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発表一覧
冨浦 洋一（とみうら よういち） データ更新日：2023.11.27

教授　／　 システム情報科学研究院 情報学部門 知能科学


学会発表等
1. Tokinori Suzuki, Shintaro Deguchi, Yoichi Tomiura, Using the Scatter of Opinions to Predict Responses to Tweets, 2022 12th International Congress on Advanced Applied Informatics, IIAI-AAI 2022, 2022.07.
2. Satoshi Fukuda, Emi Ishita, Yoichi Tomiura, Douglas W. Oard, Automating the Choice Between Single or Dual Annotation for Classifier Training, The 23rd International Conference on Asia-Pacific Digital Libraries (ICADL 2021), 2021.12, Many emerging digital library applications rely on automated classifiers that are trained using manually assigned labels. Accurately labeling training data for text classification requires either highly trained coders or multiple annotations, either of which can be costly. Previous studies have shown that there is a quality-quantity trade-off for this labeling process, and the optimal balance between quality and quantity varies depending on the annotation task. In this paper, we present a method that learns to choose between higher-quality annotation that results from dual annotation and higher-quantity annotation that results from the use of a single annotator per item. We demonstrate the effectiveness of this approach through an experiment in which a binary classifier is constructed for assigning human value categories to sentences in newspaper editorials..
3. Mei Kodama, Emi Ishita, Yukiko Watanabe, Yoichi Tomiura, Usage of E-books During the COVID-19 Pandemic: A Case Study of Kyushu University Library, Japan, iConference 2021, 2021.03.
4. Emi Nishida, Emi Ishita, Yukiko Watanabe, Yoichi Tomiura, Description of research data in laboratory notebooks: Challenges and opportunities, ASIS&T 2020, 2020.10.
5. Emi Ishita, Satoshi Fukuda, Yoichi Tomiura, Douglas W Oard, Using text classification to improve annotation quality by improving annotator consistency, ASIS&T 2020, 2020.10.
6. 福田悟志, 冨浦洋一, 網羅性を重視した学術論文に対する検索手法, 研究報告情報基礎とアクセス技術 (IFAT), 2020.07.
7. Xiaofan Zheng, Yoichi Tomiura, Kenshi Hayashi, Takaaki Soeda, Profile-Decomposing Output of Multi-Channel Odor Sensor Array, IMCS 2020, 2020.05.
8. Emi Ishita, Satoshi Fukuda, Toru Oga, Yoichi Tomiura, Douglas W Oard, Kenneth R Fleischmann, Cost-effective learning for classifying human values, iConference 2020, 2020.03.
9. M. Kodama, K. Abe, K. Fukushima, E. Hayashi, Z. Hua, M. Jiang, P. Kang, E. Nishida, S. Sakai, Y. Tomiura, Y. Watanabe, E. Ishita, Content Analysis of Library Use on Microblog: Pre-coding Results, 9th Asia-Pacific Conference on Library & Information Education and Practice (A-LIEP 2019), 2019.11.
10. H. Uchiyama, E. Ishita, Y. Watanabe, Y. Tomiura, A. Shimada, M. Yamada , A framework for sharing learner generated contents in collaborative learning, 9th Asia-Pacific Conference on Library & Information Education and Practice (A-LIEP 2019), 2019.11.
11. K. Fukushima, E. Ishita, Y. Tomiura, Y. Watanabe, H. Uchiyama, Photovoice for Student Out-of-Class Learning, 9th Asia-Pacific Conference on Library & Information Education and Practice (A-LIEP 2019), 2019.11.
