Teruko Mitamura mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Task and Event. Her Artificial intelligence research includes elements of Machine learning and Linguistics, Vocabulary. Her work on Machine translation, Example-based machine translation and Transfer-based machine translation as part of general Natural language processing research is frequently linked to Binary classification, bridging the gap between disciplines.
Her studies deal with areas such as Planner and TRECVID as well as Information retrieval. Her Task research includes themes of Paraphrase, Logical consequence, Inference and Contradiction. Her studies deal with areas such as Multimedia search, Image and Index as well as Event.
Teruko Mitamura mostly deals with Artificial intelligence, Natural language processing, Information retrieval, Task and Question answering. As part of the same scientific family, she usually focuses on Artificial intelligence, concentrating on Domain and intersecting with Domain knowledge. Her Natural language processing study combines topics in areas such as Annotation and Coreference.
Her Information retrieval research integrates issues from Information access, Event and TRECVID. Her work on Factoid as part of general Question answering study is frequently linked to Javelin, therefore connecting diverse disciplines of science. Her Machine translation study incorporates themes from Syntax, Universal Networking Language, Multilingualism and Lexicon.
Teruko Mitamura focuses on Artificial intelligence, Natural language processing, Task, Question answering and Inference. Her work deals with themes such as Argument, Machine learning and Interoperability, which intersect with Artificial intelligence. Her study focuses on the intersection of Natural language processing and fields such as Textual entailment with connections in the field of Domain knowledge and Question generation.
Her Task study combines topics from a wide range of disciplines, such as Segmentation, Word and Set. Her Question answering study deals with the bigger picture of Information retrieval. Her Inference research is multidisciplinary, incorporating elements of Domain, Logical consequence and Feature.
Teruko Mitamura mostly deals with Artificial intelligence, Natural language processing, Task, Question answering and Inference. Her research integrates issues of Speech recognition and Graph in her study of Artificial intelligence. Teruko Mitamura combines subjects such as Event and Graph based with her study of Natural language processing.
Her Task research is multidisciplinary, incorporating perspectives in Word and Set. Her work in Question answering tackles topics such as Ranking which are related to areas like Factoid and Mean reciprocal rank. Teruko Mitamura has included themes like Domain, Logical consequence, Margin and Multi-task learning in her Inference study.
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What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
Mengqiu Wang;Noah A. Smith;Teruko Mitamura.
empirical methods in natural language processing (2007)
Integrated authoring and translation system
Peggy M Anderson;Kathryn L Baker;Michael M Bauer;Nicholas D Brownlow.
Easy Samples First: Self-paced Reranking for Zero-Example Multimedia Search
Lu Jiang;Deyu Meng;Teruko Mitamura;Alexander G. Hauptmann.
acm multimedia (2014)
The KANT system: fast, accurate, high-quality translation in practical domains
Eric H. Nyberg;Teruko Mitamura.
international conference on computational linguistics (1992)
An Efficient Interlingua Translation System for Multi-lingual Document Production
Teruko Mitamura;Eric H. Nyberg;Jaime G. Carbonell.
Proceedings of Machine Translation Summit III: Papers (1991)
Controlled English for Knowledge-Based MT: Experience with the KANT System
Teruko Mitamura;Eric H. Nyberg.
Controlled Language for Multilingual Machine Translation
Proceedings of Machine Translation Summit VII (1999)
The JAVELIN Question-Answering System at TREC 2002
Eric Nyberg;Teruko Mitamura;Jaime G. Carbonell;James P. Callan.
text retrieval conference (2002)
Controlled Language for Multilingual Document Production: Experience with Caterpillar Technical English 1
Christine Kamprath;Eric Adolphson;Teruko Mitamura;Eric Nyberg.
Overview of NTCIR-9 RITE : Recognizing Inference in TExt
Hideki Shima;Hiroshi Kanayama;Cheng-Wei Lee;Chuan-Jie Lin.
Proceedings of the 9th NTCIR Workshop, 2011 (2011)
Profile was last updated on December 6th, 2021.
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