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2019ǯ 12·î ½¤Î»

  • Construction and Analysis of Multiword Expression-Aware Dependency Corpus
    • ²ÃÆ£ ÌÀɧ
    • [ PDF ]

2019ǯ 9·î ½¤Î»

  • Relation Extraction: Perspective from Weakly Supervised Methods
    • Phi Van Thuy
    • [ PDF ]

2019ǯ 6·î ½¤Î»

  • Computer-assisted Japanese Functional Expression Learning for Chinese-speaking Learners
    • Liu Jun
    • [ PDF ]

2019 (Ê¿À®31) ǯ 3·î ½¤Î»

  • Perspectives on the Making of Multiple Emotion Detection System in Text
    • Phan Duc Anh
    • NAIST-IS-DD1461213[ PDF ]
  • Context Enhancement of Recurrent Neural Network Language Models for Automatic Speech Recognition
    • Michael Alexander Hentschel
    • [ PDF ]

2018 (Ê¿À®30) ǯ 3·î ½¤Î»

  • Studies on improving two fundamental steps for Chinese natural language processing: word segmentation and spelling check
    • Äø Èô
    • NAIST-IS-DD1361023 [ PDF ]
  • An Exemplar-Based Approach to Word Representation for Information Extraction
    • ßÀ¸ý ÂóÃË
    • NAIST-IS-DD1461008 [ PDF ]
  • A Study on Syntactic and Semantic Dependency Parsing
    • ÂçÆâ ·¼¼ù
    • NAIST-IS-DD1561004 [ PDF ]
  • Neural Substrates of Individual Difference from Whole-brain Functional Connectivity
    • ¹âÌÚ Í¥
    • NAIST-IS-DD1561014 [ PDF ]

2017 (Ê¿À®29) ǯ 6·î ½¤Î»

  • Generating and Exploiting Language Resources for Indonesian Preposition Error Correction
    • Budi Irmawati
    • NAIST-IS-DD1261204 [ PDF ]
  • Hierarchical Word Sequences Structures for Language Modeling
    • ¸â ¶Ç°ì
    • NAIST-IS-DD1361024 [ PDF ]

2017 (Ê¿À®29) ǯ 3·î ½¤Î»

  • Improving Nearest Neighbor Methods from the Perspective of Hubness Phenomenon
    • ½ÅƣͥÂÀϺ
    • NAIST-IS-DD1461004 [ PDF ]
  • Recurrent Neural Networks for Natural Language and Biological Sequence
    • ÄØ¿¿»Ë
    • NAIST-IS-DD1461006 [ PDF ]
  • Improving Formal Document Translation Using Sublanguage-Specific Sentence Structure
    • Éٻν¨
    • NAIST-IS-DD1561018 [ PDF ]
  • Perspectives on the Marking of Discourse Relations: Cognitive Models and Machine Translation
    • YUNG PIKYU FRANCES
    • NAIST-IS-DD1461023 [ PDF ]

2016 (Ê¿À®28) ǯ 3·î ½¤Î»

  • Collocation Writing Assistant for Learners of Japanese as a Second Language
    • Lis Weiji Kanashiro Pereira
    • NAIST-IS-DD1361016 [ PDF ]
  • Automatic Error Tag Annotation on the Writing of Japanese Language Learners for Linguistic and Educational Research
    • Â绳¹ÀÈþ
    • NAIST-IS-DD0661003 [ PDF ]

2015 (Ê¿À®27) ǯ 9·î ½¤Î»

  • Latent Variable Models for Bag-of-Words Data Based on Kernel Embeddings of Distributions
    • µÈÀîͧÌé
    • NAIST-IS-DD1361013 [ PDF ]

2015 (Ê¿À®27) ǯ 6·î ½¤Î»

  • Automated Grammatical Error Correction Using Statistical Machine Translation Techniques with Revision Log of Language Learning SNS
    • ¿åËÜ ÃÒÌé
    • NAIST-IS-DD1261016[ PDF ]

2015 (Ê¿À®27) ǯ 3·î ½¤Î»

  • Åý·×Ūµ¡³£³Ø½¬¤òÍѤ¤¤¿ÆüËܸìÎò»Ë¥³¡¼¥Ñ¥¹¹½ÃÛ»þ¤Îɽµ­À°Íýºî¶È¤Î¼«Æ°²½
    • ²¬ ¾È¹¸
    • NAIST-IS-DD1261003[ PDF ]
  • Bilingual Dictionary Extraction via Multilingual Topic Models
    • Xiaodong Liu
    • NAIST-IS-DD1161205 [ PDF ]

2014 (Ê¿À®26) ǯ 3·î ½¤Î»

  • ÆüËܸìÄÌ»þ¥³¡¼¥Ñ¥¹¤Î¤¿¤á¤Î·ÁÂÖÏÀ¾ðÊ󥢥Υơ¼¥·¥ç¥ó¤Î¸¦µæ
    • ¾®ÌÚÁ¾ ÃÒ¿®
    • NAIST-IS-DD1161018 [ PDF ]
  • Japanese Predicate Argument Structure Analysis Based on Positional Relations between Predicates and Arguments
    • ÎÓÉô Í´ÂÀ
    • NAIST-IS-DD1161009 [ PDF ]

2013 (Ê¿À®25) ǯ 9·î ½¤Î»

  • Syntactic Dependency Structure-based Approaches for Chinese Semantic Role Labeling
    • Íå ɧɧ
    • NAIST-IS-DD1061033 [ PDF ]
  • Automatic Summarization on Various Domains with Combinatorial Optimization and Machine Learning
    • À¾Àî ¿Î
    • NAIST-IS-DD1261010 [ PDF ]
  • Statistical Induction of Tree-Generating Grammars for Natural Language Parsing
    • ¿ÊÆ£ ͵Ƿ
    • NAIST-IS-DD1261202[ PDF ]

2013 (Ê¿À®25) ǯ 3·î ½¤Î»

  • A Study of Algebraic and Graph-Theoretic Frameworks for Generalized Forward-Backward Algorithms
    • Åì Íõ
    • NAIST-IS-DD0561001 [ PDF ]
  • Techniques for Improving Transition-based Dependency Parsing Algorithms
    • ÎÓ ¹îɧ
    • NAIST-IS-DD1061018 [ PDF ]

2012 (Ê¿À®24) ǯ 9·î ½¤Î»

  • Extracting Named Entity Relations from Large Text Corpora
    • Ê¿Ìî Å°
    • NAIST-IS-DD1061203 [ PDF ]
  • Design and Development of Optimized Hygienic Input Systems for Touch Screen Gadgets
    • Asad Habib
    • NAIST-IS-DD0961209 [ PDF ]

2012 (Ê¿À®24) ǯ 3·î ½¤Î»

  • Statistical and graph-based approaches to small sample and high dimensional data
    • ÎëÌÚ °êÈþ
    • NAIST-IS-DD0661014 [ PDF ]
  • Development of Pairwise Comparison-based Japanese Dependency Parsers and Application to Corpus Annotation
    • ´äΩ ¾­ÏÂ
    • NAIST-IS-DD0961004 [ PDF ]
  • Organizing Information Based on Semantic Relation Recognition
    • ¿åÌî ½ßÂÀ
    • NAIST-IS-DD0961023 [ PDF ]

2011 (Ê¿À®23) ǯ 9·î ½¤Î»

  • Probabilistic Logic Approach to Event Structure Analysis (³ÎΨŪÏÀÍý¤Ë¤è¤ë»ö¾Ý¹½Â¤²òÀÏ)
    • µÈÀî ¹îÀµ
    • NAIST-IS-DD0961024 [ PDF ]

2011 (Ê¿À®23) ǯ 3·î ½¤Î»

  • Chinese Synthetic Word Analysis using Large-scale N-grams and An Extendable Lexicon Management System (Â絬ÌϤÊN-grams¥Ç¡¼¥¿¤òÍѤ¤¤¿Ãæ¹ñ¸ì¹çÀ®¸ì²òÀϵڤӳÈÄ¥¤Ç¤­¤ë¸ì×ôÉÍý¥·¥¹¥Æ¥à)
    • Ϥ ²Å
    • NAIST-IS-DD0861027 [ PDF ]

2010 (Ê¿À®22) ǯ 12·î ½¤Î»

  • An Educational Technological Study on Computational Supports for Learning Linguistic Styles (¸À¸ì¥¹¥¿¥¤¥ë½¬ÆÀ¤Î·×»»µ¡»Ù±ç¤Ë´Ø¤¹¤ë¶µ°é¹©³Ø¸¦µæ)
    • ¶¶ËÜ ´îÂåÂÀ
    • NAIST-IS-DD9861017 [ PDF ]

2010 (Ê¿À®22) ǯ 9·î ½¤Î»

  • °åÎÅʸ½ñ¤Î¼«Æ°ÅÀ»úËÝÌõ¤Ë¤ª¤±¤ëÀºÅÙ¸þ¾åË¡
    • ¿ûÌî °¡µª
    • NAIST-IS-DD0161020 [ PDF ]

2010 (Ê¿À®22) ǯ 3·î ½¤Î»

  • Event Relation Acquisition from Large Text Corpora (Â絬Ìϥƥ­¥¹¥È¤«¤é¤Î»öÂÖ´Ö´Ø·¸Ãμ±¤Î³ÍÆÀ)
    • °¤Éô ½¤Ìé
    • NAIST-IS-DD0561002 [ PDF ]
  • Applying Deep Grammars to Machine Translation, Paraphrasing, and Ontology Construction (µ¡³£ËÝÌõ¡¢¸À¤¤´¹¤¨¡¢¥ª¥ó¥È¥í¥¸¡¼¹½Ãۤؤο¼¤¤Ê¸Ë¡¤ÎŬÍÑ)
    • Eric Nichols
    • NAIST-IS-DD0561041 [ PDF ]
  • ¸úΨŪ¤Ê¶á˵¸¡º÷¤Î¤¿¤á¤Î¥Ô¥Ü¥Ã¥È³Ø½¬Ë¡
    • ÌÚ¼ ³Ø
    • NAIST-IS-DD0761013 [ PDF ]
  • Graph-Theoretic Approaches to Minimally-Supervised Natural Language Learning (¥°¥é¥ÕÍýÏÀŪ´ÑÅÀ¤«¤é¤Î¼«Á³¸À¸ì½èÍý¤Ë¤ª¤±¤ë¼å¶µ»Õ¤¢¤ê³Ø½¬)
    • ¾®Ä® ¼é
    • NAIST-IS-DD0761015 [ PDF ]
  • Dependency-based Predicate-Argument Structure Analysis Using Structured Learning and Named Entity Information (°Í¸¹½Â¤¤È¸Çͭɽ¸½¾ðÊó¤òÍѤ¤¤¿¹½Â¤³Ø½¬¤Ë¤è¤ë½Ò¸ì¹à¹½Â¤²òÀÏ)
    • ÅÏîµ ÍÛÂÀϺ
    • NAIST-IS-DD0761031 [ PDF ]

2009 (Ê¿À®21) ǯ 9·î ½¤Î»

