On contractible edges in convex decompositions

Let $\Pi$ be a convex decomposition of a set $P$ of $n\geq 3$ points in general position in the plane. If $\Pi$ consists of more than one polygon, then either $\Pi$ contains a deletable edge or $\Pi$ contains a contractible edge

Witness (Delaunay) Graphs

Proximity graphs are used in several areas in which a neighborliness relationship for input data sets is a useful tool in their analysis, and have also received substantial attention from the graph drawing community, as they are a natural way of implicitly representing graphs. However, as a tool for graph representation, proximity graphs have some limitations that may be overcome with suitable generalizations. We introduce a generalization, witness graphs, that encompasses both the goal of more power and flexibility for graph drawing issues and a wider spectrum for neighborhood analysis. We study in detail two concrete examples, both related to Delaunay graphs, and consider as well some problems on stabbing geometric objects and point set discrimination, that can be naturally described in terms of witness graphs.Comment: 27 pages. JCCGG 200

Witness Gabriel Graphs

We consider a generalization of the Gabriel graph, the witness Gabriel graph. Given a set of vertices P and a set of witnesses W in the plane, there is an edge ab between two points of P in the witness Gabriel graph GG-(P,W) if and only if the closed disk with diameter ab does not contain any witness point (besides possibly a and/or b). We study several properties of the witness Gabriel graph, both as a proximity graph and as a new tool in graph drawing.Comment: 23 pages. EuroCG 200

Proximity Drawings of High-Degree Trees

A drawing of a given (abstract) tree that is a minimum spanning tree of the vertex set is considered aesthetically pleasing. However, such a drawing can only exist if the tree has maximum degree at most 6. What can be said for trees of higher degree? We approach this question by supposing that a partition or covering of the tree by subtrees of bounded degree is given. Then we show that if the partition or covering satisfies some natural properties, then there is a drawing of the entire tree such that each of the given subtrees is drawn as a minimum spanning tree of its vertex set

On k-Convex Polygons

We introduce a notion of $k$-convexity and explore polygons in the plane that have this property. Polygons which are \mbox{$k$-convex} can be triangulated with fast yet simple algorithms. However, recognizing them in general is a 3SUM-hard problem. We give a characterization of \mbox{$2$-convex} polygons, a particularly interesting class, and show how to recognize them in \mbox{$O(n \log n)$} time. A description of their shape is given as well, which leads to Erd\H{o}s-Szekeres type results regarding subconfigurations of their vertex sets. Finally, we introduce the concept of generalized geometric permutations, and show that their number can be exponential in the number of \mbox{$2$-convex} objects considered.Comment: 23 pages, 19 figure

Spanish sentiment analysis in Twitter at the TASS workshop

[EN] This paper describes a support vector machine-based approach to different tasks related to sentiment analysis in Twitter for Spanish. We focus on parameter optimization of the models and the combination of several models by means of voting techniques. We evaluate the proposed approach in all the tasks that were defined in the five editions of the TASS workshop, between 2012 and 2016. TASS has become a framework for sentiment analysis tasks that are focused on the Spanish language. We describe our participation in this competition and the results achieved, and then we provide an analysis of and comparison with the best approaches of the teams who participated in all the tasks defined in the TASS workshops. To our knowledge, our results exceed those published to date in the sentiment analysis tasks of the TASS workshops.This work has been partially funded by the Spanish MINECO and FEDER founds under project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics, TIN2014-54288-C4-3-R.Pla Santamaría, F.; Hurtado Oliver, LF. (2018). Spanish sentiment analysis in Twitter at the TASS workshop. Language Resources and Evaluation. 52(2):645-672. https://doi.org/10.1007/s10579-017-9394-7S645672522Álvarez-López, T., Juncal-Martínez, J., Fernández-Gavilanes, M., Costa-Montenegro, E., González-Castaño, F.J., Cerezo-Costas, H. , & Celix-Salgado, D. (2015). GTI-gradiant at TASS 2015: A hybrid approach for sentiment analysis in Twitter. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 35–40), Alicante, Spain, September 15, 2015.Álvarez-López, T., Fernández-Gavilanes, M., García-Méndez, S., Juncal-Martínez, J., & González-Castaño, F.J. (2016). GTI at TASS 2016: Supervised approach for aspect based sentiment analysis in Twitter. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 53–57), Salamanca, Spain, September 13th, 2016.Araque, O., Corcuera, I., Román, C., Iglesias, C. A., & Sánchez-Rada, J. F. (2015). Aspect based sentiment analysis of Spanish tweets. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 29–34), Alicante, Spain, September 15, 2015.Balahur, A., & Perea-Ortega, J. M. (2013). Experiments using varying sizes and machine translated data for sentiment analysis in Twitter. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Barbosa, L., & Feng, J. (2010). Robust sentiment detection on Twitter from biased and noisy data. In Proceedings of the 23rd international conference on computational linguistics: posters, association for computational linguistics (pp. 36–44).Batista, F., & Ribeiro, R. (2012). The L2F Strategy for Sentiment Analysis and Topic Classification. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Casasola Murillo, E., & Marín Raventós, G. (2016). Evaluación de Modelos de Representación del Texto con Vectores de Dimensiónn Reducida para Análisis de Sentimiento. In Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 23–28), Salamanca, Spain, September 13th, 2016.Castellano, A., Cigarrán, J. & García-Serrano, A. (2012). UNED @ TASS: Using IR techniques for topic-based sentiment analysis through divergence models. Technical report, http://www.sepln.org/workshops/tass/2012/participation.php .Castellanos-González, A., Cigarrán-Recuero, J. & García-Serrano, A. (2013). UNED LSI @ TASS 2013: Considerations about textual representation for IR based tweet classification. In: Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Cerón-Guzmán, J. A. (2016). JACERONG at TASS 2016: An ensemble classifier for sentiment analysis of Spanish tweets at global level. In: Proceedings of TASS 2016: Workshop on sentiment analysis at SEPLN co-located with 32nd SEPLN conference (SEPLN 2016) (pp. 35–39), Salamanca, Spain, September 13th, 2016.del-Hoyo-Alonso, R., Hupont, I., & Lacueva, F. (2013). Affective polarity word discovering by means of artificial general intelligence techniques. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.del-Hoyo-Alonso, R., de la Vega Rodrigalvarez-Chamorro, M., Vea-Murguía, J., & Montañes-Salas, R. M. (2015). Ensemble algorithm with syntactical tree features to improve the opinion analysis. In Proceedings of TASS 2015: workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 53–58), Alicante, Spain, September 15, 2015.Deriu, J., Gonzenbach, M., Uzdilli, F., Lucchi, A., De Luca, V., & Jaggi, M. (2016). Swisscheese at semeval-2016 task 4: Sentiment classification using an ensemble of convolutional neural networks with distant supervision. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016) (pp. 1124–1128), Association for Computational Linguistics, San Diego, California, http://www.aclweb.org/anthology/S16-1173 .Díaz-Galiano, M. C., & Montejo-Ráez, A. (2015). Participación de SINAI DW2Vec en TASS 2015. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 59–64), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Tomás, D., Gómez, J. M. & Martínez-Barco, P. (2015). Evaluating a sentiment analysis approach from a business point of view. In Proceedings of TASS 2015: Workshop on sentiment analysis at SEPLN co-located with 31st SEPLN conference (SEPLN 2015) (pp. 93–98), Alicante, Spain, September 15, 2015.Fernández, J., Gutiérrez, Y., Gómez, J.M., Martínez-Barco, P., Montoyo A., & Muñoz, R. (2013). Sentiment analysis of Spanish Tweets using a ranking algorithm and skipgrams. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.Frank, E., Hall, M. A., & Witten, I. H. (2016). The WEKA workbench. Online appendix for “Data mining: Practical machine learning tools and techniques” (4th ed.). Burlington: Morgan Kaufmann.Gamallo, P., García, M. & Fernández-Lanza, S. (2013). TASS: A Naive-Bayes strategy for sentiment analysis on Spanish tweets. In Proceedings of the TASS workshop at SEPLN 2013, IV Congreso Español de Informática.García Cumbreras, M. 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Language identification of multilingual posts from Twitter: a case study

