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Language-Independent Discriminative Parsing of Temporal Expressions

CS 671 : Natural Language Processing. Language-Independent Discriminative Parsing of Temporal Expressions. - Gabor Angeli , Jakob Uszkoreit. Introduction. Probabilistic approach for extracting temporal information using latent parsing has been proposed .

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Language-Independent Discriminative Parsing of Temporal Expressions

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  1. CS 671 : Natural Language Processing Language-Independent Discriminative Parsing of Temporal Expressions - Gabor Angeli, JakobUszkoreit

  2. Introduction • Probabilistic approach for extracting temporal information using latent parsing has been proposed. • Temporal resolution is the process of relating a complex textual phrase with potentially complex time, date, or duration to an understandable normalized temporal representation. • The proposed approach is multilingual.

  3. Parsing Time • Detection : Finding temporal phrases in a sentence. • Interpretation : Finding the grounded meaning of the phrase • Incorporate a reference time

  4. Examples Actually I am out of station in thelast two weeks of September. I have some time available at the end of next week. They expect earnings to risenext month.

  5. Hurry up, May 9 is next week, there's still a few days. 9-5 WXX ~1D [5-5-2013] Reference Time [9-5-2013] [12-5-2013 / 18-5-2013] ~1D

  6. Grammar of Time • Range - A period between two dates • Sequence - A sequence of Ranges Ex: Today is 2012-06-05 , what is last Sunday? • Duration -A period of time: day, 2 weeks,2 years • Functions - General sequence and interval operations • Number - A number, characterized by its ordinality and magnitude • Nil - A word without direct temporal meaning

  7. Training Setup • For each temporal phrase, a grammar tag is assigned . • A total of 62 phrases are defined corresponding to instances of Ranges, Sequences, and Durations. • 10 functions are defined for manipulating temporal expressions.

  8. Training Setup Given [ { (Phrase, Reference) , Time} ] Ex : { ( w1 w2 , 15-10-2013 ) , 22-10-2013 }w1 = next w2 =Tuesday

  9. Step 1: Get k-best parses for phrase ( (next Tuesday , 15-10-2013 ) , 22-10-2013 )

  10. Step 2 : Filter and re-weight correct parses ( (next Tuesday , 15-10-2013 ) , 22-10-2013 ) • Step 3 : Update expected sufficient statistics

  11. Feature Extraction • Bracketed FeaturesEx:12th month of August 2013 can be realised as bracketed feature as <Intersect, Intersect ,12th> • Lexical Featuresin the phrase for this week the Lexical Features extracted are <for,week>, <this,week> and <for this,week>

  12. Drawbacks • Pragmatic Ambiguity - this week parsed as next week or whether next weekend refers to the coming or subsequent weekend • Semantic Errors – February the 30thor Friday the 13th this year • Bad Reference Time - Assuming that the reference time of an utterance is the publication time of the article

  13. References • Language-Independent Discriminative Parsing of Temporal Expressions - Gabor Angeli, JakobUszkoreit • Parsing Time: Learning to Interpret Time Expressions -Gabor Angeli, Chris Manning, Dan Jurafsky • Hierarchical phrase-based translation. - David Chiang

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