  • An Analysis of Non-Task-Oriented Dialogs and a Computational Model of Generating Affective Utterances (»¨ÃÌÂÐÏäÎʬÀϤȴ¶¾ð±þÅúÀ¸À®)
    • ÆÁµ× ÎÉ»Ò
    • NAIST-IS-DD0761020 [ PDF ]

2009 (Ê¿À®21) ǯ 3·î ½¤Î»

  • Constructing, Refining and Exploiting Rich Linguistic Rescources (¸À¸ì»ñ¸»¤Î¹½ÃÛ/ÀºÏ£/ÍøÍÑÊýË¡¤Î¸¦µæ)
    • Æ£ÅÄ¡ÊÀî°æ¡ËÁáÉÄ
    • NAIST-IS-DD0361008 [ PDF ]
  • Domain Adaptation of Statistical Word Segmentation System (Åý·×Ūñ¸ìʬ³ä¤ÎʬÌîŬ±þ¼êË¡)
    • ÄÚ°æ Í´ÂÀ
    • NAIST-IS-DD0561205 [ PDF ]

2008 (Ê¿À®20) ǯ 3·î ½¤Î»

  • Biomedical Text Mining Based on Machine Learning: from Information Extraction to Coordination Identification (µ¡³£³Ø½¬¤òÍѤ¤¤¿¥Æ¥­¥¹¥È¥Þ¥¤¥Ë¥ó¥° ¡½°åΞðÊóÃê½Ð¤«¤éÊÂÎó¶ç²òÀϤޤǡ½)
    • ¸¶ °ìÉ×
    • NAIST-IS-DD0461029 [ PDF ]
  • Constrcting a Temporal Relation Identification System of Chinese based on Dependency Structure Analysis (°Í¸¹½Â¤¤Ë´ð¤Å¤¯Ãæ¹ñ¸ì»ö¾Ýɽ¸½¤Î»þ´Ö´Ø·¸Æ±Äꥷ¥¹¥Æ¥à¤Î¹½Ãۤ˴ؤ¹¤ë¸¦µæ)
    • Yuchang CHENG
    • NAIST-IS-DD0561040 [ PDF ]

2007ǯ 3·î ½¤Î»

  • Identification of Multi-Sentence Question Type and Extraction of Descriptive Answer in Open Domain Question-Answering (ʬÌî¤ò¸ÂÄꤷ¤Ê¤¤¼ÁÌä±þÅú¤Ë¤ª¤±¤ëÊ£¿ôʸ¼ÁÌä¤Î¼±Ê̤ȵ­½ÒŪ¤Ê²óÅú¤ÎÃê½Ð)
    • ÉðÃÒ Êö¼ù
    • NAIST-IS-DD0061208 [ PDF ]
  • Combining Linguistic Knowledge and Machine Learning for Anaphora Resolution (¾È±þ²òÀϤΤ¿¤á¤Î¸À¸ì³ØŪÃμ±¤Èµ¡³£³Ø½¬¼êË¡¤ÎÍ»¹ç)
    • ÈÓÅÄ Î¶
    • NAIST-IS-DD0461004 [ PDF ]
  • Link Analysis with Kernel Metrics (¥«¡¼¥Í¥ëµ÷Î¥¤òÍѤ¤¤¿¥ê¥ó¥¯²òÀÏ)
    • °ËÆ£ ·Éɧ
    • NAIST-IS-DD0461005 [ PDF ]
  • Opinion Mining from Web documents¡§ Extraction and Structurization (Webʸ½ñ¤òÂоݤȤ·¤¿°Õ¸«¾ðÊó¤ÎÃê½Ð¤È¹½Â¤²½)
    • ¾®ÎÓ ¤Î¤¾¤ß
    • NAIST-IS-DD0461010 [ PDF ]

2006ǯ 9·î ½¤Î»

  • Unknown Word Identification for Chinese Morphological Analysis (Ãæ¹ñ¸ì·ÁÂÖÁDzòÀϤΤ¿¤á¤Î̤ÃθìÃê½Ð)
    • GOH, Chooi-Ling
    • NAIST-IS-DD0361217 [ PDF ]
  • Discriminative Learning Methods for Interdependent Decision Problems in Natural Language Processing(¼«Á³¸À¸ì½èÍý¤Ë¤ª¤±¤ëÁê¸ß°Í¸ͽ¬ÌäÂê¤ËÂФ¹¤ë¼±Ê̳ؽ¬Ë¡¤Î¸¦µæ)
    • ²ìÂô½¨¿Í
    • NAIST-IS-DD0461007 [ PDF ]

2006ǯ 3·î ½¤Î»

  • Multilingual Word Segmentation and Part-of-Speech Tagging: a Machine Learning Approach Incorporating Diverse Features (¿¸À¸ì¤Îñ¸ìʬ³ä¤ÈÉʻ쥿¥°ÉÕ¤±¡§Â¿ÍͤÊÁÇÀ­¤òÍøÍѤ·¤¿µ¡³£³Ø½¬¤Ë¤è¤ë¥¢¥×¥í¡¼¥Á)
    • ÃæÀîů¼£
    • NAIST-IS-DD461022 [ PDF ]

2005ǯ 3·î ½¤Î»

  • A Constraint-based Grammar Approach to Japanese Sentence Processing: Designing a Systematic Parser for Fundamental Grammatical Constructions and Its Extensions with Semantic and Pragmatic Constraints (ÆüËܸìʸ½èÍý¤Ø¤ÎÀ©Ìó¤Ë¤â¤È¤Å¤¯Ê¸Ë¡¤Ë¤è¤ë¥¢¥×¥í¡¼¥Á:½ÅÍ×¹½Ê¸¤ËÂФ¹¤ëÂηÏŪ¤Ê¥Ñ¡¼¥µ¤ÎÀ߷פȰỌ̃Ū¡¦¸ìÍÑŪÀ©Ìó¤òÍѤ¤¤¿³ÈÄ¥)
    • Âçëϯ
    • NAIST-IS-DD9961008 [ PDF ]
  • Distributional Approaches to Natural Language Processing (¼«Á³¸À¸ì½èÍý¤Ø¤ÎʬÉÛŪ¥¢¥×¥í¡¼¥Á)
    • »ý¶¶ÂçÃÏ
    • NAIST-IS-DT0061024 [ PDF ]
  • Computation of Semantic Equivalence for Question Answering¡×¡Ê¼ÁÌä±þÅú¤Î¤¿¤á¤Î°Ọ̃ŪÅù²ÁÀ­¤Îɾ²Á¡Ë
    • ¹â¶¶¡¡Å¯Ï¯
    • NAIST-IS-DT0261015 [ PDF ]
  • Automatic Generation of Syntactically Well-formed and Semantically Appropriate Paraphrases (Åý¸ìŪ¡¦°Ọ̃Ū¤ËŬ³Ê¤Ê¸À¤¤´¹¤¨¤Î¼«Æ°À¸À®)
    • Æ£ÅÄÆÆ
    • NAIST-IS-DT0261023 [ PDF ]
  • Kernels for Application Tasks in Natural Language Processing (¹½Â¤¾ðÊó¤òÍøÍѤ·¤¿¥«¡¼¥Í¥ë¤Ë´ð¤Å¤¯¼«Á³¸À¸ì½èÍý)
    • ÎëÌÚ½á
    • NAIST-IS-DT0361207 [ PDF ]

2004ǯ 12·î ½¤Î»

  • Machine Learning in Rhetorical Parsing and Open-domain Text Summarization (½¤¼­¹½Â¤²òÀϤȥƥ­¥¹¥È¼«Æ°Í×Ì󡧵¡³£³Ø½¬¤Ë¤è¤ëÀܶá)
    • ÌîËÜ Ãé»Ê
    • NAIST-IS-DD9761210 [ PDF ]
  • Translation Knowledge Acquisition for Pattern-based Machine Translation (¥Ñ¥¿¡¼¥ó¥Ù¡¼¥¹µ¡³£ËÝÌõ¤Î¤¿¤á¤ÎËÝÌõÃμ±³ÍÆÀ)
    • Ë̼ ÈþÊæ»Ò
    • NAIST-IS-DD9961202 [ PDF ]

2004ǯ 9·î ½¤Î»

  • Acquiring Paraphrases from Corpora and Its Application to Machine Translation (¥³¡¼¥Ñ¥¹¤«¤é¤Î¥Ñ¥é¥Õ¥ì¡¼¥º¤Î³ÍÆÀ¤È¤½¤Îµ¡³£ËÝÌõ¤Ø¤ÎŬÍÑ)
    • ²¼Èª ¸÷É×
    • NAIST-IS-DT0261014 [ PDF ]

2004ǯ 6·î ½¤Î»

  • Automatic Construction of Translation Knowledge for Corpus-based Machine Translation (¥³¡¼¥Ñ¥¹¥Ù¡¼¥¹µ¡³£ËÝÌõ¤Ë¤ª¤±¤ëËÝÌõÃμ±¼«Æ°¹½ÃÛË¡¤Î¸¦µæ)
    • º£Â¼ ¸­¼£
    • NAIST-IS-DT0261006 [ PDF ]

2004ǯ 3·î ½¤Î»

  • Acquiring Causal Knowledge from Text Using Connective Markers (Àܳɸ¼±¤Ë´ð¤Å¤¯Ê¸½ñ½¸¹ç¤«¤é¤Î°ø²Ì´Ø·¸Ãμ±³ÍÆÀ)
    • ´¥ ¹§»Ê
    • NAIST-IS-DT0161005 [ PDF ]
  • Machine Learning and Data Mining Approaches to Practical Natural Language Processing (µ¡³£³Ø½¬¤È¥Ç¡¼¥¿¥Þ¥¤¥Ë¥ó¥°¤òÍѤ¤¤¿¸½¼ÂŪ¤Ê¼«Á³¸À¸ì½èÍý)
    • ¹©Æ£ Âó
    • NAIST-IS-DT0161013 [ PDF ]
  • Synthetic Assistance for Creation and Communication of Information (¸¡º÷µ»½Ñ¤È¥³¥ß¥å¥Ë¥±¡¼¥·¥ç¥óµ»½Ñ¤Ë´Ø¤¹¤ë¸¦µæ)
    • ¹âÎÓ Å¯
    • NAIST-IS-DT0161025 [ PDF

2003ǯ 12·î ½¤Î»

  • Corpus-based Japanese morphological analysis
    • Àõ¸¶ Àµ¹¬
    • NAIST-IS-DT0161001 [ PDF ]

2003ǯ 3·î ½¤Î»

  • Clustering Approaches to Text Categorization (ʸ½ñʬÎà¤Ø¤Î¥¯¥é¥¹¥¿¥ê¥ó¥°¤Ë¤è¤ë¥¢¥×¥í¡¼¥Á)
    • ¹â¼ ÂçÌé
    • NAIST-IS-DT0061014 [ PDF ]

2002ǯ 9·î ½¤Î»