12. Keiya Maekawa, Yoichi Tomiura, Satoshi Fukuda, Emi Ishita, Hideaki Uchiyama, Improving OCR for Historical Documents by Modeling Image Distortion, 21st International Conference on Asia-Pacific Digital Libraries (ICADL 2019), 2019.11, [URL], Archives hold printed historical documents, many of which have deteriorated. It is difficult to extract text from such images without errors using optical character recognition (OCR). This problem reduces the accuracy of information retrieval. Therefore, it is necessary to improve the performance of OCR for images of deteriorated documents. One approach is to convert images of deteriorated documents to clear images, to make it easier for an OCR system to recognize text. To perform this conversion using a neural network, data is needed to train it. It is hard to prepare training data consisting of pairs of a deteriorated image and an image from which deterioration has been removed; however, it is easy to prepare training data consisting of pairs of a clear image and an image created by adding noise to it. In this study, PDFs of historical documents were collected and converted to text and JPEG images. Noise was added to the JPEG images to create a dataset in which the images had noise similar to that of the actual printed documents. U-Net, a type of neural network, was trained using this dataset. The performance of OCR for an image with noise in the test data was compared with the performance of OCR for an image generated from it by the trained U-Net. An improvement in the OCR recognition rate was confirmed..
13. T. Soeda, Z. Yang, Z. Xiofan, F. Sassa, Y. Tomiura and K. Hayashi, 2D LSPR multi gas sensor array with 4-segmented subpixel using Au/Ag core shell structure, IEEE Sensors, 2019.10.
14. Satoshi Fukuda, Yoichi Tomiura, Emi Ishita, Research Paper Search Using a Topic-Based Boolean Query Search and a General Query-Based Ranking Model, 30th International Conference on Database and Expert Systems Applications (DEXA 2019), 2019.08.
15. Takaaki Soeda, Zhongyuan Yang, Zheng Xiofan, Fumihiro Sassa, Yoichi Tomiura, Kenshi Hayashi, Two dimensional LSPR gas sensor with Au/Ag core-shell structure, 18th International Symposium on Olfaction and Electronic Nose, ISOEN 2019, 2019.05, [URL], If we can quickly recognize the distribution of dangerous gases, it will be useful in places such as disaster scene. Localized surface plasmon resonance (LSPR) gas sensor is known as a gas sensor with high response / recovery speed and high spatial resolution. However, the general LSPR gas sensor does not have a molecular selectivity and it is difficult to identify the gas species. We made gas selected pixelated LSPR substrate based on Au/Ag core-shell structure by photo-induced growth by exposure system using the photomask..
16. R. Marciano, V. Lemieux, M. Hedges, Y. Tomiura, S. Katuu, J. Greenberg, W. Underwood, K. Fenlon, A. Kriesberg, M. Kendig, G. Jansen, P. Piety, D. Weintrop, M. Kurtz, Establishing an International Computational Network for Librarians and Archivists, 14th International Conference on Information in Contemporary Society (iConference2019), 2019.04.
17. Emi Ishita, Satoshi Fukuda, Toru Oga, Douglas W. Oard, Kenneth R. Fleischmann, Yoichi Tomiura, An Shou Cheng, Toward Three-Stage Automation of Annotation for Human Values, 14th International Conference on Information in Contemporary Society (iConference2019), 2019.04, [URL], Prior work on automated annotation of human values has sought to train text classification techniques to label text spans with labels that reflect specific human values such as freedom, justice, or safety. This confounds three tasks: (1) selecting the documents to be labeled, (2) selecting the text spans that express or reflect human values, and (3) assigning labels to those spans. This paper proposes a three-stage model in which separate systems can be optimally trained for each of the three stages. Experiments from the first stage, document selection, indicate that annotation diversity trumps annotation quality, suggesting that when multiple annotators are available, the traditional practice of adjudicating conflicting annotations of the same documents is not as cost effective as an alternative in which each annotator labels different documents. Preliminary results for the second stage, selecting value sentences, indicate that high recall (94%) can be achieved on that task with levels of precision (above 80%) that seem suitable for use as part of a multi-stage annotation pipeline. The annotations created for these experiments are being made freely available, and the content that was annotated is available from commercial sources at modest cost..
