other,24-1-N03-4010,bq answering capability </term> on <term> free text </term> . The demonstration will focus on
tech,15-2-N06-4001,bq researchers who are not experts in <term> text mining </term> . As evidence of its usefulness
other,24-4-I05-2014,bq systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical
other,13-1-P84-1078,bq system </term> designed to create <term> cohesive text </term> through the use of <term> lexical substitutions
tech,36-1-H01-1040,bq text collections </term> via a standard <term> text browser </term> . We describe how this information
lr,1-3-P03-1050,bq training resources </term> . No <term> parallel text </term> is needed after the <term> training
other,10-5-P82-1035,bq to aid the understanding of <term> scruffy texts </term> has been incorporated into a working
other,31-1-H01-1040,bq - can be used to enhance access to <term> text collections </term> via a standard <term> text
other,0-1-A94-1026,bq language translation </term> . <term> Japanese texts </term> frequently suffer from the <term> homophone
other,24-1-A92-1027,bq specific information from <term> unrestricted texts </term> where many of the <term> words </term>
other,2-1-C94-1026,bq homophone errors </term> . To align <term> bilingual texts </term> becomes a crucial issue recently
other,27-1-P82-1035,bq newspaper stories </term> and other <term> edited texts </term> . However , a great deal of <term>
lr-prod,15-3-H94-1014,bq <term> word </term><term> Wall Street Journal text corpus </term> . Using the <term> BU recognition
other,12-3-C92-4207,bq </term> , which takes <term> natural language texts </term> and produces a <term> model </term> of
tech,26-3-P04-2005,bq Sense Disambiguation ( WSD ) </term> and <term> Text Summarisation </term> . Our method takes
other,6-2-P82-1035,bq , a great deal of <term> natural language texts </term> e.g. , <term> memos </term> , rough <term>
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