  • A Study on Generic and User-Focused Automatic Summarization (°ìÈÌŪ¤ÊÍ×Ìó¤È»ëÅÀ¤ò¹Íθ¤·¤¿Í×Ìó¤Ë´Ø¤¹¤ë¸¦µæ)
    • Ê¿Èø ÅØ
    • NAIST-IS-DT0061021 [ PDF ]

2002ǯ 3·î ½¤Î»

  • Text Categorization Using Machine Learning (µ¡³£³Ø½¬¤òÍѤ¤¤¿¥Æ¥­¥¹¥ÈʬÎà)
    • Ê¿ Çî½ç
    • NAIST-IS-DT0061207 [ PDF ]
  • A Study on Operations Used in Text Summarization (¥Æ¥­¥¹¥È²òÀϤ˴ð¤Å¤¯¼«Æ°Í×Ìó¤Î´ðÁø¦µæ)
    • ÃÝÆâ Ϲ­
    • NAIST-IS-DT9961016 [ PDF ]
  • Â絬ÌϸÀ¸ì¥³¡¼¥Ñ¥¹¤Î²òÀϤȸ¡º÷¤Ë´Ø¤¹¤ë¸¦µæ
    • »³²¼Ã£Íº
    • NAIST-IS-DT9761025 [ PDF ]
  • Partial Language Analysis Using Support Vector Learning (Support Vector ³Ø½¬¤òÍѤ¤¤¿Éôʬ¸À¸ì²òÀÏ)
    • »³ÅÄ ´²¹¯
    • NAIST-IS-DT9961028 [ PDF ]
  • Extracting Translation Knowledge from Parallel Corpora (¥³¡¼¥Ñ¥¹¤«¤é¤ÎËÝÌõÃμ±Ãê½Ð)
    • »³ËÜ ·°
    • NAIST-IS-DT9961030 [ PDF ]

2001ǯ 9·î ½¤Î»

  • Cross-language Information Retrieval, Document Alignment and Visualization --- A Study with Japanese and Chinese (¿¸À¸ì´Ö¾ðÊ󸡺÷ --- ʸ½ñÂбþÉÕ¤±¤È¤½¤Î»ë³Ð²½)
    • Md. Maruf Hasan
    • NAIST-IS-DT9861207 [ PDF ]

2000ǯ 3·î ½¤Î»

  • Japanese Dependency Structure Analysis Based on a Lexicalized Statistic Model (¸ì×ÃÅý·×¥â¥Ç¥ë¤Ë´ð¤Å¤¤¤¿ÆüËܸ췸¤ê¼õ¤±²òÀÏ)
    • Æ£Èø ÀµÏÂ
    • NAIST-IS-DT9761017 [ PS ]

1998ǯ 9·î ½¤Î»

  • A Corpus-Based Study on Conversational Interaction in Japanese: Discourse Structures, Turn-Taking, and Backchannels (¥³¡¼¥Ñ¥¹¤Ë¤â¤È¤Å¤¯ÆüËܸì²ñÏÃ¥¤¥ó¥¿¥é¥¯¥·¥ç¥ó¤Î¸¦µæ: ÃÌÏù½Â¤, ÏüԸòÂØ, ¤¢¤¤¤Å¤Á)
    • ¾®°ë ²Ö³¨
    • NAIST-IS-DT9661010 [ PDF ]
    • Çî»Î (Íý³Ø)
  • A Machine Learning Approach to Natural Language Processing (¼«Á³¸À¸ì½èÍý¤Ø¤Îµ¡³£³Ø½¬¤Ë¤è¤ë¥¢¥×¥í¡¼¥Á)
    • ½ÕÌî ²íɧ
    • NAIST-IS-DT9761211 [ PDF ]

1998ǯ 3·î ½¤Î»

  • An Integrated Robust Parsing using Multiple Knowledge Sources (Ê£¿ô¤ÎÃμ±¸»¤òÅý¹çŪ¤ËÍѤ¤¤¿´è·ò¤Ê¼«Á³¸À¸ì½èÍý)
    • º£°ì ½¤
    • NAIST-IS-DT9561005
  • Semantic Structures of Japanese Verb Phrases --- Acquisition, Representation and Use (ÆüËܸìÆ°»ì¶ç¤Î°ÕÌ£¹½Â¤ --- ³ÍÆÀ¡¤É½¸½¡¤ÍøÍÑ ---)
    • ÂçÀÐ µü
    • NAIST-IS-DT9561009 [ PS ]
  • Building Morphology-Based Statistical Models for Japanese Language (Åý·×Ū·ÁÂÖÁDzòÀϤòÍѤ¤¤¿¸À¸ì¥â¥Ç¥ë)
    • ÃÝÆâ ¹¦°ì
    • NAIST-IS-DT9561208
  • Supervised Learning of Syntactic Structure (¹½Ê¸¹½Â¤¤Î¶µ»ÕÉÕ¤­³Ø½¬)
    • Wide R. Hogenhout
    • NAIST-IS-DT9561211
    • Çî»Î (Íý³Ø)

½¤»ÎÏÀʸ¡¦²ÝÂ긦µæÊó¹ð½ñ

2019ǯ 9·î ½¤Î»

  • Improving Knowledge Graph Completion Models by Unsupervised Type Constraint Inference
    • Lu Yuxun
  • Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
    • Van_Hien Tran

2019ǯ 3·î ½¤Î»

  • Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information
    • °æ¾å ¹ä
  • Semantic Machine Translation Evaluation without References
    • Michael Wentao Li
  • Neural Tensor Networks with Diagonal Slice Matrices
    • Àи¶ ·ÉÂç
  • µ¡³£Æɲò¤Ë¤ª¤±¤ë Answer Sentence Selection ¤ÎºÆ¹Í
    • º´¡¹ÌÚ ½Ó¼ù
  • ÍÑÎãÃæ¤Îñ¸ì¤ò½Å¤ßÉÕ¤±Ï¤ˤè¤êÍøÍѤ·¤¿ÆüËܸì½Ò¸ì¹à¹½Â¤²òÀÏ
    • ¼Ç¸¶ δÁ±
  • Stochastic Tokenization with Language Model for Neural Text Classification
    • Ê¿²¬ ãÌé]
  • Ãí°Õµ¡¹½¤Ë¤è¤ë²èÁüÆÃħÎÌÁªÂò¤òÍѤ¤¤¿²èÁüÉÕ¤­´Þ°Õ´Ø·¸Ç§¼±
    • ËÜ¿ ±¦µþ
  • An Empirical Study on Reduction of Parameter Redundancy in a Weight Matrix in a Biaffine Parser
    • ¾¾Ìî ÃÒµª
  • Unsupervised Multilingual Word Embedding with Limited Resources using Neural Language Models
    • ÏÂÅÄ ¿ò»Ë
  • °å³ØÀ¸Êª³ØÏÀʸ¤«¤é¤Î¾ðÊóÃê½Ð
    • ÅÏî´ ¸­Ìé
  • ¿ÍͤʥΥ¤¥º¤Ë´è·ò¤Ê´Ø·¸¥Ç¡¼¥¿¥¯¥é¥¹¥¿¥ê¥ó¥°
    • ÉðÅÄ ÍªÍ¤

2018ǯ 3·î ½¤Î»

  • ¿¼Áسؽ¬¤Ë¤è¤ëÇжç¤Î¼«Æ°À¸À®
    • ÂÀÅÄàö»Ò
    • NAIST-IS-MT1651023
  • ¥¯¥é¥¹¥¿¥ê¥ó¥°¤òÍøÍѤ·¤¿¿¼Áسؽ¬¤Ë¤è¤ëÆüËܸìÊ£¹ç¼­¤Î¸¡½Ð
    • µ×ÊÝÂçµ±
    • NAIST-IS-MT1651046
  • ¥Á¥ã¥ó¥¯¤Ë´ð¤Å¤¤¤¿Ã༡·¿Åý¹ç¥Ñ¡¼¥¸¥ó¥°
    • ¾®ÈæÅÄÎòð
    • NAIST-IS-MT1651047
  • Cross-Domain and Cross-Lingual Parsing with Adversarial Training (ŨÂгؽ¬¤òÍѤ¤¤¿Ê¬Ìî¤È¸À¸ì¤ò²£ÃǤ¹¤ë¹½Ê¸²òÀÏ)
    • º´Æ£¸µµª
    • NAIST-IS-MT1651053
  • Èó¨»þŪ¤Ê¥¿¥¹¥¯ÀßÄê¤Ë¤ª¤±¤ë¸Çͭɽ¸½Ãê½Ð¤Î²þÁ±
    • ß·»³Ç®µ¤
    • NAIST-IS-MT1651056
  • ÊÂÎó¹½Â¤¤ÎÎà»÷À­¡¦²Ä´¹À­¤Ë´ð¤Å¤¯ÊÂÎó¶çÈϰϤÎÛ£ËæÀ­²ò¾Ã
    • »ûÀ¾Íµµª
    • NAIST-IS-MT1651075
  • Data-dependent Learning of Symmetric/Asymmetric Relations for Knowledge Base Completion (Ãμ±¥Ù¡¼¥¹Êä´°¤Ë¤ª¤±¤ë´Ø·¸¤ÎÂоÎ/ÈóÂоÎÀ­¤Î³Ø½¬)
    • ¿¿ÆéÍÛ½Ó
    • NAIST-IS-MT1651100
  • Domain Adaptation for Sentence Classification: A Study on Structured Abstract Generation (ʸʬÎà¤Î¤¿¤á¤Î¥É¥á¥¤¥óŬ±þ:¹½Â¤²½¥¢¥Ö¥¹¥È¥é¥¯¥ÈÀ¸À®)
    • Liu Xinran
    • NAIST-IS-MT1651129
  • Multi-Sense Embeddings for Semantic Role Labeling (¥Þ¥ë¥Á¥»¥ó¥¹Ê¬»¶É½¸½¤òÍѤ¤¤¿½Ò¸ì¹à¹½Â¤²òÀÏ)
    • Yong Zuo
    • NAIST-IS-MT1651134

2017ǯ 9·î ½¤Î»

  • Japanese Simplification for Non-Native Speakers
    • Muhaimin Hading
    • NAIST-IS-MT1551204

2017ǯ 3·î ½¤Î»