18. Yoichi Tomiura, Emi Ishita, Hideaki Uchiyama, Satoshi Fukuda, A Comprehensive Study for Constructing a Large Scale Information Infrastructure of Paper-based Historical Materials, 10th Asia Library and Information Research Group (ALIRG) Workshop, 2018.12.
19. Satoshi Fukuda and Yoichi Tomiura, A Study for the Support of a Search Formula Creation for the Exhaustive search of an Academic Paper based on a User’s Information Need, 10th Asia Library and Information Research Group (ALIRG) Workshop, 2018.12.
20. Emi Ishita, Yasuko Hagiwara, Yoichi Tomiura, Users’ searching behavior for academic papers, Workshop at ICADL2018, 2018.11.
21. Satoshi Fukuda and Yoichi Tomiura, Toward a Search Formula Creation Support for the Exhaustive Search of an Academic Paper, Workshop at ICADL2018, 2018.11.
22. Satoshi Fukuda and Yoichi Tomiura, Clustering of Research Papers based on Sentence Roles, 20th International Conference on Asia-Pacific Digital Library (ICADL 2018), 2018.11.
23. Satoshi Fukuda and Yoichi Tomiura, Exhaustive Search of Academic Paper Using Topic-Based Boolean Query, The 2018 International Symposium on Information Technology Convergence (ISITC 2018), 2018.10.
24. Emi Ishita, Yasuko Hagiwara, Yukiko Watanabe, Yoichi Tomiura, Which Parts of Search Results do Researchers Check when Selecting Academic Documents?, JCDL 2018, 2018.06, Our goal is to propose an alternative retrieval system of academic documents based on researcher’s behavior in practice. In this study, a questionnaire survey was conducted. Question items were developed from findings in the previous observational study for researcher’s behavior. From the results of 46 respondents, the top three elements checked in the search results were title, abstract, and the full-text version. They also checked structure “Introduction” in the full-text rather than other structures when they found previous research in an unfamiliar field. These results indicate that researchers use different ways for selecting documents based on the type of documents they look for..
25. Satoshi Fukuda, Yoichi Tomiura, Using Topic Analysis Techniques to Support Comprehensive Research Paper Searches, 21st International Conference on Asian Language Processing, IALP 2017, 2017.12, In an academic paper search to confirm the originality of a user's research, it is important that the search returns comprehensive results relevant to the user's information need. To achieve comprehensive search results, users often relax initially restrictive search formula by adding synonyms and expressions similar to the search words with operator OR, and/or replacing AND with OR operations. However, it is difficult to anticipate all the terms that authors of relevant papers might have used. In addition, the replacement of AND with OR in search phrases can return a large number of unrelated papers. To overcome these issues, we propose a research paper search method based on topic analysis, which uses Boolean search based on the topics assigned to the search words in the search formula and the abstracts that contain any search word. Our method considers synonyms and expressions similar to the search words, which a user might not anticipate, while limiting the number of papers unrelated to the information need in the search result. To investigate the effectiveness of our method, we conducted experiments using the NTCIR-1 and 2 datasets, and confirmed that our method shows a reduction effect on unrelated papers, while maintaining high coverage..
26. Yasuko Hagiwara, Emi Ishita, Emiko Mizutani, Kana Fukushima, Yukiko Watanabe, Yoichi Tomiura, Identifying Key Elements of Search Results for Document Selection in the Digital Age: An Observational Study, 19th International Conference on Asia-Pacific Digital Libraries, ICADL 2017, 2017.11, Academic database systems are vitally important tools for enabling researchers to find relevant, useful articles. Identifying how researchers select documents from search results is an extremely useful measure for improving the functions or interfaces of academic retrieval systems. This study aims to reveal which elements are checked, and in what order, when researchers select from among search results. It consists of two steps: an observational study of search sessions performed by researchers who volunteered, and a questionnaire to confirm whether extracted elements and patterns are used. This article reports findings from the observational study and introduces questions we developed based on the study. In the observational study we obtained data on nine participants who were asked to search for documents using information retrieval systems. The search sessions were recorded using a voice recorder and by capturing screen images. The participants were also asked to state which elements they checked in selecting documents, along with the reasons for their selections. Three patterns of order of checking were found. In pattern 1, seven researchers used titles and abstracts as the primary elements. In pattern 2, the others used titles and then accessed the full text before making a decision on their selection. In pattern 3, one participant searched for images and accessed the full text from the link in those pictures. We also found participants used novel elements for selecting. We subsequently developed items for a questionnaire reflecting the findings..
