David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
The capacity to recognise and interpret sluices—bare wh-phrases that exhibit a sentential meaning—is essential to maintaining cohesive interaction between human users and a machine interlocutor in a dialogue system. In this paper we present a machine learning approach to sluice disambiguation in dialogue. Our experiments, based on solid theoretical considerations, show that applying machine learning techniques using a compact set of features that can be automatically identified from PoS markings in a corpus can be an efficient tool to disambiguate between sluice interpretations.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
J. P. T. MorelandPickavance (2003). Bare Particulars and Individuation Reply to Mertz. Australasian Journal of Philosophy 81 (1):1 – 13.
Jo-Wang Lin (1999). Double Quantification and the Meaning of Shenme 'What' in Chinese Bare Conditionals. Linguistics and Philosophy 22 (6):573-593.
Hadas Shintel & Howard C. Nusbaum (2004). Dialogue Processing: Automatic Alignment or Controlled Understanding? Behavioral and Brain Sciences 27 (2):210-211.
Joachim Quantz & Birte Schmitz (1994). Knowledge-Based Disambiguation for Machine Translation. Minds and Machines 4 (1):39-57.
Added to index2009-01-28
Total downloads4 ( #267,800 of 1,101,953 )
Recent downloads (6 months)2 ( #192,006 of 1,101,953 )
How can I increase my downloads?