  • Japanese Text Normalization with Encoder-Decoder Model (Encoder-Decoder¥â¥Ç¥ë¤òÍѤ¤¤¿ÆüËܸìÊø¤ìɽµ­¤ÎÀµµ¬²½)
    • ÃÓÅÄÂç»Ö
    • NAIST-IS-MT1551006
  • ²Ê³Øµ»½Ñʸ¸¥¤ËÂФ¹¤ë¶¦»²¾È²òÀϤ˴ؤ¹¤ë¸¦µæ
    • ´ä¸µÊ¸
    • NAIST-IS-MT1551016
  • Abstract meaning representation¤Îparsing¤Î¤¿¤á¤Îñ¸ì¤ÈAMR¥°¥é¥Õ¤Î¥¢¥é¥¤¥á¥ó¥È¤Ë¤Ä¤¤¤Æ¤Î¹Í»¡
    • ±ÝËÜÍÛ°ì
    • NAIST-IS-MR1551019
  • Distant Supervision¤Ë¤ª¤±¤ë´Ø·¸Ãê½Ð¤Î¤¿¤á¤Î¥Î¥¤¥ººï¸ºË¡
    • ¿ÜÆ£¹­Âç
    • NAIST-IS-MT1551055
  • Ãê½Ð·¿Ê¸½ñÍ×Ìó¤Ë¤ª¤±¤ëʬ»¶É½¸½¤Î³Ø½¬ -ʸ½ñ¤ÈÍ×Ìó¤Îµ÷Î¥ºÇ¾®²½-
    • ÅĸýͺºÈ
    • NAIST-IS-MT1551058
  • ¥Æ¥­¥¹¥ÈÊ¿°×²½¥³¡¼¥Ñ¥¹¹½ÃۤΤ¿¤á¤Îʸ¤Îʬ³ä¤ò¹Íθ¤·¤¿Ê¸´ÖÎà»÷ÅÙ·×»»Ë¡
    • ±Ê°æÍ¥¾ë
    • NAIST-IS-MT1551065
  • ¥Ç¡¼¥¿³ÈÄ¥¤Ë¤è¤ë´¶¾ðʬÀϤΥ¢¥¹¥Ú¥¯¥È¿äÄê
    • À¾ËÜ¿µÇ·²ð
    • NAIST-IS-MT1551074
  • Joint Transition-based Dependency Parsing and Disfluency Detection for Automatic Speech Recognition Texts (²»À¼Ç§¼±·ë²Ìʸ¤ËÂФ¹¤ë·¸¤ê¼õ¤±¹½Â¤¤È¸À¤¤Íä¤ß²Õ½ê¤ÎƱ»þ¿äÄê)
    • µÈÀî¾­»Ê
    • NAIST-IS-MT1551119
  • Word Segmentation and Part-of-Speech Tagging for Lao Language (¥é¡¼¥ª¸ì¤Î¤¿¤á¤Îñ¸ìʬ³ä¤ÈÉʻ쥿¥°ÉÕ¤±)
    • Alivanh Insisiengmay
    • NAIST-IS-MT1551123

2016ǯ 9·î ½¤Î»

  • Integrating Word Embedding Offsets into the Espresso System for Part-Whole Relation Extraction
    • Phi Van Thuy
    • NAIST-IS-MT1451208

2016ǯ 3·î ½¤Î»

  • ²Ê³Øµ»½ÑÏÀʸ¤«¤é¤ÎStructured Abstract¤Î¼«Æ°À¸À®¤Ë´Ø¤¹¤ë¸¦µæ
    • ËãÀ¸ ±É¼ù
    • NAIST-IS-MT1451005
  • ¥Ë¥å¡¼¥é¥ë¥Í¥Ã¥È¥ï¡¼¥¯¤òÍѤ¤¤¿¥»¥ó¥Æ¥ó¥¹Îà»÷ÅÙ¥â¥Ç¥ë¤Î¥¨¥ó¥Æ¥£¥Æ¥£Û£ËæÀ­²ò¾Ã¤Ø¤Î±þÍÑ
    • Í­»³ Í´Ê¿
    • NAIST-IS-MT1451006
  • Construction of an English Dependency Corpus Incorporating Compound Function Words
    • ²ÃÆ£ ÌÀɧ (Ʊǯ4·îÇî»Î¸å´ü²ÝÄø¿Ê³Ø)
    • NAIST-IS-MT1451033
  • °Í¸¹½Â¤¾ðÊó¤òÍøÍѤ·¤¿±Ñ¸ìʣñ¸ìɽ¸½¤Î¤¿¤á¤Î¸úΨŪÃí¼áË¡
    • ¶ð°æ ²íÇ·
    • NAIST-IS-MT1451047
  • Semantic Structure Analysis of Noun Phrases using Abstract Meaning Representation
    • ß·°æ ͵°ìϺ (Ʊǯ4·îÇî»Î¸å´ü²ÝÄø¿Ê³Ø)
    • NAIST-IS-MT1451054
  • ¥°¥é¥Õ¥¯¥é¥¹¥¿¥ê¥ó¥°¤Ë¤è¤ë¶¦»²¾È²òÀÏ
    • ¾¾ÅÄ ¾º¸ç
    • NAIST-IS-MT1451098
  • ʸ̮¤ò¹Íθ¤·¤¿Á°ÃÖ»ì¸í¤êÄûÀµ¤Î¤¿¤á¤ÎÀøºß°ÕÌ£´Ø·¸¤ÎÍøÍÑ
    • »°ÅÄ ²í¿Í
    • NAIST-IS-MT1451103
  • ¥È¥Ô¥Ã¥¯¥â¥Ç¥ë¤òÍøÍѤ·¤¿¥Ô¥Ü¥Ã¥ÈËÝÌõ¤Î¸ìµÁÛ£ËæÀ­²ò¾Ã
    • ¼¾¾ ¹ÒÊ¿
    • NAIST-IS-MT1451110
  • ÆüËܸì»Ë»ñÎÁ¤òÂоݤȤ·¤¿¼«Æ°¥¢¥é¥¤¥á¥ó¥È
    • »³ÅÄ Í´¼Â
    • NAIST-IS-MT1451117
  • Mixture of Topic Models for Analyzing Short Text Documents with User Information
    • º£°æ Í¥ºî
    • NAIST-IS-MT1451013

2015ǯ 9·î ½¤Î»

  • Extracting Bilingual Multi-word Terms from Comparable Corpora
    • Liang Jun
    • NAIST-IS-MT1351208

2015ǯ 3·î ½¤Î»

  • Transition-based Dependency Parsing Exploiting Supertags (Supertag¤òÍøÍѤ·¤¿Á«°Ü·¿°Í¸¹½Â¤²òÀÏ)
    • ÂçÆâ ·¼¼ù
    • NAIST-IS-MT1351014
  • Japanese Predicate Argument Structure Analysis based on Multiple Predicates Relation (Ê£¿ô¤Î½Ò¸ì´Ø·¸¤ò¹Íθ¤·¤¿ÆüËܸì½Ò¸ì¹à¹½Â¤²òÀÏ)
    • Âç¼ Éñ
    • NAIST-IS-MT1351020
  • ¿¸À¸ì¥È¥Ô¥Ã¥¯¥â¥Ç¥ë¤òÍѤ¤¤¿±ÑÆüËÝÌõ
    • ¶â´Ý ÃÒ»Ë
    • NAIST-IS-MT1351031
  • ·¸¤ê¼õ¤±¾ðÊó¤òÍøÍѤ·¤¿ÆüËܸì·ÁÂÖÁDzòÀÏ
    • ɶ ͺµ®
    • NAIST-IS-MT1351067
  • Ä귿Ū·¸¤ê¼õ¤±¥Ñ¥¿¡¼¥ó¤Î²¼¹ß¼°Å¬ÍѤˤĤ¤¤Æ
    • ÁýÅÄ¡¡Í¥
    • NAIST-IS-MT1351098
  • ´Þ°Õ´Ø·¸Ç§¼±¤Èñ¸ìʬÉÛ¤òÍøÍѤ·¤¿Îò»Ë¤ÎÀµ¸íÌäÂê¤Ø¤Î²òÅú
    • »°±º¡¡Ì¤Íè
    • NAIST-IS-MT1351101
  • ÈæÓÈɽ¸½¤Îǧ¼±¤ª¤è¤ÓÍý²ò¤Ë´Ø¤¹¤ë¼«Á³¸À¸ì½èÍý¼êË¡¤ÎÄ´ºº
    • ³§Àî¡¡Àµ¹À
    • NAIST-IS-MR1351103
  • Ê£¿ôʸ½ñÍ×Ìó¤Î¤¿¤á¤Îʸ¤Î½ç½øÉÕ¤±
    • »³Ö¿¡¡ËûÍ¿
    • NAIST-IS-MT1351109
  • Neural link between obsessive-compulsive disorder and delay discounting
    • üâÌÚ¡¡Í¥
    • NAIST-IS-MT1351062
  • Visualizing Words and Documents for Revealing Multisense Words
    • ¶áÆ£¡¡²í˧
    • NAIST-IS-MT1351044

2014ǯ 3·î ½¤Î»

  • ¾ò·ïÉÕ¤­¥í¥¸¥¹¥Æ¥£¥Ã¥¯²óµ¢¤òÍѤ¤¤¿½Å¤ßÉÕ¤­Â¿¥¿¥¹¥¯³Ø½¬
    • ßÀ¸ý ÂóÃË
    • NAIST-IS-MT1151083
  • Á«°Ü¥Ù¡¼¥¹¤Î°Í¸¹½Â¤²òÀϤˤè¤ë±Ñ¸ìÁ°ÃÖ»ì¤Î¼«Æ°¸í¤êÄûÀµ
    • ¸÷À¥ ÃÒºÈ
    • NAIST-IS-MT1251041
  • Factored Translation Models¤òÍѤ¤¤¿»ö¸åʤÙÂؤ¨¤Ë¤è¤ëÆü±ÑËÝÌõ
    • ¾®ÎÓ ÏÂÌé
    • NAIST-IS-MT1251045
  • ÂÐÌõÃê½Ð¤Ë¤ª¤±¤ë¥Ï¥Ö¤Î±Æ¶Á
    • ½ÅÆ£ Í¥ÂÀϺ
    • NAIST-IS-MT1251050
  • ¥¤¥ó¥¹¥¿¥ó¥¹Ãê½Ð¥Ñ¥¿¡¼¥ó¤Î³ÈÄ¥¤Ë¤è¤ë¸ì×óÍÆÀ
    • Çò°æ º¾¼
    • NAIST-IS-MT1251053
  • Modeling of Semantic Co-Compositionality and Learning of Word Representations
    • ÄØ ¿¿»Ë
    • NAIST-IS-MT1251067
  • ¥Þ¥¤¥¯¥í¥Ö¥í¥°¤Îµï½»ÃÏ¿äÄê¤Î¤¿¤á¤ÎÆüËܸìÊý¸À¤Î·ÁÂÖÁDzòÀÏ
    • À¾Â¼ ½Ù¿Í
    • NAIST-IS-MT1251081
  • Synergies between Word Representations Learning and Dependency Parsing
    • µ×ËÜ ¶õ³¤
    • NAIST-IS-MT1251088
  • °Û¸À¸ì»ñ¸»¤òÍøÍѤ·¤¿Êª¸ì¼«Æ°¥â¥Á¡¼¥ÕʬÎà
    • »°ß· ¸­Í´
    • NAIST-IS-MT1251102
  • °Í¸¹½Â¤²òÀÏ´ï¤Ø¤ÎÊÂÎó¹½Â¤²òÀϤÎÁȤ߹þ¤ß
    • µÈËÜ ¶Çʸ
    • NAIST-IS-MT1251118
  • Äê´§»ì¤ÎÁ°Êý¾È±þÍÑË¡¤ò¹Íθ¤·¤¿´§»ì¸í¤êÄûÀµ
    • µÈËÜ °ìÊ¿
    • NAIST-IS-MT1251119
  • ImprovingWord Alignment for Statistical Machine Translation by Prediction of Unalignable Words
    • Frances Yung Pikyu
    • NAIST-IS-MT1251126
  • Efficient K-Nearest Neighbor Graph Construction Using MapReduce for Large-Scale Data Sets
    • ÏÂÎÉÉÊ Í§Âç
    • NAIST-IS-MT1251121