27. Ishita, E., Oga, T., Cheng, A.-S., Fleischmann, K.R., Yasuhiro, T., Oard, D.W., Tomiura, Y., Toward automating detection of human values in the nuclear power debate, Association for Information Science and Technology, 2017.10.
28. Yasuko Hagiwara, Emi Ishita, Emiko Mizutani, Yukiko Watanabe, Yoichi Tomiura, A Preliminary Experiment and Analysis to Identify Key Elements in Document Selection, ISIC 2016, 2016.09.
29. Takafumi Yamamoto, Yoichi Tomiura, Constructing Corpus of Scientific Abstracts Annotated with Sentence Roles, Seventh International Conference on E-Service and Knowledge Management, 2016.07.
30. Kohei Omori, Yoichi Tomiura, Kenshi Hayashi, Statistical analysis for clustering of areas on the olfactory bulb and estimation of the physico-chemical properties detected by glomeruli in each area, ISOT 2016, 2016.06.
31. Liang Shang, Chuanjun Liu, Yoichi Tomiura, Kenshi Hayashi, Artificial odor cluster map of odorant molecular parameters and odor maps in rat olfactory bulbs
, ISOT 2016, 2016.06.
32. Takeshi Shirai, Yoichi Tomiura, Shosaku Tanaka, Ryutaro Ono, Mining Latent Research Groups within Institutions Using an Author‐Topic Model, ICADL 2015, 2015.12.
33. Kosuke Furusawa, Hongjun Fan, Yoichi Tomiura, Emi Ishita, Encompassing Retrieval of Academic Papers for User's Information Need
, ICADL 2015, 2015.12.
34. Yasuhiro Takayama, Yoichi Tomiura, Kenneth R. Fleischmann, An-Shou Cheng, Douglas W. Oard, Emi Ishita, An Automatic Dictionary Extraction and Annotation Method Using Simulated Annealing for Detecting Human Values , Sixth International Conference on E-Service and Knowledge Management, 2015.07.
35. Emi Ishita, Douglas W. Oard, Kenneth R. Fleischmann, Yoichi Tomiura, Yasuhiro Takayama, An-Shou Cheng, Learning curves for automating content analysis: How much human annotation is needed ? , Sixth International Conference on E-Service and Knowledge Management, 2015.07.
36. Kenneth R. Fleischmann, Yasuhiro Takayama, An-Shou Cheng, Yoichi Tomiura, Douglas W. Oard, Emi Ishita, Thematic Analysis of Words that Invoke Values in the Net Neutrality Debate, i Conference 2015, 2015.03.
37. Shinjiro Okaku, Yoichi Tomiura, Emi Ishita, Shosaku Tanaka, Towards Generating Multiple-Choice Tests for Supporting Extensive Reading, The Seventh International Conference on Mobile, Hybrid, and On-line Learning (eLmL 2015), 2015.02, We propose a method for generating multiple-choice test for an English text selected by a learner and its answer, that are used to make a self-assessment whether the learner comprehends the text after reading it. In our method, the system extracts several important sentences from the text, and replaces one word in each of these sentences with its synonym (if possible). One of these sentences is then selected as a correct optional sentence, while further changes to the polarities or nouns in the remaining sentences are carried out to generate distractor optional sentences for the multiple-choice test. Our method has potential to make extensive reading in English more effective..
