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btinternet.com>SECOND CALL FOR PAPERS COFERENCE ON FINITENESS UNIVERSITY OF KONSTANZ (GERMANY), May 11-13, 2001 Though routinely employed in the morphological and syntactic analysis of many languages, if not all, the descriptive content and theoretical import of the category of finiteness is so unclear as to render it arbitrary and meaningless. The aim of the conference is to shed light on this category by focusing on questions such as these: - Is FINITENESS an elementary notion or is it defined in terms of more basic notions (such as marking for tense, mood, person/number/other agreement, being in construction with a non-oblique subject)? - Assuming FINITENESS is not elementary, what are the patterns of more basic functional categories that render such a derived category meaningful? (For example, are there systematic correlations between being marked for tense, mood, person/number/other agreement and being in construction with a non-oblique subject?) Are such patterns language-particular or are they universally predictable? - What kinds of units can be said to be finite or non-finifte? Words or word forms? Constructions/clauses/sentences? If both, how is the FINITENESS of words related to that of constructions? - As a word category, how does FINITENESS bear on word classes? (Is finite what verbs are, and non-finite what not-so-verby verbs are, with basic nouns and adjectives unrelated to verbs being outside the scope of this category altogether?) - As a construction category, how does FINITENESS bear on construction classes?(Is finite what sentences and perhaps clauses are, and non-finite what phrases and perhaps clauses are, if desentential? Further, how do finite and non-finite distribute over main and subordinate clauses?) - How do finite and non-finite constructions differ as domains for syntactic rules (e.g., binding, anaphora, case marking)? That is,what is the relationship between FINITENESS and syntactic opacity? - What about FINITENESS is subject to change? (For example, can finite forms or constructions become non-finite, and vice versa? If so, what are the mechanisms of change? For purposes of this conference the overall angle on such questions ought to be typological and theoretical: empirically determining crosslinguistic variation and its limitations ought to be taken as seriously as explaining what has been found, in whatever theoretical framework. Invited speakers include: Elena Kalinina, Moscow State University Maria Koptjevskaja-Tamm, University of Stockholm Jaklin Kornfilt, Syracuse University David Perlmutter, University of California, San Diego Presentation will be allotted 30 minutes with an additional 15 minutes for discussion. Abstract of one page should be submitted by March 30th, 2001. If you are submitting by regular mail, abstracts should be mailed to:=20 Irina Nikolaeva, University of Konstanz, Fachbereich Sprachwissenschaft, Fach 175, Konstanz, D-78457. If submitting electronically, please include the abstract in the body of the message(do not send attachments!) and send it to: irina.nikolaevaMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue
cs.rutgers.edu>WORKSHOP ON DATA-DRIVEN MACHINE TRANSLATION 7 July 2001 Toulouse, France http://www.cs.unca.edu/~bruce/acl01/MT.html - ---------------------------------------------------------------------- With the increased availability of online corpora, data-driven approaches have become central to the NL community. A variety of data-driven approaches have been used to help build Machine Translation systems -- example-based, statistical MT, and other machine learning approaches -- and there are all sorts of possibilities for hybrid systems. We wish to bring together proponents of as many techniques as possible to engage in a discussion of which combinations will yield maximal success in translation. We propose to center the workshop on Data Driven MT, by which we mean all approaches which develop algorithms and programs to exploit data in the development of MT, primarily the use of large bilingual corpora created by human translators, and serving as a source of training data for MT systems. The workshop will focus on the following topics: - statistical machine translation (modeling, training, search) - machine-learning in translation - example-based machine translation - acquisition of multilingual training data - evaluation of data driven methods (also with rule-based methods) - combination of various translation systems; integration of classical rule-based and data driven approaches - word/sentence alignment An especially important question that we wish to address is which techniques are best for each of the subparts of a complete MT system - e.g. learning grammars, building lexicons, parsing input data, determining transfer principles, generating target text, etc. WORKSHOP CHAIRS Jessie Pinkham, Microsoft Research jessiepMail to author|Respond to list|Read more issues|LINGUIST home page|Top of issue