2013ǯ 3·î ½¤Î»

  • ¸½Âå¥Ú¥ë¥·¥¢¸ì³Ø½¬¤ò»Ù±ç¤¹¤ëweb¼­½ñ¥·¥¹¥Æ¥à¤Î³«È¯¤È¤½¤Î¸ú²Ì¤Ë¤Ä¤¤¤Æ¤Î¸¦µæ
    • ËÌ΢ ζÂÀ
    • NAIST-IS-MT0951036
  • Collocation Suggestion for Japanese Second Language Learners¡ÊÆüËܸì³Ø½¬¼Ô¤Î¤¿¤á¤Î¥³¥í¥±¡¼¥·¥ç¥óÄ󼨡Ë
    • Lis Kanashiro
    • NAIST-IS-MT1151128
  • Using Large-scale unlabeled data for parsing internal structure of Chinese Synthetic words¡ÊÃæ¹ñ¸ì¤Î¹çÀ®¸ì¤ÎÆâÉô¹½Â¤²òÀϤؤÎÂ絬Ìϥǡ¼¥¿ÍøÍÑ¡Ë
    • Äø Èô
    • NAIST-IS-MT1151131
  • ´¶¾ð¼´¤Ë¤ª¤±¤ë´¶¾ð¶ËÀ­¤ÈÊ£¿ô¼´¤ò¹Íθ¤·¤¿¥Þ¥ë¥Á¥é¥Ù¥ë´¶¾ð¿äÄê
    • ¹¾ºê Âç»Ì
    • NAIST-IS-MT1151134
  • ¥«¥¹¥¿¥Þ¡¼¥ì¥Ó¥å¡¼Ê¸½ñ¤«¤é¤Î¾Êά¤µ¤ì¤¿Â°À­¤Î¿äÄê¤ò´Þ¤á¤¿°Õ¸«¾ðÊóÃê½Ð
    • ÇðÌÚ ·é
    • NAIST-IS-MT1151034
  • A Monte Carlo Method for Hilbert Space Embeddings of Distributions and Its Application to Filtering in State Space Models¡Ê¥â¥ó¥Æ¥«¥ë¥íË¡¤Ë¤è¤ë³ÎΩʬÉۤΥҥë¥Ù¥ë¥È¶õ´ÖËä¹þ¤ß¤È¤½¤Î¾õÂÖ¶õ´Ö¥â¥Ç¥ë¤Î¥Õ¥£¥ë¥¿¥ê¥ó¥°¤Ø¤Î±þÍÑ¡Ë
    • ¶âÀî ¸µ¿®
    • NAIST-IS-MT1151039
  • ¥Ö¥í¥°¾ðÊó¤È¥Ö¥í¥°¥æ¡¼¥¶´Ö¤Î¥ê¥ó¥¯¹½Â¤¤òÍѤ¤¤¿Ãø¼Ô¤ÎǯÎð¿äÄê
    • ¼ò°æ ·¼Æ»
    • NAIST-IS-MT1151051
  • Joint English Spelling Error Correction and POS tagging for Language Learning Writing¡Ê±Ñ¸ì¥¹¥Ú¥ê¥ó¥°ÄûÀµ¤ÈÉʻ쥿¥°ÉÕ¤±¤ÎƱ»þ²òÀÏ¡Ë
    • ºä¸ý ·ÄÍ´
    • NAIST-IS-MT1151052
  • Assisting Verb Selection for ESL:Considering Error Patterns Produced by Learners¡Ê±Ñ¸ì³Ø½¬¼Ô¤Î¸í¤ê·¹¸þ¤ò¹Íθ¤·¤¿Æ°»ìÁªÂò»Ù±ç¡Ë
    • ß·°æ ͪ
    • NAIST-IS-MT1151054
  • ºÒ³²»þ¤Ë¤ª¤±¤ë¥Þ¥¤¥¯¥í¥Ö¥í¥°¤ÎÅê¹Æ¤«¤é¤Îή¸ÀÄûÀµ¾ðÊó¤Î³ÍÆÀ
    • ¹â¶¶ ¹°»Ö
    • NAIST-IS-MT1151062
  • ÆüËܸì³Ø½¬¼Ô¤Îºîʸ¤Î·ÁÂÖÁDzòÀϤÎÄ´ºº¤È²þÁ±
    • Æ£Ìî ÂóÌé
    • NAIST-IS-MT1151092
  • µ¡³£³Ø½¬¤òÍѤ¤¤¿Web¥Æ¥­¥¹¥È¤Ë¤ª¤±¤ëÍ­³²É½¸½¤Î¼±ÊÌ
    • »°Ã« μ²ð
    • NAIST-IS-MT1151102
  • Information Diffusion Models for Capturing Latent Factors of Real World Phenomena on Social Networks¡Ê¥½¡¼¥·¥ã¥ë¥Í¥Ã¥È¥ï¡¼¥¯¤Ë¤ª¤±¤ë¸½¼ÂÀ¤³¦¤Î¸½¾Ý¤ÎÀøºß°ø»Ò¤òª¤¨¤ë¤¿¤á¤Î¾ðÊó³È»¶¥â¥Ç¥ë¡Ë
    • µÈÀî ͧÌé
    • NAIST-IS-MT1151114

2012ǯ 9·î ½¤Î»

  • Analysis of Patterns of Complex Sentences for Statistical Translation
    • To Thi Chinh
    • NAIST-IS-MT1051206

2012ǯ 3·î ½¤Î»

  • µ¡³£³Ø½¬¤òÍѤ¤¤¿Îò»ËŪ»ñÎÁ¤Ø¤ÎÂùÅÀ¤Î¼«Æ°ÉÕÍ¿
    • ²¬ ¾È¹¸
    • NAIST-IS-MT1051019
  • ÆüËܸì³Ø½¬»Ù±ç¤Î¤¿¤á¤Î¼«Æ°¸í¤êÄûÀµ
    • ³Þ¸¶ À¿»Ê
    • NAIST-IS-MT1051027
  • Âç°èŪʸ̮¾ðÊó¤òÍѤ¤¤¿±Ñ¸ì»þÀ©¸í¤ê¤Î¸¡½Ð¤ÈÄûÀµ
    • ÅÄ¿¬ ½Ó½¡
    • NAIST-IS-MT1051063
  • À¸À®¸ì×ÃÏÀ¤Ë´ð¤Å¤¯ÆüËܸì¤ÎÆüÁ¹½Â¤¤Î¥é¥ó¥­¥ó¥°³Ø½¬¤Ë¤è¤ë¼«Æ°³ÍÆÀ
    • ¾ïµÈ ¹â¹°
    • NAIST-IS-MT1051071
  • Twitter¤òÍøÍѤ·¤¿É¾²Á¶ËÀ­¼­½ñ¤Î¼«Æ°³ÈÄ¥
    • Ä»ÁÒ ¹­Âç
    • NAIST-IS-MT1051075
  • Automated Japanese Error Correction with Revision Logs of Language Learning SNS
    • ¿åËÜ ÃÒÌé
    • NAIST-IS-MT1051107
  • Transfer Learning for Multiple-Domain Sentiment Analysis
    • µÈÅÄ ¹¯µ×
    • NAIST-IS-MT1051124
  • Narrative Schema as World Knowledge for Coreference Resolution
    • JOSEPH HOWARD IRWIN
    • NAIST-IS-MT1051134

2011ǯ 3·î ½¤Î»

  • ÆüËܸ쵡ǽɽ¸½¤òÍøÍѤ·¤¿µ¡³£ËÝÌõ¤Î¤¿¤á¤Î½Åʸ¤Èʣʸ¤Îʸ·¿¥Ñ¥¿¡¼¥ó¤Î³ÍÆÀ
    • ±«µÜ¾°ÈÏ
    • NAIST-IS-MR0951002
  • Short-text Oriented Chinese Named Entity Recognition (¥·¥ç¡¼¥È¥á¥Ã¥»¡¼¥¸Ãæ¤ÎÃæ¹ñ¸ì¸Çͭɽ¸½Ãê½Ð)
    • Jiawei Ye
    • NAIST-IS-MT0951137
  • Web¥Õ¥£¥ë¥¿¥ê¥ó¥°½èÍý»þ¤Ë¤ª¤±¤ëɽµ­¤æ¤ìÌäÂê¤Î²ò·è¼êË¡¤Ë´Ø¤¹¤ë¸¦µæ
    • °æ¼ê¸ü
    • NAIST-IS-MT0951009
  • ʸ´Ö¤Î¼åÂÐΩ´Ø·¸¤ÎÄêµÁ¤Èǧ¼±
    • ÂçÌÚ´ÄÈþ
    • NAIST-IS-MT0951018
  • Mutual k-Nearest Neighbor Graphs for Semi-Supervised Learning
    • ¾®Ö¿ ¹ÌÊ¿
    • NAIST-IS-MT0951027
  • Unsupervised Seed Selection and Stop List Construction for Bootstrapping: a Graph-based Approach (¥Ö¡¼¥È¥¹¥È¥é¥Ã¥Ô¥ó¥°¤Ë¤ª¤±¤ë¥°¥é¥Õ¤Ë´ð¤Å¤¯¥·¡¼¥ÉÁªÂò¤ª¤è¤Ó¥¹¥È¥Ã¥×¥ê¥¹¥È¹½ÃÛ)
    • ÌÚÁ¾ Å´ÃË
    • NAIST-IS-MT0951058
  • ¥Æ¥­¥¹¥È¥Þ¥¤¥Ë¥ó¥°¤È¥ê¥ó¥¯²òÀϤòÍѤ¤¤¿´ë¶È´Ö¼è°ú¥Í¥Ã¥È¥ï¡¼¥¯¤Î´ë¶ÈʬÎà
    • ¸åƣȻ¿Í
    • NAIST-IS-MT0951054
  • ¥Õ¥ì¡¼¥ºÃ±°Ì¤ÎÀþ·Á½ç½øÌäÂê¤Ë´ð¤Å¤¯Åý·×Ūµ¡³£ËÝÌõ¤Ë¤ª¤±¤ëŵ÷Î¥¤Î¸ì½çʤÙÂؤ¨¥â¥Ç¥ë¤Î²þÁ±
    • ¶áÆ£½¤Ê¿
    • NAIST-IS-MT1051046
  • ʸ̮¾ðÊó¤È³Ê¹½Â¤¤ÎÎà»÷ÅÙ¤òÍѤ¤¤¿ÆüËܸìʸ´Ö½Ò¸ì¹à¹½Â¤²òÀÏ
    • ÎÓÉôÍ´ÂÀ
    • NAIST-IS-MT0951098
  • ¶¥¹ç»öʪ´Ö¤Ë¤ª¤±¤ëÈæ³Ó´Ø·¸Ç§¼±
    • »³ºêµÁδ
    • NAIST-IS-MT0951129