38. Yasuhiro Takayama, Yoichi Tomiura, Emi Ishita, Douglas W. Oard, Kenneth R. Fleischmann, An-Shou Cheng, A Word-Scale Probabilistic Latent Variable Model for Detecting Human Values, ACM International Conference on Information and Knowledge Management (CIKM2014), 2014.12, This paper describes a probabilistic latent variable model that is designed to detect human values such as justice or freedom that a writer has sought to reflect or appeal to when participating in a public debate. The proposed model treats the words in a sentence as having been chosen based on specific values; values reflected by each sentence are then estimated by aggregating values associated with each word. The model can determine the human values for the word in light of the influence of the previous word. This design choice was motivated by syntactic structures such as noun+noun, adjective+noun, and verb+adjective. The classifier based on the model was evaluated on a test collection containing 102 manually annotated documents focusing on one contentious political issue --- Net neutrality, achieving the highest reported classification effectiveness for this task. We also compared our proposed classifier with human second annotator. As a result, the proposed classifier effectiveness is statistically comparable with human annotators..
39. 田中 省作, 冨浦 洋一, Michio Tokumi, 機関リポジトリから得られる著者の語彙分布に基づいた部局別重要語彙の選定, 人文科学とコンピュータシンポジウム　じんもんこ2014, 2014.12.
40. 田中 省作, 冨浦 洋一, 宮崎 佳典, Michio Tokumi, 機関リポジトリの言語資源としての活用　-大学毎の部局別英語重要語彙の選定, 第62回日本図書館情報学会研 究大会, 2014.11.
41. 古澤昂典, 冨浦 洋一, LDA による有意なトピック分析が可能な文書集合の量的な考察, FIT2014 第13回情報科学技技術フォーラム, 2014.09.
42. 末次展章, 冨浦 洋一, LDA を用いたトピック分析におけるトピックの理解容易性, FIT2014 第13回情報科学技技術フォーラム, 2014.09.
43. Shuhei Otani, Yoichi Tomiura, Extraction of Key Expressions Indicating the Important Sentence from Article Abstracts, ESKM 2014, 2014.09.
44. Shinjiro OKaku, Yoichi Tomiura, Kou Shu, Shosaku Tanaka, Towards Generating Multiple-Choice Tests for Evaluating Comprehension of Arbitrary English Texts, ESKM 2014, 2014.09, a.
45. Toshiaki Funatsu, Yoichi Tomiura, Emi Ishita, Kosuke Furusawa, Extracting Representative Words of a Topic Determined by Latent Dirichlet Allocation, eKNOW 2014 (Digital World 2014), 2014.03, Determining the topic of a document is necessary to understand the content of the document efficiently. Latent Dirichlet Allocation (LDA) is a method of analyzing topics. In LDA, a topic is treated as an unobservable variable to establish a probabilistic distribution of words. We can interpret the topic with a list of words that appear with high probability in the topic. This method works well when determining a topic included in many documents having a variety of contents. However, it is difficult to interpret the topic just using conventional LDA when determining the topic in a set of article abstracts found by a keyword search, because their contents are limited and similar. We propose a method to estimate representative words of each topic from an LDA result. Experimental results show that our method provides better information for interpreting a topic than LDA does..
46. 小野 龍太郎, 冨浦 洋一, 田中 省作, 上瀧 恵里子, オーサートピックモデルを用いた論文分析による潜在的研究グループの発掘に関する研究, 言語処理学会第２０回年次大会, 2014.03.
47. Yuichiro Kobayashi, Shosak Tanaka, Yoichi Tomiura, Yoshinori Miyazaki, Michio Tokumi, Identifying Discipline-Specific Expressions Based on Institutional Repository, Digital Humanities, 2014.03,
.
