I recently heard 2 teenagers talking and it was just amazing how extensively they used the word "like". There was hardly a sentence without it in about 6 minute conversation. There was even a sentence with 4 instances. It made me think about NL parsers. The word can be used as noun, verb, adverb, adjective, preposition, particle, conjunction, hedge, interjection, quotative...
Regards, Jiri Jelinek On Nov 6, 2007 10:19 AM, <[EMAIL PROTECTED]> wrote: > > Where I think parser fall down is in recognizing common English typing and > spelling errors. > > "Hello how are you" would be recognizable by a parser but the following > constructs all recognizable by a human > would only be recognizable to a fuzzy pattern matcher. > > "Helohowareyou" > "Hellllllo hwo r u" > "Hell, howwww areyu" > > Examining the Transcripts in pas years Turing competitions it is very easy > to see that all of the entries are very > intolerant to fuzzy data and would respond with a obvious bluff when > presented with such inputs. > > > -------------- Original message -------------- > From: "Jean-paul Van Belle" <[EMAIL PROTECTED]> > > > > Research Associate: CITANDA > Post-Graduate Section Head > Department of Information Systems > Phone: (+27)-(0)21-6504256 > Fax: (+27)-(0)21-6502280 > Office: Leslie Commerce 4.21 > > >>> Linas Vepstas [EMAIL PROTECTED]> 2007/11/05 23:41 >> > >Do you have any recommendations for other parsers? > One of the reasons I like Python: It's got NLTK and > MontyLingua: > > - MontyTokenizer > - normalizes punctuation, spacing and contractions, with sensitivity to > abbrevs. > - MontyTagger > - Part-of-speech tagging using PENN TREEBANK tagset > - enriched with "Common Sense" from the Open Mind Common Sense project > - MontyREChunker > - chunks tagged text into verb, noun, and adjective chunks (VX,NX, and > AX respectively) > - MontyExtractor > - extracts verb-argument structures, phrases,  ;and&n bsp;other > semantically valuable information from sentences and returns sentences as > "digests" > > - MontyLemmatiser > - part-of-speech sensitive lemmatisation > - strips plurals (geese-->goose) and tense (were-->be, had-->have) > - includes regexps from Humphreys and Carroll's morph.lex, and UPENN's > XTAG corpus > - MontyNLGenerator > - generates summaries > - generates surface form sentences > - determines and numbers NPs and tenses verbs > - accounts for sentence_type > > Note: It also has chatterbot code. > ____________________________________________________________________________________ > This e-mail is subject to the UCT ICT policies and e-mail disclaimer > published on our website at > http://www.uct.ac.za/about/policies/emaildisclaimer/ or obtainable from +27 > 21 650 4500. This e-mail is intended only for the person(s) to whom it is > addressed. If the e-mail has reached you in error, please notify the author. > If you are not the intended recipient of the e-mail you may not use, > disclose, copy, redirect or print the content. If this e-mail is not related > to the business of UCT it is sent by the sender in the sender's individual > capacity. > ____________________________________________________________________________________ > ________________________________ > > This list is sponsored by AGIRI: http://www.agiri.org/email > To unsubscribe or change your options, please go to: > http://v2.listbox.com/member/?&; > ________________________________ > This list is sponsored by AGIRI: http://www.agiri.org/email > To unsubscribe or change your options, please go to: > http://v2.listbox.com/member/?&; ----- This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?member_id=8660244&id_secret=61675415-1b7843