2010ǯ 3·î ½¤Î»

  • Handling Tokenization Ambiguities in English Part-of-Speech Tagging
    • Alex Shinn
    • NAIST-IS-MT0751204
  • Æ°»ì¤Î¹à¹½Â¤¼­½ñ¤òÍøÍѤ·¤¿»öÂÖÀ­Ì¾»ì¤Î¹àƱÄê
    • Í­ÌÚ È»¿Í
    • NAIST-IS-MT0851003
  • Anaphora Resolution for Japanese Definite Noun Phrases¡ÊÆüËܸì¤ÎľÀܾȱþ¤ª¤è¤Ó´ÖÀܾȱþ¤ÎÅý¹çŪ²òÀÏ¡Ë
    • °æÇ·¾å ľÌé
    • NAIST-IS-MT0851009
  • ¿¿µ¶¾ðÊ󡦲ÁÃ;ðÊó¤ò¹Íθ¤·¤¿³ÈÄ¥¥â¥À¥ê¥Æ¥£²òÀÏ
    • ¹¾¸ý ˨
    • NAIST-IS-MT0851014
  • Evaluating Lexical/Semantic Resources for Bridging the Semantic Gap in Textual Inference
    • Francisco Soares
    • NAIST-IS-MT0851138

2009ǯ 3·î ½¤Î»

  • Japanese Dependency Parsing Using a Tournament Model (¥È¡¼¥Ê¥á¥ó¥È¥â¥Ç¥ë¤òÍѤ¤¤¿ÆüËܸ췸¤ê¼õ¤±²òÀÏ)
    • ´äΩ ¾­ÏÂ
    • NAIST-IS-MT0751019
  • ·ÏÎ󥢥饤¥ó¥á¥ó¥È¤Èµ¡³£³Ø½¬¤òÍѤ¤¤¿ÆüËܸìÊÂÎó¹½Â¤²òÀÏ
    • Âç·§ ½¨¼£
    • NAIST-IS-MT0751024
  • »¨ÃÌÂÐÏäΤ¿¤á¤Îɾ²Áɽ¸½¤òÍøÍѤ¹¤ëÁêÄÈ
    • À¶¿å ͧ͵
    • NAIST-IS-MT0751059
  • ¥¦¥§¥Ö¥Ë¥å¡¼¥¹¤òÍøÍѤ·¤¿»¨ÃÌÂÐÏÃ¥·¥¹¥Æ¥à
    • ¿åÌî ½ßÂÀ
    • NAIST-IS-MT0751116
  • ¥Æ¥­¥¹¥È¾ðÊó¤Î»ö¼ÂÀ­²òÀϸ¦µæ¤ÎÄ´ºº
    • ¿¹ÅÄ ·¼
    • NAIST-IS-MT07512
  • Machine Learning on Temporal Relation Identification with Joint Inference (µ¡³£³Ø½¬Ë¡¤Ë¤è¤ë·ë¹ç¿äÏÀ¤òÍøÍѤ·¤¿»þ´ÖŪ½ç½ø´Ø·¸¿äÄê)
    • µÈÀî ¹îÀµ
    • NAIST-IS-MT0751203

2008ǯ 3·î ½¤Î»

  • ÂèÆó¸À¸ì¤È¤·¤Æ¤ÎÆüËܸ콬ÆÀ»Ù±ç¥·¥¹¥Æ¥à¤Ë¤ª¤±¤ë¸ìµÁÛ£ËæÀ­²ò¾Ã¤Î¸¦µæ
    • ¾®ÎÓ Êþ¹¬
    • NAIST-IS-MT0251040
  • Â絬ÌÏÀ¤³¦Ã챤òÍѤ¤¤¿ÂÐÏÃ¥·¥¹¥Æ¥à¤Î±þÅúÀ¸À®
    • ÂÀÅÄ ¤Õ¤ß
    • NAIST-IS-MT0651022
  • »öÂÖ´Ö´Ø·¸Ã챤ÎÀ°È÷¤È´Þ°ÕʸÀ¸À®¤Ø¤Î±þÍÑ
    • ÂçÀ¾ ÎÉÌÀ
    • NAIST-IS-MT0651024
  • ¥³¡¼¥Ñ¥¹¤òÍѤ¤¤¿±Ñ¸ì½¬ÆÀÅ٤οäÄê
    • ºäÅÄ ¹Àμ
    • NAIST-IS-MT0651041
  • ¥Ç¡¼¥¿¥Þ¥¤¥Ë¥ó¥°¼êË¡¤òÍѤ¤¤¿ÀûΧ¥Ñ¥¿¡¼¥ó¤ÎÃê½Ð
    • ë¸ý ͺºî
    • NAIST-IS-MT0651071
  • ½Ò¸ì¤ÎÁªÂòÁª¹¥À­¤ËÃåÌܤ·¤¿Ì¾»ìɾ²Á¶ËÀ­¤Î³ÍÆÀ
    • Å컳 ¾»É§
    • NAIST-IS-MT0651102
  • Cocytus: Parallel Natural Language Prossing over Disparate Data
    • Noah Evans
    • NAIST-IS-MT0651147
  • Automatic Text Summarization with Probabilistic Latent Indexing
    • Bhandari Harendra
    • NAIST-IS-MT0651150
  • Chinese Synthetic Words Analysis
    • Ϥ ²Å
    • NAIST-IS-MT0651151

2007ǯ 3·î ½¤Î»

  • »öÂÖ´Ö´Ø·¸¤Î³ÍÆÀ¤Î¤¿¤á¤Î¹ñ¸ì¼­ÅµÆ°»ì¸ì¼áʸ¤Î¹½Â¤²½
    • ÀÄ»³ ºù»Ò
    • NAIST-IS-MT0551001
  • Ⱦ¶µ»Õ¤¢¤ê¥¯¥é¥¹¥¿¥ê¥ó¥°¤òÍѤ¤¤¿Æ°»ì¼­½ñ¤Ø¤ÎÍÑÎãÉÕÍ¿
    • ¾åÌî ¹§¼£
    • NAIST-IS-MT0551014
  • Argument Structure Analysis of Event Nouns Based on Noun-verb Co-occurrences and Noun Phrase Patterns
    • ¾®Ä® ¼é
    • NAIST-IS-MT0551054
  • Webʸ½ñ¤òÍøÍѤ·¤¿È¾¶µ»Õ¤¢¤êÍѸìÃê½Ð
    • ¶áÆ£ ¸÷Àµ
    • NAIST-IS-MT0551057
  • ¶áÂåÆüËܸ쾮Àâ¤ÎÃø¼ÔȽÊ̵»½Ñ¤Ë´Ø¤¹¤ë¸¡Æ¤
    • Ê¡²¬ Í´°ì
    • NAIST-IS-MR0451105
  • DOM¹½Â¤¤òÍøÍѤ·¤¿¾ò·ïÉÕ³ÎΨ¾ì¤Ë¤è¤ëWikipediaʸ½ñÃæ¤Î¸Çͭɽ¸½¤Î°ÕÌ£ÂηϤؤγä¤êÅö¤Æ
    • ÅÏî´ ÍÛÂÀϺ
    • NAIST-IS-MT0551137
  • Learning from Data of Varying Quality for Sentence Role Identification in MEDLINE
    • Natalia Aizenberg
    • NAIST-IS-MT0551138
  • Japanese-Spanish Thesaurus Construction Using English as a Pivot
    • Jessica Ramirez
    • NAIST-IS-MT0551142

2006ǯ 3·î ½¤Î»

  • ¾®µ¬ÌϤʼ­½ñ¤ÈÂçÎ̤Υƥ­¥¹¥È¥Ç¡¼¥¿¤ò»ÈÍѤ·¤¿´è·ò¤ÊÍѸìÃê½Ð
    • ÆìÅĹÀ»°
    • NAIST-IS-MT0251084
  • ¼ÁÌä±þÅú¥·¥¹¥Æ¥à¤Î¸¦µæÆ°¸þ
    • Àî¸ý¹°¾¼
    • NAIST-IS-MR0451042
  • ¥ì¥¹¥È¥é¥ó¥É¥á¥¤¥ó¤Ë¤ª¤±¤ë°Õ¸«¾ðÊóÃê½Ð
    • ×¢À¥Êö»Ë
    • NAIST-IS-MT0451101
  • Semi-Markov Conditional Random Fields¤òÍѤ¤¤¿¸Çͭɽ¸½Ãê½Ð¤Ë´Ø¤¹¤ë¸¦µæ
    • Ê¡²¬·òÂÀ
    • NAIST-IS-MT0451104
  • ¸À¸ìµ¡Ç½²òÌÀ¤Ë¸þ¤±¤¿¼è¤êÁȤߤ˴ؤ¹¤ëÄ´ºº
    • Ãݸ¶¤±¤¤¤³
    • NAIST-IS-MR0451204

2005ǯ 3·î ½¤Î»

  • À©Ìóʸˡ¤Ë´ð¤Å¤¯´è·ò¤«¤Ä½ÀÆð¤ÊÆüËܸìʸ²òÀÏ¥·¥¹¥Æ¥à¤ÎÄó°Æ
    • ÅìÍõ
    • NAIST-IS-MT0351003
  • °Í¸¹½Â¤²òÀϤˤè¤ë¥³¡¼¥Ñ¥¹¤«¤é¤ÎÉѽÐɽ¸½¤Î¼èÆÀ
    • ¶Ì¿¹¡¡ºÌÌï¹á
    • NAIST-IS-MT0351080
  • Æ°»ì¹à¹½Â¤¼­½ñ¤Ø¤ÎÂ絬ÌÏÍÑÎãÉÕÍ¿
    • Ê¿ÌîÅ°
    • NAIST-IS-MT0351102
  • ¸ì×óµÇ°¹½Â¤¤òÍѤ¤¤¿µ¡Ç½Æ°»ì·ë¹ç¤Î¸À¤¤´¹¤¨
    • ¹ßȨ¡¡·úÂÀϺ
    • NAIST-IS-MT0351110
  • Web¥Ú¡¼¥¸¤Î¥Æ¥­¥¹¥È¥»¥°¥á¥ó¥È³¬Áع½Â¤¤ÎÃê½Ð
    • ¾¾ËܵȻÊ
    • NAIST-IS-MT0351121
  • ¾ðÊ󸡺÷ÂÐÏäˤª¤±¤ë¸¡º÷·ë²Ì¤ÎÂÐÏÃŪ¹Ê¤ê¹þ¤ß¤È¥æ¡¼¥¶È¯ÏäÎÛ£ËæÀ­²ò¾Ã
    • çиýͪ°ì
    • NAIST-IS-MT0351141
  • Automatic extraction of fixed multiword expressions¡Ê±Ñ¸ì¤Ë¤ª¤±¤ë¸ÇÄêĹʣ¹ç¸ìɽ¸½¤Î¼«Æ°³ÍÆÀ¡Ë
    • Campbell Hore
    • NAIST-IS-MT0351147
  • Chinese Deterministic Dependency Analyzer: Examining Effects of Chunking, Root node finder and Global Features¡Ê·èÄêÀ­¤ÎÃæ¹ñ¸ì°Í¸¹½Â¤²òÀϴ¥Á¥ã¥ó¥«¡¼¡¢¥ë¡¼¥È¥Î¡¼¥È²òÀÏ´ïµÚ¤ÓÂç¶ÉÁÇÀ­¤ÎƳÆþ¤Ë¤Ä¤¤¤Æ¸¦µæ¡Ë
    • Å¢¡¡°é¾»
    • NAIST-IS-MT0351204
  • The Role of Dependency Trees in HPSG Parse Filtering¡ÊHPSG¹½Ê¸²òÀÏ¥Õ¥£¥ë¥¿¥ê¥ó¥°¤ÇÍѤ¤¤ë°Í¸¹½Â¤¡Ë
    • Eric Nichols
    • NAIST-IS-MT0351205

