Kyushu University Academic Staff Educational and Research Activities Database
List of Papers
Yoichi Tomiura Last modified date:2018.10.19

Professor / Intelligence Science / Department of Informatics / Faculty of Information Science and Electrical Engineering

1. Liang Shang, Chuanjun Liu, Yoichi Tomiura, Kenshi Hayashi, Odorant clustering based on molecular parameter-feature extraction and imaging analysis of olfactory bulb odor maps, Sensors and Actuators, B: Chemical, 10.1016/j.snb.2017.08.024, 255, 508-518, 2018.02, Progress in the molecular biology of olfaction has revealed a close relationship between the structural features of odorants and the response patterns they elicit in the olfactory bulb. Molecular feature-related response patterns, termed odor maps (OMs), may represent information related to basic odor quality. Thus, studying the relationship between OMs and the molecular features of odorants is helpful for better understanding the relationships between odorant structure and odor. Here, we explored the correlation between OMs and the molecular parameters (MPs) of odorants by taking OMs from rat olfactory bulbs and extracting feature profiles of the corresponding odorant molecules. 178 images of glomerular activities in olfactory bulb that are corresponding to odorants were taken from the OdorMapDB, a publicly accessible database. The gray value of each pixel was extracted from the images (178 × 357 pixels) to fabricate an image matrix for each odorant. Forty-six molecular feature parameters were calculated using BioChem3D software, which was used to construct a second matrix for each odorant. Correlation analysis between the two matrixes was first carried out by establishing coefficient maps. Results from hierarchical clustering showed that all parameters could be segregated into seven clusters, and each cluster showed a relatively similar response pattern in the olfactory bulb. Using the information from the OMs and MPs, we mapped odorants in 2D space by incorporating dimension-reducing techniques based on principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). Artificial neural network models based on the OM and MP feature values were proposed as a means to identify odorant functional groups. An OM-PCA-based model calibrated via extreme learning machine (ELM) was 94.81% and 93.02% accuracy for the calibration and validation sets, respectively. Similarly, an MP-t-SNE-based model calibrated by ELM was 86.67% and 93.35% accuracy for the calibration set and the validation set, respectively. Thus, this research supports a structure-odor relationship from a data-analysis perspective..
2. Satoshi Fukuda, Yoichi Tomiura, Using Topic Analysis Techniques to Support Comprehensive Research Paper Searches, 21st International Conference on Asian Language Processing, IALP 2017, 10.1109/IALP.2017.8300606, 314-317, 2018.01, 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..
3. 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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10647 LNCS, 10.1007/978-3-319-70232-2_20, 237-242, 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..
4. Liang Shang, Chuanjun Liu, Yoichi Tomiura, Kenshi Hayashi, Machine-Learning-Based Olfactometer:
Prediction of Odor Perception from Physicochemical Features of Odorant Molecules, Analytical Chemistry, 10.1021/acs.analchem.7b02389, 89, 22, 11999-12005, 2017.11, Gas chromatography/olfactometry (GC/O) has been used in various fields as a valuable method to identify odor-active components from a complex mixture. Since human assessors are employed as detectors to obtain the olfactory perception of separated odorants, the GC/O technique is limited by its subjectivity, variability, and high cost of the trained panelists. Here, we present a proof-of-concept model by which odor information can be obtained by machine-learning-based prediction from molecular parameters (MPs) of odorant molecules. The odor prediction models were established using a database of flavors and fragrances including 1026 odorants and corresponding verbal odor descriptors (ODs). Physicochemical parameters of the odorant molecules were acquired by use of molecular calculation software (DRAGON). Ten representative ODs were selected to build the prediction models based on their high frequency of occurrence in the database. The features of the MPs were extracted via either unsupervised (principal component analysis) or supervised (Boruta, BR) approaches and then used as input to calibrate machine-learning models. Predictions were performed by various machine-learning approaches such as support vector machine (SVM), random forest, and extreme learning machine. All models were optimized via parameter tuning and their prediction accuracies were compared. A SVM model combined with feature extraction by BR-C (confirmed only) was found to afford the best results with an accuracy of 97.08%. Validation of the models was verified by using the GC/O data of an apple sample for comparison between the predicted and measured results. The prediction models can be used as an auxiliary tool in the existing GC/O by suggesting possible OD candidates to the panelists and thus helping to give more objective and correct judgment. In addition, a machine-based GC/O in which the panelist is no longer needed might be expected after further development of the proposed odor prediction technique..
