Hiroyuki Shinnou, Lead
4th international conference on Language Resources and Evaluation (LREC2004), May 2004, [Reviewed] - EMアルゴリズムの最適ループ回数の予測を用いた語義判別規則の教師なし学習
新納 浩幸; 佐々木 稔, Corresponding
情報処理学会論文誌, Dec. 2003 - SVDPACKCとその語義判別問題への利用
Hiroyuki Shinnou; Minoru Sasaki, Corresponding
自然言語処理, Apr. 2003 - Unsupervised Learning of Word Sense Disambiguation Rules by Estimating an Optimum Iteration Number in the EM Algorithm
Hiroyuki Shinnou; Minoru Sasaki, Corresponding
Seventh Conference on Natural Language Learning(CoNLL-2003), 2003 - Automatic Thesaurus Construction Using Word Clustering
Minoru Sasaki; Hiroyuki Shinnou, Corresponding
Pacific Association for Computational Linguistics(PACLING03), 2003 - Learning of Word Sense Disambiguation Rules by Belief Networks
Hiroyuki Shinnou; Syuya Abe; Minoru Sasaki, Corresponding
Pacific Association for Computational Linguistics(PACLING03), 2003 - ランダム・プロジェクションによるベクトル空間情報検索モデルの次元削減
Minoru Sasaki; Kenji Kita, Lead
Natural Language Processing, Jan. 2001 - 情報検索システムの統計的手法による特徴と精度の分析
Minoru Sasaki; Kenji Kita, Lead
自然言語処理, Jan. 2001 - Vector Space Information Retrieval Using Concept Projection
Minoru Sasaki; Kenji Kita, Lead
the Proceeding of the 19th International Conference on Computer Processing of Oriental Languages, 2001 - Information Retrieval System Using Concept Projection Based on PDDP algorithm
Minoru Sasaki; Kenji Kita, Lead
Pacific Association for Computational Linguistics(PACLING2001), 2001 - Improvement of Vector Space Information Retrieval Model based on Supervised Learning
Tai Xiao Ying; Minoru Sasaki; Kenji Kita; Yasuhito Tanaka, Corresponding
the Proceeding of the Fifth International Workshop on Information Retrieval with Asian Languages(IRAL2000), 2000 - Rule-based text categorization using hierarchical categories
Minoru Sasaki; Kenji Kita, Document categorization, which is defined as the classification of text documents into one of several fixed classes or categories, has become important with the explosive growth of the World Wide Web. The goal of the work described here is to automatically categorize Web documents in order to enable effective retrieval of Web information. In this paper, based on the rule learning algorithm RIPPER (for Repeated Incremental Pruning to Produce Error Reduction), we propose an efficient method for hierarchical document categorization., IEEE
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1998 - Rule-based hierarchical document categorization for the World Wide Web
K Kita; M Sasaki; XY Tai, Document categorization, which is defined as the classification of text documents into one of several fixed classes or categories, has become important with the explosive growth of the World Wide Web. The goal of the work described here is to automatically categorize Web documents in order to enable effective retrieval of Web information. Based on the rule learning algorithm RIPPER (Repeated Incremental Pruning to Produce Error Reduction), we propose an efficient method Sor hierarchical document categorization., WORLD PUBLISHING CORPORATION
WEB TECHNOLOGIES AND APPLICATIONS, 1998 - Automatic Text Categorization based on Hierarchical Rules
Minoru Sasaki; Kenji Kita, Lead
5th International Conference on Soft Computing, 1998 - Rule-based text categorization using hierarchical categories
M Sasaki; K Kita, Document categorization, which is defined as the classification of text documents into one of several fixed classes or categories, has become important with the explosive growth of the World Wide Web. The goal of the work described here is to automatically categorize Web documents in order to enable effective retrieval of Web information. In this paper, based on the rule learning algorithm RIPPER (for Repeated Incremental Pruning to Produce Error Reduction), we propose an efficient method for hierarchical document categorization., IEEE
