If No Media Were Allowed inside the Venue, Was Anybody Allowed? Page: 860
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If No Media Were Allowed inside the Venue, Was Anybody Allowed?
Zahra Sarabi and Eduardo Blanco
Human Intelligence and Language Technologies Lab
University of North Texas
Denton, TX, 76203
zahrasarabi@my .unt . edu, eduardo . blanco@unt . edu
This paper presents a framework to under-
stand negation in positive terms. Specif-
ically, we extract positive meaning from
negation when the negation cue syntacti-
cally modifies a noun or adjective. Our
approach is grounded on generating poten-
tial positive interpretations automatically,
and then scoring them. Experimental re-
sults show that interpretations scored high
can be reliably identified.
Negation is a complex phenomenon present in all
human languages, allowing for the uniquely hu-
man capacities of denial, contradiction, misrep-
resentation, lying, and irony (Horn and Wans-
ing, 2015). Acquiring and understanding negation
poses unique challenges. For example, children
acquire negation after learning to communicate in
positive terms (Nordmeyer and Frank, 2013), and
adults take longer to process sentences containing
negation (Clark and Chase, 1972).
In any given language, humans communicate
in positive terms most of the time, and use
negation to express something unusual or an ex-
ception (Horn, 1989). But negation is ubiqui-
tous (Morante and Sporleder, 2012): In scien-
tific papers, 13.76% of sentences contain a nega-
tion (Szarvas et al., 2008); in product reviews,
19% (Councill et al., 2010); and in Conan Doyle
stories, 22.23% (Morante and Daelemans, 2012).
From a theoretical perspective, it is accepted
that negation has scope and focus, and that hu-
mans intuitively understand positive meanings
from negation (Rooth, 1992; Huddleston and Pul-
lum, 2002). For example, from (1) John didn't
earn a steady paycheck until he was 40 years old,
humans understand that (1 a) John earned unsteady
paychecks before he was 40 years old, and that
(lb) John earned steady paychecks when he was
40 years old. This kind of positive interpretations
would benefit language understanding in general.
For example, a question answering system would
benefit from interpretation (lb) when answering
question Did John ever earn a steady paycheck?
Within computational linguistics, automated
approaches to extract positive meanings from
negation target verbal negation (Section 3), i.e.,
when the negation cue is grammatically associ-
ated with a verb, as in (1). Verbal negation ac-
counts only for a portion of all negations, e.g., out
of all syntactic dependencies indicating a negation
modifier (neg dependency) in OntoNotes (Hovy
et al., 2006), 64.4% modify verbs, 19.6% nouns,
10.3% adjectives, and 5.7% other part-of-speech
tags. Non-verbal negation also conveys positive
meanings, e.g., from (2) No media were allowed
inside the venue (No modifies noun media), hu-
mans understand that (2a) Somebody (e.g., invited
guests) were allowed inside the venue and that
(2b) Media where allowed somewhere outside the
venue (presumably in a designated press area).
Similarly, from (3) She was not alive when she
got to the Lafayette area (not modifies adjective
alive), humans understand that (3a) She was dead
when she got to the Lafayette area and that (3b)
She was alive before she got to the Lafayette area.
This paper presents new corpora and experi-
mental results to extract positive interpretations
from negation when the negation cue modifies a
noun or adjective. The main contributions are:
(1) analysis of negation in OntoNotes beyond ver-
bal negation; (2) procedure to automatically gen-
erate potential positive interpretations from non-
verbal negation, specifically, when the negation
cue modifies a noun or adjective; (3) annotations
validating and scoring potential interpretations ac-
cording to their likelihood;' and (4) experimental
results showing that the task can be automated.
'Available at http: //www. cse. unt.
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 860-869,
Valencia, Spain, April 3-7, 2017. @2017 Association for Computational Linguistics
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Sarabi, Zahra & Blanco, Eduardo. If No Media Were Allowed inside the Venue, Was Anybody Allowed?, paper, April 2017; Stroudsburg, Pennsylvania. (digital.library.unt.edu/ark:/67531/metadc993391/m1/1/: accessed December 14, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT College of Engineering.