48. 濱田 龍之介, 船津 繁晃, 冨浦 洋一, フレーズ生成機構を組み込んだ潜在変数を有する生成モデルによるトピック分析, 情報処理学会自然言語処理研究会第214回研究発表会, 2013.11.
49. 船津 繁晃, 濱田 龍之介, 末次 展章, 冨浦 洋一, 古澤 昂典, LDAによるトピック分析におけるトピック内容を示す単語の抽出, 第66回電気関係学会九州支部連合大会, 2013.09.
50. Yasuhiro Takayama, Yoichi Tomiura, Emi Ishita, Zheng Wang, Douglas W. Oard, Kenneth R. Fleischmann, An-Shou Cheng, Improving Automatic Sentence-Level Annotation of Human Values Using Augmented Feature Vectors, Pacling, 2013.09, This paper describes an effort to improve identification of human values that are directly or indirectly invoked within the prepared statements of witnesses before legislative and regulatory hearings. We automatically code human values at the sentence level using supervised machine learning techniques trained on a few thousand annotated sentences. To simulate an actual situation, we treat a quarter of the data as labeled for training and the remaining three quarters of the data as unlabeled for test. We find that augmenting the feature space using a combination of lexical and statistical co-occurrence evidence can yield about a 6% relative improvement in F1 using a Support Vector Machine classifier. .
51. 田中 省作, 冨浦 洋一, 機関リポジトリを活用した英語学術表現リストの階層的構築, 言語処理学会, 2013.03,

.
52. 田中省作, 冨浦 洋一, 宮崎佳典, 小林雄一郎, 徳見 道夫, 機関リポジトリを活用した部局別英語学術表現リストの作成支援, 情報処理学会, 2013.03.
53. 田中省作, 小林雄一郎, 徳見道夫, 後藤一章, 冨浦洋一, 柴田雅博, 学校英文法の学参例文データベースとその応用, 情報処理学会人文科学とコンピュータ研究会第93回研究発表会, 2012.01.
54. 小林雄一郎, 田中省作, 冨浦洋一, N-gramを素性とするパタン認識を用いた英語科学論文の質判定, 情報処理学会自然言語処理研究会第205回研究発表会, 2012.01.
55. 高山泰博, 冨浦洋一, 情報利用の有効性の観点からの評価表現の分析, 情報処理学会自然言語処理研究会第205回研究発表会, 2012.01.
56. 小林雄一郎, 田中省作, 冨浦洋一, メタ談話標識を素性とするパターン認識を用いた英語科学論文の質判定, 情報処理学会人文科学とコンピュータ研究会シンポジウム「じんもんこん2011」, 2011.12.
57. 上田修一，杉本重雄，冨浦洋一，石田栄美, ライブラリーサイエンスのチカラ, 第41回ディジタル図書館ワークショップ 情報処理学会 第104回情報基礎とアクセス技術研究会 合同研究会 , 2011.11.
58. 田中省作，冨浦洋一，徳見道夫, 学校文法に基づいた英文解析による言語データの頻度分析, 英語コーパス学会第３７回大会, 2011.10.
59. M. Shibata, T. Funatsu, Y. Tomiura, Extraction of Alternative Candidates for Unnatural Adjective-Noun Co-occurrence Construction of English, Pacific Association for Computational Linguistics (PACLING'11), 2011.07.
60. 小林雄一郎，田中省作，冨浦洋一, ランダムフォレストを用いた英語科学論文の分類と評価, 情報処理学会　人文科学とコンピュータ研究会第９０回研究発表会, 2011.05, 近年、非母語話者が書く英語科学論文（以後、論文）と母語話者の論文の分類を通して、両者の様々な言語的差異を抽出することが試みられている。本発表では、まず、論文中の談話表現に着目し、その頻度を素性の候補とするランダムフォレストに基づく分類器を構築する。その分類精度は約90%で類似研究の中でも高いものであった。そして、構築された分類器の素性を分析することで、非母語話者の論文に特徴的な談話表現を抽出する。.