1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5, 1998 - Automatic Acquisition of Probabilistic Dialogue Models
Kenji Kita; Minoru Sasaki, Corresponding
4th International Conference on Soft Computing, 1996 - Improvement of a Probabilistic CFG Using a Cluster-Based Language Modeling Technique
Kenji Kita; Minoru Sasaki, Corresponding
4th International Conference on Soft Computing, 1996
MISC
- カタカナ語の意味分類に対する大規模言語モデルの有効性検証
小滝主紀; 佐々木稔
言語資源ワークショップ2024, Aug. 2024
Last - Analysis of the Statements Expressing the Pros and Cons of a Bill in Tokyo Metropolitan Assembly:Toward NTCIR-15 QA Lab-PoliInfo-2 Stance Classification Task
TAKAMARU Keiichi; KIMURA Yasutomo; UCHIDA Yuzu; SASAKI Minoru; YOSHIOKA Masaharu; AKIBA Tomoyoshi; SHIBUKI Hideyuki
Proceedings of the Annual Conference of JSAI, Jun. 2020 - 日本語歴史コーパスの現代語辞書における未知語義判定システム
田邊 絢; 古宮 嘉那子; 浅原 正幸; 佐々木 稔; 新納 浩幸
言語資源活用ワークショップ2018発表論文集, Sep. 2018 - 『岩波国語辞典』の語義タグを用いたall-wordsの語義曖昧性解消
平林照雄; 鈴木類; 古宮嘉那子; 浅原正幸; 佐々木稔; 新納浩幸
言語処理学会第24回年次大会(NLP2018) 発表論文集, Mar. 2018 - Polarity Classification Using Ensemble Learning
金子 顕之; 古宮 嘉那子; 佐々木 稔; 新納 浩幸
電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 07 Sep. 2017 - 『分類語彙表』の類義語と分散表現を利用したall-words語義曖昧性解消
鈴木類; 古宮嘉那子; 浅原正幸; 佐々木稔; 新納浩幸
言語資源活用ワークショップ2016, Mar. 2017 - KyTeaを利用した日本語all-words WSD
新納 浩幸; 森 信介; 古宮 嘉那子; 佐々木 稔
言語処理学会第22回年次大会, 2016 - all-words WSDのための概念辞書の自動作成 (言語理解とコミュニケーション)
新納 浩幸; 古宮 嘉那子; 佐々木 稔
電子情報通信学会技術研究報告 = IEICE technical report : 信学技報, 03 Dec. 2015 - Domain Adaptation for Word Sense Disambiguation using k-Nearest Neighbors Method and Topic Model
新納浩幸; 佐々木稔
IPSJ SIG Notes, 16 May 2013 - Detection of Peculiar Examples using Distance Metric Learning from Labeled Example pairs
佐々木 稔; 新納 浩幸
研究報告自然言語処理（NL）, 09 Sep. 2011 - A new concept of the search engine for the Web API
Yuka Obu; Minoru Sasaki; Tatsuhiro Yonckura
WEBIST 2008: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON WEB INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2, 2008, [Reviewed] - Refinement of clustering results by spectral method using the reduced similarity matrix
新納 浩幸; 佐々木 稔
知識ベ-スシステム研究会, 03 Dec. 2007 - Collection of example sentences according to the meaning of a word and estimation of the number of meanings by using semi-supervised clustering
SHINNOU Hiroyuki; SASAKI Minoru
IPSJ SIG Notes, 24 Jul. 2007 - Collection of example sentences according to the meaning of a word and estimation of the number of meanings by using semi-supervised clustering
SHINNOU Hiroyuki; SASAKI Minoru
IEICE technical report, 17 Jul. 2007 - Ping-Pong Document Clustering by using NMF and Linkage Based Refinement
Shinnou Hiroyuki; Sasaki Minoru
IPSJ SIG Notes, 24 May 2007 - Estimation of prior distribution of word senses by mixture distribution of multinomial and uniform distributions(Natural Language Understanding and Models of Communication)
SHINNOU Hiroyuki; SASAKI Minoru
IEICE technical report. Natural language understanding and models of communication, 07 Nov. 2003 - Estimation of prior distribution of word senses by mixture distribution of multinomial and uniform distributions
SHINNOU Hiroyuki; SASAKI Minoru
IPSJ SIG Notes, 06 Nov. 2003 - Word Clustering for Word Sense Disambiguation
SASAKI Minoru; SHINNOU Hiroyuki
IPSJ SIG Notes, 06 Mar. 2003 - Fast method of word sense disambiguation using information retrieval technique
SHINNOU Hiroyuki; SASAKI Minoru
IPSJ SIG Notes, 12 Nov. 2002 - Query Expansion Using Latent Contextual Document Relevance
SASAKI Minoru; SHINNOU Hiroyuki
IPSJ SIG Notes, 17 Sep. 2002
Lectures, oral presentations, etc.
