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. Moreland & Timothy Pickavance (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 ( #298,430 of 1,692,473 )
Recent downloads (6 months)2 ( #111,548 of 1,692,473 )
How can I increase my downloads?