5. Emi Ishita, Toru Oga, An Shou Cheng, Kenneth R. Fleischmann, Yasuhiro Takayama, Douglas W. Oard, Yoichi Tomiura, Toward Automating Detection of Human Values in the Nuclear Power Debate, Proceedings of the Association for Information Science and Technology, 10.1002/pra2.2017.14505401127, 54, 1, 714-715, 2017.10, This paper describes the creation of a corpus of newspaper articles about the Fukushima nuclear disaster, a coding frame for content analysis of human values expressed or reflected in that corpus and preliminary results for automation of the content analysis. Understanding the human values that motivate sentiment towards an idea can help to characterize the basis for that sentiment, and this work is a first step toward applying that approach to positions on controversial events reported in the news..
6. Takafumi Yamamoto, Yoichi Tomiura, Constructing Corpus of Scientific Abstracts Annotated with Sentence Roles, Proc. Seventh International Conference on E-Service and Knowledge Management, 2016.07.
7. Yasuhiro Takayama, Yoichi Tomiura, Kenneth R. Fleischmann, An-Shou Cheng, Douglas W. Oard, Emi Ishita, Automatic Dictionary Extraction and Content Analysis Associated with Human Values, Information Engineering Express, International Institute of Applied Informatics, Vol.1, No.4, 107 – 118, 2015.12.
8. 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 (Special Session on Library Science), 2015.07.
9. 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 (Special Session on Library Science), 2015.07.
10. 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, Proc. i Conference 2015, 2015.03.
11. Shinjiro Okaku, Yoichi Tomiura, Emi Ishita, Shosaku Tanaka, Towards Generating Multiple-Choice Tests for Supporting Extensive Reading, Proc. 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..
12. 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, Proc. 23th ACM International Conference on Information and Knowledge Management (CIKM 2014), 1-10, 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..
13. Shuhei Otani, Yoichi Tomiura, Extraction of Key Expressions Indicating the Important Sentence from Article Abstracts, Proc. IIAI 3rd International Conference on Advanced Applied Informatics, 216-219, 2014.08, In this study, we aim to extract key expressions that indicate the important sentence describing the originalities or contributions from article abstracts. The expense of searching academic information increases because of increases in the number of articles, discipline subdivisions, and promotion of interdisciplinary research. Improving the extraction and presentation of the main points from article abstracts will contribute to reducing academic information search expenses. We extracted pseudo-important sentences from each article abstract based on the ratio of the number of words in the identified sentence that appear in the article title to the number of all words in the sentence. After that, we evaluated the ratio of the number of the pseudo-important sentences including each Ngram to the number of all sentences including that N-gram. We then extracted the N-grams with a ratio as high as the key expressions..
14. Shinjiro Okaku, Yoichi Tomiura, Kou Shu, Shosaku Tanaka, Towards Generating Multiple-Choice Tests for Evaluating Comprehension of Arbitrary English Texts, Proc. IIAI 3rd International Conference on Advanced Applied Informatics, 220-225, 2014.08.
15. Toshiaki Funatsu, Yoichi Tomiura, Emi Ishita, Kosuke Furusawa, Extracting Representative Words of a Topic Determined by Latent Dirichlet Allocation, Proc. The Sixth International Conference on Information, Process, and Knowledge Management (eKNOW 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..
16. Yuichiro Kobayashi, Shosak Tanaka, Yoichi Tomiura, Yoshinori Miyazaki, Michio Tokumi, Identifying discipline-specific expressions based on institutional repository, Proc. Digital Humanities Australasia 2014, 2014.03, From the early 1960s, English for the Specific Purposes (ESP) has become one of the prominent fields of English teaching (e.g. Hutchinson and Waters, 1987). It aims to provide learners with academic English skills in a context of tertiary education. In terms of English vocabulary, the Academic Word List (AWL) contains 570 word families selected from academic texts (Coxhead, 2000). The list has been widely known to both academic English teachers and researchers, but does not cover context-bound or topic-dependent vocabulary frequently used in each discipline and sub-discipline.