- 議会会議録と予算表を紐づけるMinutes-to-Budget Linkingタスクの提案
木村泰知; 梶縁; 乙武北斗; 門脇一真; 佐々木稔; 小林暁雄
言語処理学会第29回年次大会, 16 Mar. 2023, 言語処理学会
20230313, 20230317 - BERTの学習済みモデルを用いた語義定義文の類義判定に有効な日本語辞書の変更方法
石井佑樹; 佐々木稔
言語処理学会第29回年次大会, 15 Mar. 2023, 言語処理学会
20230313, 20230317 - RoBERTaを用いた経済不確実性のテキスト分類
桑名祥平; 佐々木稔
言語処理学会第29回年次大会, 14 Mar. 2023, 言語処理学会
20230313, 20230317 - 接尾辞を持つ単語の語義定義文とその分散表現の分析
須山晃平; 佐々木稔
言語処理学会第29回年次大会, 14 Mar. 2023, 言語処理学会
20230313, 20230317 - WordNet Lexicographerカテゴリ推定による語義サイズ縮約を用いた語義曖昧性解消
橋口卓弥; 佐々木稔
言語処理学会第29回年次大会, 14 Mar. 2023, 言語処理学会
20230313, 20230317 - NTCIR-17 QA Lab-PoliInfo-4 のタスク設計
小川泰弘; 木村泰知; 渋木英潔; 乙武北斗; 内田ゆず; 高丸圭一; 門脇一真; 秋葉友良; 佐々木稔; 小林暁雄
言語処理学会第29回年次大会, 13 Mar. 2023, 言語処理学会
20230313, 20230317 - 第11回iiCafe「AIがヒトの言葉を理解する日」
佐々木稔
iiCafe, 06 Oct. 2022, URA, [Invited]
20221006, 20221006 - Overview of the NTCIR-16 QA Lab-PoliInfo-3 Task
Yasutomo Kimura; Hideyuki Shibuki; Hokuto Ototake; Yuzu Uchida; Keiichi Takamaru; Madoka Ishioroshi; Masaharu Yoshioka; Tomoyoshi Akiba; Yasuhiro Ogawa; Minoru Sasaki; Ken-ichi Yokote; Kazuma Kadowaki; Tatsunori Mori; Kenji Araki; Teruko Mitamura and Satoshi Sekine
The 16th NTCIR Conference Evaluation of Information Access Technologies, 14 Jun. 2022
20220614, 20220617 - Ibrk at the NTCIR-16 QA Lab-PoliInfo-3 Budget Argument Mining Subtask
Kohei Seguchi and Minoru Sasaki
The 16th NTCIR Conference Evaluation of Information Access Technologies, 14 Jun. 2022
20220614, 20220617 - 予算項目に関連する議論を対応づけるBudget Argument Mining のデータセット構築
木村泰知; 永渕景祐; 乙武北斗; 佐々木稔
第249回自然言語処理研究会, 28 Jul. 2021, 情報処理学会
20210727, 20210728 - The reliability of word meanings in online dictionaries and how word meanings change over time
Hans-Werner Sehring; Robert Duncan; Minoru Sasaki; Claudio Zandron; Christoph Reich
The Thirteenth International Conference on Pervasive Patterns and Applications,(PATTERNS2021), 18 Apr. 2021, [Invited]