61. Atsushi TAGAMI, Shigehiro ANO, Yoichi TOMIURA, Simulation Analysis of Moving Peer Influence on Location-aware P2P Network, International Conference on Advanced Information Networking and Applications (AINA'10), 2010.04.
62. Teiko NAKANO, Yoichi TOMIURA, Evaluation of a Japanese Composition Support System, IADIS International Conference e-Society 2010, 2010.03.
63. Teiko NAKANO, Yoichi TOMIURA, Providing Appropriate Alternative Co-occurrence Candidates; Towards a Japanese Composition Support System, The Ninth IASTED International Conference on Web-Based Education (WBE2010), 2010.03.
64. 田中省作，冨浦洋一，安東奈穂子，柴田雅博, Webを源とした英語科学技術論文コーパスの構築 －技術的方法論と法的観点からの検討―, 英語コーパス学会第34回大会, 2009.10.
65. 水田貴章，冨浦洋一，柴田雅博，木村　恵, 母語話者/非母語話者英語論文コーパスを用いた不自然な英語表現の抽出, 第62回電気関係学会九州支部連合大会, 2009.09.
66. Masahiro SHIBATA, Yoichi TOMIURA, Takaaki MIZUTA, Identification among Similar Languages Using Statistical Hypothesis Testing, Pacific Association for Computational Linguistics (PACLING'09), 2009.09.
67. 田中省作，冨浦洋一, 分割表に対する漸近近似検定としてのχ2検定, 英語コーパス学会第33回大会, 2009.04.
68. Teiko NAKANO, Yoichi TOMIURA, Measure of Appropriateness of Word Co-occurrence in Japanese for Specific Purpose: Towards a Support System Framework for Writing Technical Japanese, Empirical Methods for Asian Language Processing Workshop, 2008.12.
69. 水田 貴章，柴田 雅博，冨浦 洋一, 仮説検定に基づいた言語識別, 情報処理学会自然言語処理研究会, 2008.11.
70. Masahiro Shibata, Tomomi Nishiguchi, Yoichi Tomiura, A Method for Automatically Generating Proper Responses to User's Utterances in Open-ended Conversation by Retrieving Documents on the Web, 2008 IEEE International Conference on Information Reuse and Integration (IEEE IRI'08), 2008.07.
71. Atsushi TAGAMI, Chikara SAKAKI, Teruyuki HASEGAWA, Shigehiro ANO, Yoichi TOMIURA, Optimization of Answering Method with Probability Conversion, 2008 International Symposium on Applications and the Internet (SAINT'08), 2008.07.
72. Atsushi TAGAMI, Chikara SAKAKI, Teruyuki HASEGAWA, Shigehiro ANO, Yoichi TOMIURA, Analysis of Answering Method with Probability Conversion for Internet Research, Fifth IEEE Consumer Communications & Networking Conference (CCNC'08), 2008.01.
73. 西口 友美，冨浦 洋一，柴田 雅博, 話題の遷移と意味的関連性を利用した対話システムの開発, 合同エージェントワークショップ＆シンポジウム(JAWS), 2007.10.
74. 青木 さやか，冨浦 洋一，柴田 雅博, Web上からの母語話者英論文・非母語話者英論文の自動収集システム, 合同エージェントワークショップ＆シンポジウム(JAWS), 2007.10.
75. 田中省作，冨浦洋一，行野顕正，池田洋子，徳見道夫，木村 恵, 言語処理技術を活用したスラッシュ・リーディング用教材の開発, JUCE 大学教育・情報戦略大会, 2007.09.
76. 柴田 雅博，冨 浦洋一，西口友美, Web文書を言語資源とする情報検索型対話システム, 人工知能学会, 2007.07.
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78. Masahiro Shibata, Youichi Tomiura, Hideki Matsumoto, Tomomi Nishiguchi, Kensei Yukino, Akihiro Hino, Developing a Dialog System for New Idea Generation Support, 21st International Conference on the Computer Processing of Oriental Languages, 2006.12.