The most significant words in each academic context differ depending on the discipline, university, laboratory or researcher. Therefore, it is necessary to compile a large corpus which covers a wide range of academic domains to create a more appropriate word list for researchers in different academic fields. What is more, it is important for language learners to acquire discipline-specific expressions to achieve native-like performance in an ESP context.

The purpose of this study is to identify discipline-specific expressions, especially multi-word expressions, using institutional repositories and natural language processing techniques. An institutional repository is an online collection of the intellectual output of a research institution. The recent trend of constructing institutional repository in Japan allows researchers to share the same academic resources. The present study demonstrates the effectiveness of creating a multi-word list specific to an institution.

We used a gapped n-gram approach for the identification of discipline-specific expressions. An ‘n-gram’ is a contiguous sequence of items, such as words or part-of speech tags, and a ‘gapped n-gram,’ or ‘skipgram,’ is a refinement of the n-gram approach designed to detect non-contiguous item associations (e.g. Cheng, Greaves, and Warren, 2006). Our algorithm for extracting gapped n-grams is based on the method proposed by Kozawa, Sakai, Sugiki, and Matsubara (2010). First, we filtered the texts in the institutional repository by the number of words to exclude too short or too long writings. Second, we used TreeTagger, an automatic chunking program, to identify the basic phrase and clause structures. Third, we detected general patterns of multi-word expressions by lemmatizing words except for participial verbs and by replacing determiners and cardinal numbers into part-of-speech tags. Finally, we counted the number of gapped n-grams occurred in the writings collected from each academic disciplines.

This method enabled us to effectively detect frequent patterns characteristic of each discipline. We applied the method to the QIR, the institutional repository of Kyushu University, using the repository of the Faculty of Information Science and Electrical Engineering (ISEE), and identified discipline-specific expressions for each faculty at the university as an example of the automatic detection of gapped n-grams. We collected 229 writings in the QIR, and successfully obtained 1,061 frequent phrasal expressions specific to ISEE. We expect this study contributes to teaching English for the Specific Purposes and the application of digital library to linguistic research..
17. 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, Proc. Pacific Association for Computational Linguistics (PACLING'13), 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. .
18. Masahiro Shibata, Toshiaki Funatsu, Yoichi Tomiura, Extraction of Alternative Candidates for Unnatural Adjective-Noun Co-occurrence Construction of English, Procedia - Social and Behavioral Sciences, 27, pp.32--41, 2011.12.
19. A Platform for Context-aware Notification Service with Location-aware P2P Network.
20. Relationship between Errors and Corrections in Verb Selection: Basic Research for Composition Support,
Journal of Natural Language Processing, Vol.18, No.1, pp.3-29.
21. A System Providing Appropriate Alternative Candidates for Japanese Writing using Word Co-occurrence,
T. Nakano, Y. Tomiura, Jpn. J. Educ. Technol., 34(3), pp.181-189 (2010).
22. Atsushi TAGAMI, Shigehiro ANO, Yoichi TOMIURA, Simulation Analysis of Moving Peer Influence on Location-aware P2P Network, Proc. of International Conference on Advanced Information Networking and Applications (AINA'10), pp.1121--1127, 2010.04.
23. Teiko NAKANO, Yoichi TOMIURA, Evaluation of a Japanese Composition Support System, Proc. of IADIS International Conference e-Society 2010, pp.396--400, 2010.03.
24. Teiko NAKANO, Yoichi TOMIURA, Providing Appropriate Alternative Co-occurrence Candidates; Towards a Japanese Composition Support System, Proc. of the Ninth IASTED International Conference on Web-Based Education, pp. 173--179, 2010.03.
25. Method for Selecting Appropriate Sentence from Documents on the WWW for the Open-ended Conversation Dialog System.
26. Masahiro Shibata, Tomomi Nishiguchi, Yoichi Tomiura , Dialog System for Open-ended Conversation Using Web Documents, Informatica, Vol.33, No.3, pp.277-284, 2009.10.
27. M. Shibata, Y. Tomiura, T. Mizuta, Identification among Similar Languages Using Statistical Hypothesis Testing
, Proc. of Pacific Association for Computational Linguistics (PACLING'09) , pp.47--52 , 2009.09.
28. Analysis of Answering Method with Probability Conversion for Internet Research.
29. Discernment of Nativeness of English Documents Based on Statistical Hypothesis Testing,
Y. Tomiura, S. Aoki, M. Shibata, K. Yukino,
Journal of Natural Language Processing, Vol.16, No.1, pp.23-46 (2009).
30. Teiko NAKANO, Yoichi TOMIURA, Measure of Appropriateness of Word Co-occurrence in Japanese for Specific Purposes: Towards a Support System Framework for Writing Technical Japanese, Proc. of Empirical Methods for Asian Language Processing Workshop, pp.133-147, 2008.12.
31. Use of Fast Algorithm for Adaptive Background Modeling with Parzen Density Estimation to Detect Objects,
T. Tanaka, A. Shimada, D. Arita, R. Taniguchi, Y. Tomiura,
The Journal of The Institute of Image Information and Television Engineers,
Vol.62, No.12, pp.2045-2052.
32. Atsushi TAGAMI, Chikara SAKAKI, Teruyuki HASEGAWA, Shigehiro ANO, Yoichi TOMIURA, Optimization of Answering Method with Probability Conversion, Proc. of 2008 International Symposium on Applications and the Internet (SAINT'08), pp.249-252, 2008.07.
33. 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, Proc. of 2008 IEEE International Conference on Information Reuse and Integration (IEEE IRI'08), pp.268-279, 2008.07.
34. 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), pp.110-111, 2008.01.
35. M. Shibata, Y. Tomiura, H. Matsumoto, T. Nishiguchi, K. Yukino, A. Hino, Developing a Dialog System for New Idea Generation Support, Proc. of 21st International Conference on Computer Processing of Oriental Language, 2006.12.
36. Documents Discrimination between Native English Documents and Nonnative Ones Based on Language Identification Technique

