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Language-Independent Discriminative Parsing of Temporal Expressions

This paper explores a probabilistic, multilingual approach to extracting temporal information from text using latent parsing methods. It focuses on the concept of temporal resolution, which relates complex phrases to normalized temporal representations. The process involves detecting temporal phrases, interpreting their meanings, and incorporating reference times for accurate understanding. The authors detail the grammar of time expressions, including ranges, sequences, and durations, while also discussing challenges such as pragmatic ambiguity and semantic errors. The methodology is tested against various temporal phrases, demonstrating its effectiveness.

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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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