79. 田中 省作，行野 顕正，冨浦 洋一，北尾 謙治，木村 恵, 柔軟性の高いスラッシュ・リーディング用教材作成支援システム
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80. 谷川 龍司，冨浦 洋一，行野 顕正, 母語話者・非母語話者文書を利用したコロケーション誤り指摘システム, 電気関係学会九州支部連合大会, 2006.09.
81. 馬場 慎也，冨浦 洋一, AIC基準に基づく事例列からの格フレームの獲得, 電気関係学会九州支部連合大会, 2006.09.
82. 西口 友美，柴田 雅博，冨浦 洋一，松本 英樹, 思索支援を目的とした対話システム, 電気関係学会九州支部連合大会, 2006.09.
83. 青木 さやか，冨浦 洋一，行野 顕正，谷川 龍司, 言語識別技術を応用した英語における母語話者文書・非母語話者文書の判別, FIT2006, 2006.09.
84. 行野顕正，馬場慎也，田中省作，冨浦洋一, 確率モデルによる英文へのスラッシュ自動挿入手法, 情報処理学会 火の国シンポジウム2006, 2006.03.
85. 松島 洋介，冨浦 洋一, 大規模文章からの意味情報の獲得に関する研究, 第2回人工頭脳工学シンポジウム, 2006.03.
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87. M. Kimura, S. Tanaka, Y. Tomiura, Tracing Japanese EFL Learners' Development in Productive Vocabulary, The NICT JLE Corpus Symposium, 2005.11.
88. K. Yukino, S. Tanaka, Y. Tomiura, H. Matsumoto, Robust Language Identification for Similar Languages and short texts using Low-Frequent Byte Strings, Pacific Association for Computational Linguistics 2005 (Pacling 2005), 2005.08.
89. 田中 省作，冨浦洋一, 類語集合の対応関係に基づく英語を介した対訳辞書の合成, JACET第6回英語辞書学ワークショップ2005, 2005.03.
90. S. Tanaka, Y. Tomiura, K. Yukino, A System for Extensive Slash Reading Using Web, An Interactive Workshop on Language e-Learning (IWLeL2004), 2004.12.
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92. M. Motoki, Y. Tomiura, N. Takahashi, Problems of FGREP Module and Their Solution, 3rd IEEE International Conference on Cognitive Informatics (ICCI2004), 2004.08.
93. M. Shibata, Y. Tomiura, S. Tanaka, A Method for Retrieving Translations of Collocation in Web Data, IJCNLP-04 Satellite Symposium, 2004.03.
94. 冨浦洋一，田中省作，日高 達, 共起データに基づく名詞のn次元空間への配置, 情報処理学会 自然言語処理研究会, 2003.03.
95. 田中省作，冨浦洋一, 類語集合による英語を介して導出した対訳候補の絞り込み, 情報科学技術フォーラム, 2002.09.
96. 冨浦洋一，田中省作，日高達, 言語コーパスからの語の共起性の推定, 言語処理学会第8回年次大会, 2002.03.
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100. D. トウシンバット，冨浦洋一，日高 達, 係り受け文脈自由文法の強化法, 情報処理学会自然言語処理研究会, 1998.11.
101. 冨浦洋一，日高 達, スパースな学習データにおける PCFG の確率パラメタの推定法, 電子情報通信学会　言語理解とコミュニケーション研究会, 1998.07.
102. 田中省作，冨浦洋一，日高 達, 統計的手法を用いた名詞句「NP の NP」の意味関係の抽出法, 電子情報通信学会　言語理解とコミュニケーション研究会, 1998.05.
103. 田中省作，飯田健二，冨浦洋一，日高 達, 名詞句「NP の NP」の意味関係とその統計的性質, 言語処理学会第４回年次大会, 1998.03.


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