S. Aoki, Y. Tomiura, K. Yukino, R. Tanigawa

Information Technology Letters, Vol.5, pp.85-88.
37. A Learning Method of a Layered Neural Network Whose Inputs and Outputs are Symbol Sequences,

M. Motoki, Y. Tomiura, N. Takahashi

IPSJ Journal, Vol.47, No.8, pp.2279--2791.
38. Language Identification Using Low-frequent Byte-strings

K. Yukino, S. Tanaka, Y. Tomiura, H. Matsumoto

IPSJ Journal, Vol.47, No.4, pp.1287--1294.
39. Y. Tomiura, S. Tanaka, T. Hitaka, Estimating Satisfactoriness of Selectional Restriction from Corpus without Thesaurus, ACM Transactions on Asian Language Information Processing, Vol.4, No.4, pp.400--416, 2005.12.
40. 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), pp.368-373, 2005.08.
41. Estimation of Nativeness of Documents Based on Skew Divergence,
H. Fujii, Y. Tomiura, S. Tanaka,
Journal of Natural Language Processing, Vol. 12, No. 4, pp.79-96 (2005).
42. Assisting with Translating Collocations Based on the Word Co-occurrence on the Web Texts,
M. Shibata, Y. Tomiura, S. Tanaka,
IPSJ Journal, Vol.46, No.6, pp.1480-1491 (2005).
43. Implementation of Writing Support System for Translating Collocations Based on the Word Co-occurrence on the Web Documents

M.Shibata, S.Tanaka, Y.Tomiura

Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.10, No.1, pp.39--44.
44. Examination about Usefulness of the Method for Translating Collocations Based on the Word Co-occurence on the Web Documents

M.Shibata, S.Tanaka, Y.Tomiura

Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.10, No.1, pp.45--49.
45. S. Tanaka, Y. Tomiura, K. Yukino, A System for Extensive Slash Reading Using Web, An Interactive Workshop on Language e-Learning (IWLeL2004), pp.133-138, 2004.12.
46. Estimation of Synonym's Mappings and its Application to Compiling Translational Dictionary using English,
S. Tanaka, Y. Marubayashi, Y. Tomiura,
Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.9, No.2, pp.73-78.
47. Discrimination of Native/Non-native Documents Based on Skew Divergence,
H. Fujii, S. Tanaka, Y. Tomiura,
Information Technology Letters, Vol.3, pp.81-83 (2004).
48. M. Motoki, Y. Tomiura, N. Takahashi, Problems of FGREP Module and Their Solution, 3rd IEEE International Conference on Cognitive Informatics (ICCI2004), 10.1109/COGINF.2004.1327479, 220-227, pp.220-227, 2004.08.
49. Masahiro SHIBATA, Yoichi TOMIURA, Shosaku TANAKA, A Method for Retrieving Translations of Collocation in Web Data, Asian Symposium on Natural Language Processing to Overcome Language Barriers (in conjunction with IJCNLP-04), 2004.03.
50. Parsing Method for the Translation Grammar