20210418, 20210422 - Ibrk at the NTCIR-15 QA Lab-PoliInfo-2
Ryo Kato; Minoru Sasaki
The Fourteenth NTCIR conference (NTCIR-15), 10 Dec. 2020 - 複数の事前学習済みモデルを用いたQAサイト質問回答ペアの分類
佐々木稔; 古宮嘉那子
IDRユーザフォーラム2020, 24 Nov. 2020, 国立情報学研究所
20201124, 20201124 - Understanding the Meaning of Words Using Dictionary
Gregor Grambow; Tim vor der Brück; Minoru Sasaki; Javier Fabra
The Fourteenth International Conference on Advances in Semantic Processing (SEMAPRO2020), 25 Oct. 2020, [Invited]
20201025, 20201029 - 語義曖昧性解消における辞書に定義された単義語利用についての分析
佐々木稔; 谷田部梨恵
言語資源活用ワークショップ2020, 08 Sep. 2020
20200908, 20200909 - テレビ番組データを対象とした人名抽出と番組ジャンル推定
織田一輝; 佐々木稔
第19回情報科学技術フォーラム(FIT2020), 03 Sep. 2020, 電子情報通信学会、情報処理学会
20200901, 20200903 - 東京都議会会議録における議案への賛否を表明する発言の分析,NTCIR-15 QA Lab-PoliInfo-2 Stance Classification Taskに向けて
高丸圭一 木村泰知 内田ゆず 佐々木稔 吉岡真治 秋葉友良 渋木英潔
第34回人工知能学会全国大会, 12 Jun. 2020
20200609, 20200612 - 大規模地方議会会議録の分散表現を用いた地方議会のトピック分析
佐々木稔; 乙武北斗; 木村泰知
第34回人工知能学会全国大会, 12 Jun. 2020
20200609, 20200612 - 極性表現を用いた株価変動に関わるニュース記事の抽出
田中一澄; 谷田部梨恵; 佐々木稔; 鈴木智也
第34回人工知能学会全国大会, 12 Jun. 2020
20200609, 20200612 - 東京都議会会議録における議案への賛否を表明する発言の分析 NTCIR-15 QA Lab-PoliInfo-2 Stance Classification Taskに向けて
高丸圭一; 木村泰知; 内田ゆず; 佐々木稔; 吉岡真治; 秋葉友良; 渋木英潔
第34回人工知能学会全国大会, 12 Jun. 2020 - BERTの学習済みモデルを用いた用例文ペアの同義判定
谷田部梨恵; 佐々木稔
言語処理学会第26回年次大会, 18 Mar. 2020
20200316, 20200319 - NTCIR-15 QA Lab-PoliInfo2 のタスク設計
木村泰知; 渋木英潔; 高丸圭一; 秋葉友良; 石下円香; 内田ゆず; 小川泰弘; 乙武北斗; 佐々木稔; 三田村照子; 横手健一; 吉岡真治; 神門典子
言語処理学会第26回年次大会, 18 Mar. 2020
20200316, 20200319 - 単語区切りの違いによるQAサイトの質問回答ペアの分類
佐々木稔; 古宮嘉那子
IDRユーザフォーラム2019, 29 Nov. 2019 - 半教師あり語義曖昧性解消における各ジャンルの語義なし用例文の利用
谷田部梨恵; 佐々木稔
言語資源活用ワークショップ2019, 03 Sep. 2019 - グラフニューラルネットワークを用いた半教師あり語義曖昧性解消
谷田部梨恵; 佐々木稔
情報処理学会 第241回自然言語処理研究会, 29 Aug. 2019 - Ibrk at the NTCIR-14 QA Lab-PoliInfo Classification Task
Minoru Sasaki; Tetsuya Nogami