2004ǯ 3·î ½¤Î»

  • ¾È±þ²òÀϤΤ¿¤á¤Îʸ̮Ū¼ê¤¬¤«¤ê¤ò¹Íθ¤·¤¿µ¡³£³Ø½¬¥â¥Ç¥ë
    • ÈÓÅÄζ
    • NAIST-IS-MT0251010
  • MEDLINEʸ½ñ¸¡º÷¤Î¤¿¤á¤Îʸ¤ÎÌò³äʬÎà
    • »³ºêµ®¹¨
    • NAIST-IS-MT0251126
  • Application of Kernels to Citation Analysis¡Ê¥«¡¼¥Í¥ëË¡¤Ë´ð¤Å¤¯°úÍѲòÀÏ¡Ë
    • °ËÆ£·Éɧ
    • NAIST-IS-MT0251016
  • Ï¢Àܸ½¾Ý¤ò²ð¤·¤¿HPSG¤Ë´ð¤Å¤¯½õ»ì¤ÎʬÀÏ
    • ¹â¶¶·Ä
    • NAIST-IS-MT0251057
  • °Õ¸«Ãê½Ð¤òÌÜŪ¤È¤·¤¿É¾²Áɽ¸½¤Î¼ý½¸
    • ¾®ÎӤΤ¾¤ß
    • NAIST-IS-MT0251041
  • ¥Á¥ã¥Ã¥ÈÂÐÏäˤª¤±¤ëȯ¸À¤Î·Ñ³´Ø·¸¤È±þÅú´Ø·¸¤ÎƱÄê
    • ÆÁ±ÊÂÙ¹À
    • NAIST-IS-MT0251075
  • µ¡³£³Ø½¬¤òÍѤ¤¤¿¼ç´ÑŪɾ²Áʸ¤ÎʬÎà
    • ¾å½Ð¾­¹Ô
    • NAIST-IS-MT0251129

2003ǯ 9·î ½¤Î»

  • Chinese Unknown Word Identification by Combining Statistical Models (Ãæ¹ñ¸ì¤Î̤Ãθìǧ¼±¤Ë¤ª¤±¤ëÅý·×Ū¤Ê¼êË¡¤ÎÍøÍÑ)
    • GOH Chooi Ling
    • NAIST-IS-MT0151207

2003ǯ 3·î ½¤Î»

  • ¥µ¥Ý¡¼¥È¥Ù¥¯¥¿¡¼¥Þ¥·¥ó¤òÍѤ¤¤¿Ê¸½ñʬÎà¤Î¤¿¤á¤Îǽư³Ø½¬
    • º´¡¹ÌÚ´²
    • NAIST-IS-MT0151048
  • ¥Ù¥¯¥È¥ë¶õ´Ö¥â¥Ç¥ë¤òÍѤ¤¤¿»²¹Íʸ¸¥¤ÎƱÄê
    • ËÙÉô»ËϺ
    • NAIST-IS-MT0151094
  • £Î£Á£É£Ó£ÔÆüËܸì¶ç¹½Â¤Ê¸Ë¡¤Î³ÈÄ¥¤È¥È¥ì¡¼¥µ¤Î¼ÂÁõ
    • ¿¹ËÜ˧¹°
    • NAIST-IS-MT0151112
  • ʸ½ñ½¸¹ç¤ËÂФ¹¤ë¾ðÊóõº÷¤Î¤¿¤á¤ÎÂÐÏÃÍúÎò´ÉÍý
    • »³²¼°¡´õ»Ò
    • NAIST-IS-MT0151115
  • ·ÁÂÖÁDzòÀÏ´ï¤Î½ÐÎϤ¹¤ëÆɤߤÎÛ£ËæÀ­²ò¾Ã
    • ÊÆÅÄδ°ì
    • NAIST-IS-MT0151124
  • Data Classification and Question Generation in OSARU "A web based Japanese language acquisition support system"
    • NEUPANE Bhooshan Raj
    • NAIST-IS-MT0151129

2002ǯ 9·î ½¤Î»

  • Ãæ¹ñ¸ì¤ÈÆüËܸìÂÐÌõ¥³¡¼¥Ñ¥¹¤Î¤¿¤á¤Î¼«Æ°Ê¸¥¢¥é¥¤¥ó¥á¥ó¥È¤Î¸¦µæ
    • ÌÒ Ê¸Îï
    • NAIST-IS-MT0051205

2002ǯ 3·î ½¤Î»

  • ʸ´Ö¤ÎÀܳ´Ø·¸¤òÍѤ¤¤¿¥Æ¥­¥¹¥Èʬ³ä
    • ÂçÅç Ë㵪»Ò
    • NAIST-IS-MT0051014
  • Authorship Identification for Heterogeneous Documents (°Û¤Ê¤ë¥¿¥¤¥×¤Î¥É¥­¥å¥á¥ó¥È¤ËÂФ¹¤ëÃø¼Ô¿äÄê)
    • ÄÚ°æ Í´ÂÀ
    • NAIST-IS-MT0051063
  • Morphological Analysis and Corpus Error Detection Using Support Vector Machines (Support Vector Machine ¤òÍѤ¤¤¿·ÁÂÖÁDzòÀϤȥ³¡¼¥Ñ¥¹¤Î¸í¤ê¸¡½Ð)
    • ÃæÀî ů¼£
    • NAIST-IS-MT0051070
  • À¸À®¸ì×ÃÏÀ¤òÍѤ¤¤¿¥ò³Ê¤È¥µÊÑ̾»ì¤Îñ°ì²½¤Ë¤Ä¤¤¤Æ
    • Ê¡ÅÄ ¾¡¿Î
    • NAIST-IS-MT0051090
  • OSARU, a Web-Based Japanese Language Acquisition Support System (¤ª¤µ¤ë¥¦¥§¥Ö¥Ù¡¼¥¹ÆüËܸì³Ø½¬»Ù±ç¥·¥¹¥Æ¥à)
    • ¿¹Àî Áï
    • NAIST-IS-MT0051204
  • ¼ÁÌä±þÅú¥·¥¹¥Æ¥à¤Ë¤ª¤±¤ë¿ôÎ̤˴ؤ¹¤ë¼ÁÌä²ò·èÊýË¡
    • Ongart Supackchookul
    • NAIST-IS-MT0051124

2001ǯ 3·î ½¤Î»

  • Extended Statistical Model for Morphological Analysis (ÆüËܸì·ÁÂÖÁDzòÀϤ«¤é¿¹ñ¸ì·ÁÂÖÁDzòÀϤØ)
    • Àõ¸¶ Àµ¹¬
    • NAIST-IS-MT9851001
  • ²»À¼ÂÐÏäˤª¤±¤ë°Õ¿Þ¤äÏÃÂê°Ü¹Ô¤Îɽ½Ð¤Ë´Ø¤ï¤ë¸À¸ìÆÃħ¤ÎʬÀÏ
    • ¾®Ìº ÆØ»Ò
    • NAIST-IS-MT9951025
  • Japanese Dependency Structure Analysis Based on Support Vector Machines (Support Vector Machine ¤òÍѤ¤¤¿ÆüËܸ췸¤ê¼õ¤±²òÀÏ)
    • ¹©Æ£ Âó
    • NAIST-IS-MT9951036
  • Writing Assistance through Search Techniques (¸¡º÷µ»½Ñ¤òÍѤ¤¤¿ºîʸ»Ù±ç)
    • ¹âÎÓ Å¯
    • NAIST-IS-MT9951064
  • ¼ÁÌä±þÅú¥·¥¹¥Æ¥à¤Ë¤ª¤±¤ëÃÊÍîÃê½Ð¤ÎÊýË¡¤Ë´Ø¤¹¤ë¸¦µæ
    • ¾¾±Ê ¹á
    • NAIST-IS-MT9951101
  • ¼«Á³¤ÊÍ×Ìó¤Ë¸þ¤±¤¿Ê¸Àá¤ÎÁªÂò
    • »³ÅÄ ¸ç»Ë
    • NAIST-IS-MT9951124
  • µ¡³£³Ø½¬¤òÍѤ¤¤¿ÆüËܸ쥼¥íÂå̾»ì¾È±þ´Ø·¸¤ÎƱÄê
    • µÈÌî ·½°ì
    • NAIST-IS-MT9951128

2000ǯ 3·î ½¤Î»

  • ´ØÏ¢À­ÍýÏÀ¤òÍѤ¤¤¿ÆüËܸ쥼¥íÂå̾»ì¤Î¾È±þ´Ø·¸¤ÎƱÄê
    • ¶Í»³ ϵ×
    • NAIST-IS-MT9851030
  • °å³ØÀ¸Êª³Øʸ¸¥¤«¤é¤ÎÀìÌçÍѸì¤ÎÃê½Ð¤ÈʬÎà
    • ¹ç¸¶ Çî
    • NAIST-IS-MT9851036
  • À¸À®¸ì×ä˴ð¤Å¤¯Ã±°ì²½¥¨¥ó¥¸¥ó¤òÍѤ¤¤¿ÆüËܸìÆ°»ì¶ç¤Î°ÕÌ£²òÀÏ
    • ¹â²¬ °ìÇÏ
    • NAIST-IS-MT9851057
  • Éʻ쥿¥°ÉÕ¤­¥³¡¼¥Ñ¥¹ºîÀ®»Ù±ç´Ä¶­¤Î¹½ÃÛ
    • ¾¾ÅÄ ´²
    • NAIST-IS-MT9851103
  • Semantic Model and Semantic Analysis with a Stochastic Association (³ÎΨŪϢÁۤ˴ð¤Å¤¯°ÕÌ£¥â¥Ç¥ë¤È°ÕÌ£²òÀÏ)
    • »ý¶¶ ÂçÃÏ
    • NAIST-IS-MT9851113

1999ǯ 3·î ½¤Î»

