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Course Introduction. Instructor: Smaranda Muresan Columbia University firstname.lastname@example.org. Natural Language Processing Applications.
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Information Extraction: Identifying the instances of facts names/entities , relations and events from semi-structured or unstructured text; and convert them into structured representations (e.g. databases)
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“Watson also tripped up on an “Olympic Oddities” answer, but so imperceptibly that Alex Trebekdidn’t notice at first, raising an important point of clarification. After Jennings responded incorrectly that Olympian gymnast George Eyser was “missing a hand”, Watson responded, “What is a leg?”
The journalist William Finnegan has said about his profession (New Yorker, July 2,2012): ``You fish for facts and instead pull up boatloads of speculation, some of it well informed, much of it trailing tangled agendas. You end up reporting not so much what happened as what people think or imagine or say happened.'’
[Thanks Owen Rambow for this reference]
In this class we are interested in understanding communication through the eyes of the authors/speakers.
Broad research interests: computational semantics, language in social media
User: I'm so happy I'm going back to the emergency room
User: Newspaper faces court over sleazing Facebook ? Facebook is so defenseless and innocent .
(Gonzalez, Muresanand Wacholder, 2011; Muresan et al., underreview)
Other issues/observations – We will have a whole class on Sarcasm Detection
User1: A shooting has just occurred at the Occupy Oakland encampment.
User2: Shootings happen in Oakland all the time and it had nothing to do with the Occupy movement.
User1: This shooting does have something to do with the Occupy movement because many of the witness's are the Occupiers and it happened only a few yards away from the encampment.
User3: On Twitter, Occupy Oakland has said the shooting was "related to the occupation. Please keep this man in your thoughts."
T: John Smith, who was 65, resigned yesterday.
H: 65-year-old Mr. Smith left office.
T: UberSoftCEO Bill Jobs
H: Frank N. Furter is CEO of Ubersoft
Framed as a 2-way Textual Entailment problem (contradict., non-contradictory).
Assume utterances are about the same topic/event
T: A case of indigenously acquired rabies infection has been confirmed.
H: No case of rabies was confirmed.
3. Contradiction features &classification
score = =
“The movie was great”
- How can we automatically detect sentiment? (word level and text level)
“John will arrive at 6”
- Non-committed Belief (NCB): W/S weakly believes p
“John may arrive at 6”
“John said he would arrive at 6”
How can we automatically detect/tag beliefs?
“I love shopping on Black Friday”
“A Shooting in Oakland? That NEVER happend”
(Recent years authors are encourage to submit datasets and code)
(If interested to have access to some corpora for your project ask Instructor, most likely we have it)