Utilizing temporal information in topic detection and tracking
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The document discusses utilizing temporal information in topic detection and tracking. It describes recognizing and formalizing temporal expressions to measure the temporal similarity between documents. The authors present their approach to resolving temporal expressions, recognizing terms, formalizing expressions as intervals on a timeline, and comparing temporal references between documents. They also discuss experiments on a news corpus and directions for future work, including improving composite expression processing and introducing vagueness.
![University of Helsinki Department of Computer Scien
Formalization
We map the expressions onto a calendar
a time-line – points with precedence relation,
a set of granularities (year, month, week, . . . )
note: March, Thursday and weekend are also granularities.
a set of conversion functions between granularities.
The expressions are mapped as intervals [tstart , tend ] of the
bottom granularity which in our case is day.
Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.8/15 2003-08-1](https://image.slidesharecdn.com/utilizingtemporalinformationintopicdetectionandtracking-100531210612-phpapp02/85/Utilizing-temporal-information-in-topic-detection-and-tracking-8-320.jpg){kind=tracking}
Utilizing temporal information in topic detection and tracking
- 1. University of Helsinki Department of Computer Scien Utilizing Temporal Information in Topic Detection and Tracking Juha Makkonen and Helena Ahonen–Myka {jamakkon,hahonen}@cs.helsinki.fi University of Helsinki – Department of Computer Science Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.1/15 2003-08-1
- 2. University of Helsinki Department of Computer Scien Outline Introduction Topic Detection and Tracking Resolving temporal expressions Recognition Formalization Comparison Experiments Future Work Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.2/15 2003-08-1
- 3. University of Helsinki Department of Computer Scien Introduction Temporal expressions are often omitted. their extraction requires tools, they have to be formalized in order to be of any use, comparing formalizations is sometimes tricky. By no means a novel idea in AI to form chronologies of events, in question answering to extract a fact, in databases, diagnosing systems, dialog systems . . . We want to measure the temporal similarity of two documents. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.3/15 2003-08-1
- 4. University of Helsinki Department of Computer Scien Topic Detection and Tracking TDT system monitors news broadcasts in order to detect new, previously unreported events, and to track the development of the detected events. The focus is on news events: something untrivial taking place at a specific time and place. A topic is understood as as is an event or an activity, along with all related events and activities. The news stream that is monitored in intrinsically sensitive to time. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.4/15 2003-08-1
- 5. University of Helsinki Department of Computer Scien Resolving Temporal Expressions An expression can be explicit: “the 19th of August 2003”, implicit: “today”, “Tuesday afternoon”, or vague: “since April”, “a couple of weeks ago” . The evaluation is based on a point of reference. “The winter of 1974 was cold. The next winter will be colder.” “The winter of 1974 was cold. The next winter was colder.” Resolving the meaning of the latter winter requires the reference time or the utterance time and the tense of the relevant verb. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.5/15 2003-08-1
- 6. University of Helsinki Department of Computer Scien Recognition The relevant terms are split into categories. category terms baseterm day, week, weekday, month, monthname, quarter, season, year, decade indexical yesterday, today, tomorrow internal beginning, end, early, late, middle determiner this, last, next, previous, the temporal in, on, by, during, after, until, since, before, later postmodifier of, to numeral one, two, . . . ordinal first, second, . . . adverb ago meta throughout vague some, few, several recurrence every, per source from Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.6/15 2003-08-1
- 7. University of Helsinki Department of Computer Scien Recognition The categories are used to build automata. postmodifier determiner ordinal postmodifier determiner year determiner init monthname determiner internal temporal numeral internal ordinal “The strike started on the 15th of May 1919. It lasted until the end of June, although there was still turmoil in late January next year”. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.7/15 2003-08-1
- 8. University of Helsinki Department of Computer Scien Formalization We map the expressions onto a calendar a time-line – points with precedence relation, a set of granularities (year, month, week, . . . ) note: March, Thursday and weekend are also granularities. a set of conversion functions between granularities. The expressions are mapped as intervals [tstart , tend ] of the bottom granularity which in our case is day. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.8/15 2003-08-1
- 9. University of Helsinki Department of Computer Scien Formalization The baseterm of the expression defines interval. The non-baseterms are interpreted as shift and span functions that modify the start and end points. shift: this, next, last, 3 weeks ago, etc. span: until, before, after, from, etc. the length of the interval is modified by internals in the beginning of 1970s, late May, etc. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.9/15 2003-08-1
- 10. University of Helsinki Department of Computer Scien Comparison We want to measure the temporal similarity of two documents, i.e., how much the references overlap. When comparing the intervals of two documents compare pairwise all intervals similarity = 2 * overlap / size of the intervals take the average of the best matches for each interval. The outcome measures how well the references of one document cover those of the other. Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.10/15 2003-08-1
- 11. University of Helsinki Department of Computer Scien Experiments Data: transcribed TV and radio broadcasts and online news. 8595 documents from the TDT2 corpus. 2383 documents were labeled to one of 35 events. Temporal expression recognition with 1417 sentences type freq recognition canonization simple 326 0.98 0.93 composite 209 0.85 0.66 Verbs like to schedule , to plan or to expect gave hard time. “The meeting was scheduled for Monday.” Which one? Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.11/15 2003-08-1
- 12. University of Helsinki Department of Computer Scien Experiments The distribution of temporal relations same event relation yes no before 0.761 0.831 meets 0.001 0.000 overlaps 0.016 0.008 begins 0.010 0.006 falls within 0.168 0.122 finishes 0.010 0.008 exact 0.072 0.056 Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.12/15 2003-08-1
- 13. University of Helsinki Department of Computer Scien Experiments Temporal similarity is higher when documents are relevant. average of same event different event ratio of yes/no sum of pairwise 0.0034 0.0023 1.4783 max of pairwise 0.0059 0.0040 1.4750 Finding the best-match for each interval does not pay off. A better accuracy on formalization would help. What is the meaning of “three years ago?” How to represent informativeness? Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.13/15 2003-08-1
- 14. University of Helsinki Department of Computer Scien Future Work Improvement of the composite expression processing more work on the automata Introduction of vagueness: an expression would formalized as probability distributions on the timeline similarity could be Kullback-Leibler, for instance. Survey of the behaviour of the temporal expressions how the references distribute per medium? the first story compared to the following ones? Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.14/15 2003-08-1
- 15. University of Helsinki Department of Computer Scien The End Thank you Juha Makkonen and Helena Ahonen–Myka Utilizing Temporal Information in Topic Detection and Tracking – p.15/15 2003-08-1