61,493 research outputs found
Training and Scaling Preference Functions for Disambiguation
We present an automatic method for weighting the contributions of preference
functions used in disambiguation. Initial scaling factors are derived as the
solution to a least-squares minimization problem, and improvements are then
made by hill-climbing. The method is applied to disambiguating sentences in the
ATIS (Air Travel Information System) corpus, and the performance of the
resulting scaling factors is compared with hand-tuned factors. We then focus on
one class of preference function, those based on semantic lexical collocations.
Experimental results are presented showing that such functions vary
considerably in selecting correct analyses. In particular we define a function
that performs significantly better than ones based on mutual information and
likelihood ratios of lexical associations.Comment: To appear in Computational Linguistics (probably volume 20, December
94). LaTeX, 21 page
Character N-Grams for Detecting Deceptive Controversial Opinions
[EN] Controversial topics are present in the everyday life, and opinions about them can be either truthful or deceptive. Deceptive opinions are emitted to mislead other people in order to gain some advantage. In the most of the cases humans cannot detect whether the opinion is deceptive or truthful, however, computational approaches have been used successfully for this purpose. In this work, we evaluate a representation based on character n-grams features for detecting deceptive opinions. We consider opinions on the following: abortion, death penalty and personal feelings about the best friend; three domains studied in the state of the art. We found character n-grams effective for detecting deception in these controversial domains, even more than using psycholinguistic features. Our results indicate that this representation is able to capture relevant information about style and content useful for this task. This fact allows us to conclude that the proposed one is a competitive text representation with a good trade-off between simplicity and performance.We would like to thank CONACyT for partially supporting this work under grants 613411, CB-2015-01-257383, and FC-2016/2410. The work of the last author was partially funded by the Spanish MINECO under the research project SomEMBED (TIN2015-71147-C2-1-P).Sánchez-Junquera, JJ.; Luis Villaseñor Pineda; Montes Gomez, M.; Rosso, P. (2018). Character N-Grams for Detecting Deceptive Controversial Opinions. Lecture Notes in Computer Science. 11018:135-140. https://doi.org/10.1007/978-3-319-98932-7_13S13514011018Aritsugi, M., et al.: Combining word and character n-grams for detecting deceptive opinions, vol. 1, pp. 828–833. IEEE (2017)Buller, D.B., Burgoon, J.K.: Interpersonal deception theory. Commun. Theory 6(3), 203–242 (1996)Cagnina, L.C., Rosso, P.: Detecting deceptive opinions: intra and cross-domain classification using an efficient representation. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 25(Suppl. 2), 151–174 (2017)Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection, pp. 171–175. Association for Computational Linguistics (2012)Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detection of opinion spam with character n-grams. In: Gelbukh, A. (ed.) CICLing 2015. LNCS, vol. 9042, pp. 285–294. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18117-2_21Hernández-Castañeda, Á., Calvo, H., Gelbukh, A., Flores, J.J.G.: Cross-domain deception detection using support vector networks. Soft Comput. 21(3), 1–11 (2016)Mihalcea, R., Strapparava, C.: The lie detector: explorations in the automatic recognition of deceptive language. In: Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pp. 309–312. Association for Computational Linguistics (2009)Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 309–319. Association for Computational Linguistics (2011)Pérez-Rosas, V., Mihalcea, R.: Cross-cultural deception detection. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), vol. 2, pp. 440–445 (2014)Sapkota, U., Solorio, T., Montes-y-Gómez, M., Bethard, S.: Not all character n-grams are created equal: a study in authorship attribution. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93–102 (2015)Vrij, A.: Detecting Lies and Deceit: Pitfalls and Opportunities. Wiley, Hoboken (2008
Discourse Structure in Machine Translation Evaluation
In this article, we explore the potential of using sentence-level discourse
structure for machine translation evaluation. We first design discourse-aware
similarity measures, which use all-subtree kernels to compare discourse parse
trees in accordance with the Rhetorical Structure Theory (RST). Then, we show
that a simple linear combination with these measures can help improve various
existing machine translation evaluation metrics regarding correlation with
human judgments both at the segment- and at the system-level. This suggests
that discourse information is complementary to the information used by many of
the existing evaluation metrics, and thus it could be taken into account when
developing richer evaluation metrics, such as the WMT-14 winning combined
metric DiscoTKparty. We also provide a detailed analysis of the relevance of
various discourse elements and relations from the RST parse trees for machine
translation evaluation. In particular we show that: (i) all aspects of the RST
tree are relevant, (ii) nuclearity is more useful than relation type, and (iii)
the similarity of the translation RST tree to the reference tree is positively
correlated with translation quality.Comment: machine translation, machine translation evaluation, discourse
analysis. Computational Linguistics, 201
Knowledge Base Population using Semantic Label Propagation
A crucial aspect of a knowledge base population system that extracts new
facts from text corpora, is the generation of training data for its relation
extractors. In this paper, we present a method that maximizes the effectiveness
of newly trained relation extractors at a minimal annotation cost. Manual
labeling can be significantly reduced by Distant Supervision, which is a method
to construct training data automatically by aligning a large text corpus with
an existing knowledge base of known facts. For example, all sentences
mentioning both 'Barack Obama' and 'US' may serve as positive training
instances for the relation born_in(subject,object). However, distant
supervision typically results in a highly noisy training set: many training
sentences do not really express the intended relation. We propose to combine
distant supervision with minimal manual supervision in a technique called
feature labeling, to eliminate noise from the large and noisy initial training
set, resulting in a significant increase of precision. We further improve on
this approach by introducing the Semantic Label Propagation method, which uses
the similarity between low-dimensional representations of candidate training
instances, to extend the training set in order to increase recall while
maintaining high precision. Our proposed strategy for generating training data
is studied and evaluated on an established test collection designed for
knowledge base population tasks. The experimental results show that the
Semantic Label Propagation strategy leads to substantial performance gains when
compared to existing approaches, while requiring an almost negligible manual
annotation effort.Comment: Submitted to Knowledge Based Systems, special issue on Knowledge
Bases for Natural Language Processin
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
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