  • ¥Ù¥¯¥È¥ë¶õ´Ö¥â¥Ç¥ë¤òÍѤ¤¤¿ÏÀʸ¸¡º÷¤Ë¤ª¤±¤ë¥Ù¥¯¥È¥ë²½Ë¡¤ª¤è¤Ó¤½¤Îɾ²Á
    • ²¬ß· ¿Î
    • NAIST-IS-MT9751022
  • ʪ¸ìʸÃæ¤Î»þ´Öɽ¸½¤ÎʬÀϤȤ½¤ì¤òÍѤ¤¤¿Ê¸¾Ï¤Î¹½Â¤²½
    • Æ£ÅÄ ÁáÉÄ
    • NAIST-IS-MT9751092
  • ¥Æ¥­¥¹¥È½¤¼­¹½Â¤¥¿¥°ÉÕ¤±¤ÎȾ¼«Æ°²½¤Ë´Ø¤¹¤ë¸¦µæ
    • ÃÝÆâ Ϲ­
    • NAIST-IS-MT9751065
  • ʸ¾ÏÃæ¤Î̾»ì´Ö¾È±þ´Ø·¸¤ÎƱÄê
    • »³º¬ ÍÎÊ¿
    • NAIST-IS-MT9751111

1998ǯ 3·î ½¤Î»

  • ¥³¡¼¥Ñ¥¹¤«¤é¤ÎÆüËܸ콾°Àá·¸¤ê¼õ¤±Áª¹¥¾ðÊó¤ÎÃê½Ð
    • À¾²¬»³ ¼¢Ç·
    • NAIST-IS-MT9551082
  • ÆüËܸìʸ¤Î·¸¼õ¤±¹½Â¤¤òÍøÍѤ·¤¿¾ðÊ󸡺÷
    • º£Â¼ ͧÌÀ
    • NAIST-IS-MT9651011
  • Acquisition of Nominal Lexical Knowledge from Japanese Corpora
    • ²ÏÉô ¹±
    • NAIST-IS-MT9651036
  • ¸í¤ê¶îÆ°·¿¤Î³ÎΨ¥â¥Ç¥ë³Ø½¬¤Ë¤è¤ëÆüËܸì·ÁÂÖÁDzòÀÏ
    • ËÌÆâ ·¼
    • NAIST-IS-MT9651040
  • ¶É½êÁêƱÀ­¤òÍøÍѤ·¤¿ORF¤ÎʬÎॢ¥ë¥´¥ê¥º¥à³«È¯µÚ¤ÓÂçIJ¶Ý¥²¥Î¥à¤Ë¤ª¤±¤ë±þÍÑ
    • ¥·¥Ð¥¹¥ó¥¿¥é¥ó ¥¹¥Ï¥ë¥Ê¥ó
    • NAIST-IS-MT9651129
  • ¥³¡¼¥Ñ¥¹¤«¤é¤Î³Ê¥Õ¥ì¡¼¥àȾ¼«Æ°³ÍÆÀ¤Î¤¿¤á¤Î»Ù±ç´Ä¶­¤Î¹½ÃÛ
    • ÃæÄÍ ¹¬µ£
    • NAIST-IS-MT9651078
  • ±¤Î§¾ðÊó¤Ë´ð¤¤¤¿¤¢¤¤¤Å¤ÁÁÞÆþ²Õ½ê¤Î¿äÄê
    • Ìî¸ý ¹­¾´
    • NAIST-IS-MT9651203
  • ȯÏÃÀø»þ¬Äê¤òÍѤ¤¤¿Ä´²»±¿Æ°´ë²èÀ¸À®µ¡¹½¤Î¸¦µæ
    • Æ£¸¶ ¾´É§
    • NAIST-IS-MT9651094
  • Á²¿ÊŪÂбþÉÕ¤±¤Ë¤è¤ëÂÐÌõ¥Æ¥­¥¹¥È¤«¤é¤ÎËÝÌõɽ¸½¤ÎÃê½Ð
    • ÊÆÂô ·Ã»Ê
    • NAIST-IS-MT9651124
  • ¸úΨ¤Ë´ð¤Å¤¤¤¿ÂÐÏÃ¥¤¥ó¥¿¥Õ¥§¡¼¥¹¤Î¹½ÃÛ
    • ÏÉÅÄ ±É°ìϺ
    • NAIST-IS-MT9651125

1997ǯ 3·î ½¤Î»

  • ÉÊ»ì¾ðÊóÉÕ¤­DTD¤òÍѤ¤¤¿³Ø½ÑÏÀʸ¥Æ¥­¥¹¥È¤ÎSGML¥¿¥°ÉÕ¤±
    • ´äÅÄ ¿¿¶×
    • NAIST-IS-MT9551009
  • ¥³¡¼¥Ñ¥¹¤«¤é¤Î¶¦µ¯¾ðÊó¤òÍѤ¤¤¿¥·¥½¡¼¥é¥¹Æâ¤Î³µÇ°¹½Â¤¤ÎºÆ¹½ÃÛ
    • Ê¿Èø ÅØ
    • NAIST-IS-MT9551089
  • ÍѸÀ¤Î³èÍѤò¹Íθ¤·¤¿´Ú¹ñ¸ìÉÊ»ìÂηϤÎÄó°Æ¤È¤½¤ì¤òÍѤ¤¤¿´Ú¹ñ¸ì·ÁÂÖÁDzòÀÏ
    • Ê¿Ìî Á±Î´
    • NAIST-IS-MT9551092
  • Japanese Dependency Structure Analysis based on Lexicalized Statistic Model
    • Æ£Èø ÀµÏÂ
    • NAIST-IS-MT9551093
  • ÅÅ»ÒÇÞÂÎʸ¾Ï¤«¤é´¶¾ð¤òÆɤߤȤëÊýË¡¤Ë´Ø¤¹¤ë¸¦µæ
    • ´Ý»³ ¹¬»Ò
    • NAIST-IS-MT9551106
  • µ¬Â§¤È³ÎΨ¥â¥Ç¥ë¤ÎÅý¹ç¤Ë¤è¤ë·ÁÂÖÁDzòÀÏ
    • »³²¼ ãͺ
    • NAIST-IS-MT9551119

1996ǯ 9·î ½¤Î»

  • ¸½ÂåÆüËܸì¤Î¥Æ¥ó¥¹¡¦¥¢¥¹¥Ú¥¯¥ÈÂηϤȻþ´Ö½¾Â°Ê£Ê¸¤ÎÀ¸À®
    • À¾Åĸµ¼ù
    • NAIST-IS-MT9451085

1996ǯ 6·î ½¤Î»

  • Ê£¿ô¼­½ñ¤ÎÅý¹çŪÍøÍѤΤ¿¤á¤ÎÈÆÍÑÆüËܸ켭½ñ¤Î¹½ÃÛ
    • ±©ÅÄ ¤æ¤«¤ê
    • NAIST-IS-MR9551002

1996ǯ 3·î ½¤Î»

  • »ö¾Ýµ­½Ò¥Æ¥­¥¹¥È¤Ë¤ª¤±¤ë¥¤¥Ù¥ó¥È´Ö¤Î»þ´Ö´Ø·¸¤ÎÃê½Ð
    • Åì ÀµÂ¤
    • NAIST-IS-MT9451002
  • Bayes ¤Î·èÄêË¡¤òÍѤ¤¤¿Ê¸¾Ï¹½À®»Ù±ç
    • ÃÓÅÄ Ê¸¿Í
    • NAIST-IS-MT9451005
  • ÆüËܸ쥳¡¼¥Ñ¥¹¤«¤é¤ÎÆ°»ì¤Î¶¦µ¯Ã챤ÎÃê½Ð
    • ¾®È¨ Ì÷
    • NAIST-IS-MR9451025
  • Èó½ªÃ¼/½ªÃ¼µ­¹æ¤ÎÅý·×ŪÎà»÷Å٤˴ð¤Å¤¯¹½Ê¸²òÀϺѤߥ³¡¼¥Ñ¥¹¤«¤é¤Îʸˡ³Ø½¬
    • ¾®¶Ì ½¤»Ê
    • NAIST-IS-MT9451039
  • Ê£¿ô¤Î¸À¸ì»ñ¸»¤òÍѤ¤¤¿¥·¥½¡¼¥é¥¹¤Î¹½ÃÛ
    • ¿ÜÆ£ ÌÐ
    • NAIST-IS-MT9451060
  • Æ°»ì·ÁÂÖÊѲ½¤Î·Á¼°ÅªÉ½¸½¤Î¸¦µæ
    • ¶ÌÌî ·ò°ì
    • NAIST-IS-MT9451071
    • ½¤»Î (Íý³Ø)
  • ¶¨Ä´Åª¥³¥ó¥µ¥ë¥Æ¡¼¥·¥ç¥ó¤Ë¤ª¤±¤ëÂÐÏôÉÍý¤Ë´Ø¤¹¤ë¸¦µæ
    • ÃæȪ ˧¼ù
    • NAIST-IS-MT9451079
  • ¸À¸ì¾ðÊóɾ²Á»Ù±ç¤Î¤¿¤á¤Î¸À¸ì²òÀÏ¥·¥¹¥Æ¥à
    • Ã滳 ÂóÌé
    • NAIST-IS-MT9451083
  • ÂÐÏäˤª¤±¤ë¾Êάɽ¸½¤È»Ø¼¨É½¸½¤Î²òÀϤª¤è¤ÓÀ¸À®
    • ÌðÁÒ ÍÎÇ·
    • NAIST-IS-MR9451116
  • Ï²ν¸¥Æ¥­¥¹¥È¤Ë¤ª¤±¤ë̤ÃθìÉÊ»ì¿äÄꤪ¤è¤Ó·ÁÂÖÁǼ­½ñ¹½ÃÛ»Ù±ç¤Ø¤ÎÍøÍÑ
    • »³ËÜ Ì÷
    • NAIST-IS-MT9451122

1995ǯ 9·î ½¤Î»

  • ±£¤ì¥Þ¥ë¥³¥Õ¥â¥Ç¥ë¤Ë¤è¤ëÆüËܸì·ÁÂÖÁDzòÀÏ¥·¥¹¥Æ¥à¤Î¥Ñ¥é¥á¡¼¥¿¿äÄê
    • ÃÝÆâ ¹¦°ì
    • NAIST-IS-MT9451067
  • Enrichment of Models for Stochastic Grammars: Semantical Categories
    • Wide Roeland Hogenhout
    • NAIST-IS-MT9451207

1995ǯ 6·î ½¤Î»

  • ÏÀʸÁ´Ê¸¥Ç¡¼¥¿¥Ù¡¼¥¹¤Î¤¿¤á¤ÎSGML¥¿¥°ÉÕ¤±¤Î¼«Æ°²½
    • À®ÅĤ¨¤ê¤«
    • NAIST-IS-MT351084

1995ǯ 3·î ½¤Î»

  • An Ingetrated Framework for Processing Grammatically Ill-Formed Sentences
    • Osamu IMAICHI
    • NAIST-IS-MT351012
  • ÂÐÌõ¥³¡¼¥Ñ¥¹¤«¤é¤ÎËÝÌõÃμ±¤Î³ÍÆÀ¤Èµ¡³£ËÝÌõ¤Ø¤ÎŬÍÑ
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    • NAIST-IS-MT351031
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