The Fourteenth NTCIR conference (NTCIR-14), 12 Jun. 2019 - 東京都議会の会派を用いたStance classificationの試み
木村泰知; 佐々木稔
言語処理学会第25回年次大会, 15 Mar. 2019 - Gaussian LDAを用いた地方議会会議録のトピック分析
佐々木稔; 木村泰知
言語処理学会第25回年次大会, 14 Mar. 2019 - 短文に対する補助文脈を考慮した語義曖昧性解消
佐々木稔
第12回 テキストアナリティクス・シンポジウム, 06 Sep. 2018 - 単語の分散表現を用いた領域における出現単語の特徴分析
佐々木稔
国語研言語資源活用ワークショップ2018, 05 Sep. 2018 - 係り受け関係を用いた短単位の単語ベクトルから長単位の単語ベクトルの合成
清藤拓実; 古宮嘉那子; 佐々木稔; 新納浩幸
言語処理学会第24回年次大会, P10-1, 15 Mar. 2018 - 単義語の分散表現と単語間の係り受け関係を用いた語義曖昧性解消
遊佐宣彦; 佐々木稔; 古宮嘉那子; 新納浩幸
言語処理学会第24回年次大会, P4-10, 14 Mar. 2018 - 『岩波国語辞典』の語義タグを用いたall-wordsの語義曖昧性解消
平林照雄; 鈴木類; 古宮嘉那子; 浅原正幸; 佐々木稔; 新納浩幸
言語処理学会第24回年次大会，P7-25, 14 Mar. 2018 - 深層学習と合議を用いた極性分類
金子顕之; 古宮嘉那子; 佐々木稔; 新納浩幸
第11回 テキストアナリティクス・シンポジウム, 08 Sep. 2017 - 単義語と共起する多義語に対する分散表現を利用した語義分析
遊佐宣彦; 佐々木稔; 古宮嘉那子; 新納浩幸
言語資源活用ワークショップ, 06 Sep. 2017 - nwjc2vec の fine-tuning
新納浩幸; 古宮嘉那子; 佐々木稔
国語研言語資源活用ワークショップ, 05 Sep. 2017 - 順方向多層 LSTM と分散表現を用いた教師あり学習による語義曖昧性解消
新納浩幸; 古宮嘉那子; 佐々木稔
情報処理学会自然言語処理研究会, NL-232-4, 19 Jul. 2017 - 用例文拡張辞書を利用したトピックモデルに基づく新語義検出
神宮理織; 佐々木稔; 古宮嘉那子; 新納浩幸
言語処理学会第23回年次大会, P20-2, 16 Mar. 2017 - 固有表現抽出におけるタグセットの相互適応
鈴木雅也; 古宮嘉那子; 佐々木稔; 新納浩幸
言語処理学会第23回年次大会, P7-3, 15 Mar. 2017 - 教師データを用いた語義の分散表現の構築
山木翔馬; 新納浩幸; 古宮嘉那子; 佐々木稔
言語処理学会第23回年次大会, E1-1, 14 Mar. 2017 - 分散表現に基づく日本語語義曖昧性解消における類義語と辞書定義文を併用した語義表現の有効性
遊佐宣彦; 佐々木稔; 古宮嘉那子; 新納浩幸
言語処理学会第23回年次大会, E1-2, 14 Mar. 2017 - 『分類語彙表』の類義語と分散表現を利用したall-words語義曖昧性解消
鈴木類; 古宮嘉那子; 浅原正幸; 佐々木稔; 新納浩幸
言語処理学会第23回年次大会, E1-3, 14 Mar. 2017 - 画像キャプション生成における複数形表現の統一
西友佑; 新納浩幸; 古宮嘉那子; 佐々木稔
言語処理学会第23回年次大会, P4-5, 14 Mar. 2017 - 擬似用例を追加する能動学習を用いた一般単語の語義曖昧性解消
寺内 賢志; 佐々木 稔; 古宮 嘉那子; 新納 浩幸
情報処理学会自然言語処理研究会, 30 Sep. 2016 - 固有表現抽出におけるアノテーション手法の比較
鈴木雅也; 古宮嘉那子; 岩倉友哉; 佐々木稔; 新納浩幸
情報処理学会自然言語処理研究会, NL-228-7, 30 Sep. 2016 - 点推定による日本語 all-words WSD システム KyWSD
新納浩幸; 古宮嘉那子; 佐々木稔; 森信介
情報処理学会自然言語処理研究会, 29 Jul. 2016 - 分散表現による語義曖昧性解消の領域適応
