人工智能 ACL 2019 Annual Meeting of the Association for Computational Linguistics 全文截稿: 2019-03-04 开会时间: 2019-07-28 会议难度: ★★★★★ CCF分类: A类 会议地点: Florence, Italy 网址： http://www.acl2019.org The 57th Annual Meeting of the Association for Computational Linguistics (ACL) will take place in Florence (Italy) at the 'Fortezza da Basso' from July 28th to August 2nd, 2019. The program of the conference will include poster sessions, tutorials, workshops, and demonstrations in addition to the main conference . ACL is the premier conference of the field of computational linguistics, covering a broad spectrum of diverse research areas that are concerned with computational approaches to natural language.
Finding a Good Division of Labor: Linguistics and Machine Learning in NLP Dan Flickinger Senior Research Associate Stanford University, USA 15:00-16:30, September 26, 2018 Room 1-312, FIT Building Tsinghua University Linguists developing formal models of language seek to provide detailed accounts of linguistic phenomena, making predictions that can be tested systematically. Part of the challenge in this endeavor comes in making the expressivity of the formal apparatus match the requirements of existing linguistic analyses, and part comes in exploiting the formalism to guide in extending the theory. Computational linguists building broad-coverage grammar implementations must balance several competing demands if the resulting systems are to be both effective and linguistically satisfying. There is an emerging consensus within computational linguistics that hybrid approaches combining rich symbolic resources and powerful machine learning techniques will be necessary to produce NLP applications with a satisfactory balance of robustness and precision. In this talk I will present one approach to this division of labor which we have been exploring at CSLI as part of an international consortium of researchers working on deep linguistic processing (www.delph-in.net). I will argue for the respective roles of a large-scale effort at manual construction of a grammar of English, and the systematic construction of statistical models building on annotated corpora parsed with such a grammar. Illustrations of this approach will come from three applications of NLP: machine translation, English grammar instruction, and teaching of introductory logic. Dr. Dan Flickinger (danf@stanford.edu) is a Senior Research Associate at the Center for the Study of Language and Information (CSLI) at Stanford University. He began working in computational linguistics in 1983 in the NLP group at Hewlett-Packard Laboratories, and received his doctorate from the Linguistics Department at Stanford University in 1987. He continued in project management at HP Labs until 1993, when he moved to CSLI to manage what is now the Linguistic Grammars Online (LinGO) laboratory. From 1994 through 2002 he also served as consultant and then as Chief Technology Officer at YY Technologies, once an NLP software company based in Mountain View, California. He is the principal developer of the English Resource Grammar (ERG), a precise broad-coverage implementation of Head-driven Phrase Structure Grammar (HPSG). Current LinGO research is focused on collaborating with McGraw-Hill Education in developing more advanced methods and technology for digital learning in writing, and with Up366 in Beijing to improve writing skills for learners of English as a second language. Flickinger's primary research interests are in wide-coverage grammar engineering for both parsing and generation, lexical representation, the syntax-semantics interface, methodology for evaluation of semantically precise grammars, and practical applications of deep processing. Web page: http://lingo.stanford.edu/danf
标签: 计算语言学