M. Shibata, Y. Tomiura, T. Hitaka

Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.9, No.1, pp.31--36.
51. Generation of Texts for Slash Reading Based on Chunking Process

S.Tanaka, Y.Tomiura, S.Yamamoto

Annual Report on Computing and Communication Center, Vol.4, pp.1--8.
52. Estimation of Words' Cooccurrency from Corpus


IPSJ Journal, Vol.45, No.1, pp.324-332.
53. Placement of Nouns in a Multi-Dimensional Space Based on Words' Cooccurrency


Transactions of the Japanese Society for Artificial Intelligence, Vol.19, No.1A, pp.1-9.
54. Machine Translation using Translation Grammar


Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.8, No.1, pp.61-66.
55. Judgement on Validity of Candidates of Translations Dirived via English Using Sets of Synonyms

Shosaku TANAKA, Yoichi TOMIURA

Forum on Information Technology, Information Technology Letters, pp.75-76.
56. mplementation of The Machine Translation System Using The Translation Context Free Grammar

Masahiro Shibata, Toru Hitaka, Yoichi Tomiura

Annual Report of Computing and Communication Center Kyushu University, No.2, pp.71-79.
57. TAKAHASHI Naoto, MOTOKI Minoru, SHIMAZU Yoshio, TOMIURA Yoichi, HITAKA Toru, PP-attachment Ambiguity Resolution Using a Neural Network wiht Modified FGREP Method, the 2nd Workshop on Natural Language Processing and Neural Networks (post-conference workshop of NLPRS2001), pp.1-7, 2001.11.
58. An Efficient Parsing Algorithm for CFG with Robust Dependency Constraints

Toushinbatto D., Yoichi TOMIURA, Toru HITAKA

Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.5, No.2, pp223-227.
59. Disambiguation of Syntactic Structures of Japanese Noun Phrases "NP 'no' NP" by PCFG Having Cooccurence Constraints Embedded


Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.5, No.1, pp.69-74.
60. Context Free Grammar Expressing Dependency Constraint and its Application to Japanese Language

Toshifumi TANABE, Yoichi TOMIURA, Toru HITAKA

IPSJ Journal, Vol.41, No.1, pp.36 - 45.
61. A Parameter Estimation of PCFG Expressing Dependency Constraints on a Sparse Sample


IPSJ Journal, Vol.40, No.11, pp.4055 - 4063.
62. Classification of Syntactic Categories of Nouns by the Scattering of Semantic Categories


IPSJ Journal, Vol.40, No.9, pp.3387 - 3396.
63. Estimating Semantic Relation of Japanese Noun Phrases based on k-Nearest Neighbor Estimation


Research Reports on Information Science and Electrical Engineering of Kyushu University, Vol.4, No.2, pp.159-164.
64. Semantic Structure of Japanese Noun Phrasese "NP 'no' NP",
IPSJ Journal, Vol.36, No.6, pp.1441-1448 (1995).
65. Preference on Common Sense Reasoning and Application to Contextual Processing,
IPSJ Journal, Vol.35, No.11, pp.2239 - 2248 (1994).
66. A New Data Structure for Searching Prefix Words: Prefix-Closed B-tree,
Yoichi TOMIURA, Teigo NAKAMURA, Toru Hitaka,
IPSJ Journal. Vol.35, No.5, pp.779-789 (2007).
67. Semantic Validity of Japanese Noun Phrases with Adnominal Particles,
in Proc. of PRICAI'92, Vol.1, No.2, pp.433-437 (1992).
68. Y. TOMIURA, T. NAKAMURA, T. HITAKA, S. YOSHIDA, Logical Form of Hierarchical Relation on Verbs and Extracting it from Definition Sentences in a Japanese Dictionary, Proc. of the th International Conference on Computational Linguistics(Coling-92), Vol.2, No.14, pp.574-580, 1992.07.
69. Extracting the Superordinate-Subordinate Relation between Verbs from Definition Sentences in Japanese Dictionaries,
IPSJ Journal, Vol.32, No.1, pp.42 - 49 (1991).