鈴木翔太; 古宮嘉那子; 佐々木稔; 新納浩幸; 奥村学
情報処理学会自然言語処理研究会, 16 May 2016 - 概念辞書における子概念からの親概念の分散表現の推定
大野達也; 古宮嘉那子; 佐々木稔; 新納浩幸
言語処理学会第22回年次大会, 10 Mar. 2016 - 物語における登場人物の親しさ推定
小井出慎; 古宮嘉那子; 佐々木稔; 新納浩幸
言語処理学会第22回年次大会, 10 Mar. 2016 - 分散表現に基づく日本語語義曖昧性解消における辞書定義文の有効性
佐々木稔; 古宮嘉那子; 新納浩幸
言語処理学会第22回年次大会, 09 Mar. 2016 - KyTea を利用した日本語 all-words WSD
新納浩幸; 森信介; 古宮嘉那子; 佐々木稔
言語処理学会第22回年次大会, 09 Mar. 2016 - 分散表現と文脈ベクトルによるオノマトぺの分類の比較
古宮嘉那子; 佐々木稔; 新納浩幸
言語処理学会第22回年次大会, 09 Mar. 2016 - 半教師あり学習と素性の重み付け学習の交互適用による文書分類の領域適応
新納浩幸; 古宮嘉那子; 佐々木稔
言語処理学会第22回年次大会, 09 Mar. 2016 - 類義語を利用した単語の分散表現から語義の分散表現の構築
大内克之; 新納浩幸; 古宮嘉那子; 佐々木稔
言語処理学会第22回年次大会, 08 Mar. 2016 - 分散表現から得た用例間類似度を素性に加えた語義曖昧性解消
山木翔馬; 新納浩幸; 古宮嘉那子; 佐々木稔
言語処理学会第22回年次大会, 08 Mar. 2016 - 分散表現を用いた教師あり機械学習による語義曖昧性解消
山木翔馬; 新納浩幸; 古宮嘉那子; 佐々木稔
情報処理学会自然言語処理研究会, 04 Dec. 2015 - all-words WSD のための概念辞書の自動作成
新納浩幸; 古宮嘉那子; 佐々木稔
情報処理学会自然言語処理研究会, 03 Dec. 2015 - 語義曖昧性解消の誤り分析
新納浩幸; 白井清昭; 村田真樹; 福本文代; 藤田早苗; 佐々木稔; 古宮嘉那子; 乾孝司
言語処理学会第21回年次大会 ワークショップ「エラー分析ワークショップ」, 20 Mar. 2015 - ベイズ規則による確率密度比の推定を用いた語義曖昧性解消の領域適応
菊池裕紀; 新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 19 Mar. 2015 - Stacked Denoising Autoencoderを利用した語義曖昧性解消の領域適応
河野和平; 新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 19 Mar. 2015 - 文書分類をタスクとした Pylearn2 の Maxout+Dropout の利用
永田純平; 新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 19 Mar. 2015 - 日本語人名辞書を用いた中国語文書からの人名抽出
Xiao Liying; 新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 19 Mar. 2015 - 素性に重みを付けるSelf-training手法を用いた文書分類の領域適応
國井慎也; 新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 17 Mar. 2015 - 語義曖昧性解消におけるシソーラス利用の問題分析
新納浩幸; 佐々木稔; 古宮嘉那子
言語処理学会第21回年次大会, 17 Mar. 2015 - 領域適応のためのサポートベクトルを用いた訓練事例の反復的選択
小林優稀; 古宮 嘉那子; 佐々木稔; 新納 浩幸; 奥村 学
第7回日本語学ワークショップ, 10 Mar. 2015 - uLSIF を用いた事例への重み付けによる語彙曖昧性解消の領域適応
新納 浩幸; 菊池 裕紀; 佐々木 稔; 古宮 嘉那子
情報処理学会自然言語処理研究会 NL-218-2, 01 Sep. 2014, 情報処理学会 - インスタンス選択による文書データの効率的な分類モデル構築手法
小幡智裕; 佐々木稔
言語処理学会第20回年次大会, 20 Mar. 2014, 言語処理学会 - 語義曖昧性解消を対象とした領域固有のシソーラスの構築
新納 浩幸; 國井 慎也; 佐々木 稔
第5回コーパス日本語学ワークショップ, 07 Mar. 2014, 国立国語研究所 - ミドルソフトタグのトピック素性を利用した語義曖昧性解消
國井慎也; 新納浩幸; 佐々木稔
言語処理学会第19回年次大会, 15 Mar. 2013, 言語処理学会 - サポートベクターマシンに基づくHit Miss Networkを用いたインスタンス選択
小幡智裕; 佐々木稔; 新納浩幸
言語処理学会第19回年次大会, 15 Mar. 2013, 言語処理学会 - 商品タイトルから商品名を自動抽出するための効率的な教師データ作成手法
佐々木稔; 新納浩幸
言語処理学会第18回年次大会, 16 Mar. 2012 - トピックモデルを用いた語義曖昧性解消
西野太樹; 新納浩幸; 佐々木稔
言語処理学会第18回年次大会, 16 Mar. 2012 - 逆トピックワードを利用した外れ値文書検出
真下飛瑠; 新納浩幸; 佐々木稔
言語処理学会第18回年次大会, 16 Mar. 2012 - 外れ値検出手法を利用した新語義の検出
新納浩幸; 佐々木稔
言語処理学会第18回年次大会, 16 Mar. 2012 - 人名構成文字確率を用いた文字ベース CRF による中国語人名検出
新納浩幸; 全太俊; 佐々木稔
言語処理学会第18回年次大会, 15 Mar. 2012 - 教師データ間距離学習を利用した新語義用例の検出
佐々木稔; 新納浩幸
情報処理学会 第203回自然言語処理研究会, 16 Sep. 2011 - 教師付き外れ値検出による新語義の発見
新納浩幸; 佐々木稔
言語処理学会第17回年次大会, 10 Mar. 2011 - 距離学習に基づく語義識別の性能分析
佐々木稔; 新納浩幸
言語処理学会第17回年次大会, 09 Mar. 2011 - 商品説明文からの商品名の直接的な説明、言い換え表現の自動抽出
林華; 佐々木稔; 新納浩幸
第3回楽天研究開発シンポジウム, 18 Dec. 2010, 楽天株式会社 楽天技術研究所 - Webディレクトリを利用した意味的関連語集合の作成
佐々木稔; 三上健太; 新納浩幸
言語処理学会第16回年次大会, 11 Mar. 2010 - 名詞の主要語義の推定と語義識別への応用
江口晃; 新納浩幸; 佐々木稔
言語処理学会第16回年次大会, 10 Mar. 2010 - LOF と One Class SVM を用いた特異用例の検出
新納浩幸; 佐々木稔
言語処理学会第16回年次大会, 10 Mar. 2010 - Web ディレクトリを利用した名詞のジャンルベクトルの作成
林華; 新納浩幸; 佐々木稔
言語処理学会第16回年次大会, 10 Mar. 2010 - クラスタリング手法を用いた語義別用例の収集
佐々木稔; 新納浩幸
コーパスを利用した国語辞典編集法の研究, 14 Nov. 2009, [Invited] - 類似性の不明なデータを手がかりとして与えるクラスタリング手法
佐々木稔,松本良太,新納浩幸
DEIM フォーラム 2009, 08 Mar. 2009 - 文書クラスタリングを対象とした Weighted Kernel K-means の初期値設定法
茂木哲矢; 新納浩幸; 佐々木稔
言語処理学会第15回年次大会, 05 Mar. 2009 - グラフクラスタリングによる単語用例クラスタリング
相原功昌; 佐々木稔; 新納浩幸
言語処理学会第15回年次大会, 05 Mar. 2009 - 商品説明文からの検索語に対する関連語抽出
久保田敦; 佐々木稔; 新納浩幸
言語処理学会第15回年次大会, 05 Mar. 2009 - 用例間類似度測定のための属性重みの推定
新納浩幸; 佐々木稔
言語処理学会第15回年次大会, 05 Mar. 2009 - 行動パターンに基づくお薦め観光ルート作成
木村泰知; 佐々木稔; 沼澤政信
NLP若手の会シンポジウム, 23 Sep. 2008 - 文書関連性を素性として追加した文書クラスタリング
佐々木稔; 新納浩幸
言語処理学会第14回年次大会, 19 Mar. 2008 - 縮約類似度行列を用いたスペクトラル手法によるクラスタリング結果の改善
新納浩幸; 佐々木稔
人工知能学会第79回知識ベースシステム研究会, 05 Dec. 2007 - Webサイトの階層的なWebディレクトリへの自動分類手法
佐々木稔; 新納浩幸
情報処理学会自然言語処理研究会, 25 Jul. 2007 - 半教師有りクラスタリングを用いた語義数の推定と語義別用例の収集
新納浩幸; 佐々木稔
情報処理学会自然言語処理研究会, 24 Jul. 2007 - NMF とリンクベースの修正法によるピンポン型文書クラスタリング
新納浩幸; 佐々木稔
情報処理学会自然言語処理研究会, 24 May 2007 - metaタグを利用したWebディレクトリの自動構築手法
佐々木稔; 新納浩幸
言語処理学会第13回年次大会, 22 Mar. 2007 - Mcut+NMF による文書クラスタリング
新納浩幸; 佐々木稔
言語処理学会第13回年次大会, 21 Mar. 2007 - 制約を修正に用いた半教師有りクラスタリング
新納浩幸; 佐々木稔; 村上浩司
第９回情報論的学習理論ワークショップ(IBIS-2006), 01 Nov. 2006 - 「Web2.0」へ導く技術
佐々木稔
情報科学技術フォーラム(FIT2006), 07 Sep. 2006, [Invited] - 文書分類手法を用いた企業Webサイトからの業種分類
佐々木稔; 新納浩幸
NLP2006, 14 Mar. 2006 - 検索エンジンを利用した未登録単語に関する単語間距離の測定
新納浩幸; 佐々木稔
NLP2006, 14 Mar. 2006 - A Study of Restoring Voiceless Fricatives on Laryngectomee Speech Using Machine Learning techniques
村上浩司; 佐々木稔
電子情報通信学会 思考と言語、福祉情報工学 合同研究会, 13 Jan. 2006 - Web Document Clustering using Threshold Selection Partitioning
Minoru Sasaki; Hiroyuki Shinnou
4th NTCIR Workshop Meeting, Jun. 2004
Research Themes
- 安全評価へのAI適用に関する研究
Apr. 2022 - Mar. 2023 - 〔Major achievements〕半教師あり深層学習を用いた語義曖昧性解消
基盤研究（C)
Apr. 2018 - Mar. 2023 - AI運用のためのニューステキストからのアルファならびにセンチメント情報自動抽出
Aug. 2018 - Jul. 2020 - Research on method of extracting collocation for each word sense and its application for word sense disambiguation
Grant-in-Aid for Young Scientists (B)
Ibaraki University
01 Apr. 2012 - 31 Mar. 2014 - Prospects of Biofuels in Japan, China and India : Environmental Economic Analysis supported by GIS
Grant-in-Aid for Scientific Research (B)
The University of Tokushima
2008 - 2011 - Japanese semantic analysis using balanced corpus of contemporary Written Japanese
Grant-in-Aid for Scientific Research on Priority Areas
Tokyo Institute of Technology
2006 - 2010