The final publication is available at Springer via http://dx.doi.org/10.1007/s10115-016-0997-xThis paper describes a method for handling multi-class and multi-label classification problems based on the support vector machine formalism. This method has been applied to the language identification problem in Twitter. The system evaluation was performed mainly on a Twitter data set developed in the TweetLID workshop. This data set contains bilingual tweets written in the most commonly used Iberian languages (i.e., Spanish, Portuguese, Catalan, Basque, and Galician) as well as the English language. We address the following problems: (1) social media texts. We propose a suitable tokenization that processes the peculiarities of Twitter; (2) multilingual tweets. Since a tweet can belong to more than one language, we need to use a multi-class and multi-label classifier; (3) similar languages. We study the main confusions among similar languages; and (4) unbalanced classes. We propose threshold-based strategy to favor classes with less data. We have also studied the use of Wikipedia and the addition of new tweets in order to increase the training data set. Additionally, we have tested our system on Bergsma corpus, a collection of tweets in nine languages, focusing on confusable languages using the Cyrillic, Arabic, and Devanagari alphabets. To our knowledge, we obtained the best results published on the TweetLID data set and results that are in line with the best results published on Bergsma data set.This work has been partially funded by the project ASLP-MULAN: Audio, Speech and Language Processing for Multimedia Analytics (MINECO TIN2014-54288-C4-3-R).Pla Santamaría, F.; Hurtado Oliver, LF. (2016). Language identification of multilingual posts from Twitter: a case study. 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In: 3rd international conference on statistical analysis of textural dataHurtado LF, Pla F, Giménez M, Arnal ES (2014) Elirf-upv en tweetlid: Identificación del idioma en twitter, In: Proceedings of the Tweet language identification workshop co-located with 30th conference of the Spanish society for natural language processing, TweetLID@SEPLN 2014, Girona, 16 Sept 2014, pp 35–38Jauhiainen T, Lindén K, Jauhiainen H (2015) Language set identification in noisy synthetic multilingual documents. In: Gelbukh A (ed) Computational linguistics and intelligent text processing, vol 9041 of lecture notes in computer science. Springer International Publishing, pp 633–643Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: Nédellec C, Rouveirol C (eds) Proceedings of ECML-98, 10th European conference on machine learning, no. 1398. Springer, Heidelberg, pp 137–142Liu B (2012) Sentiment analysis and opinion mining. A comprehensive introduction and survey. Morgan & Claypool Publishers, San RafaelLjubešić N, Mikelić N, Boras D (2007) Language identification: How to distinguish similar languages, In: Lužar-Stifter V, Hljuz Dobrić V (eds), Proceedings of the 29th international conference on information technology interfaces. SRCE University Computing Centre, Zagreb, pp 541–546Lui M, Baldwin T (2014) Accurate language identification of twitter messages. In: Proceedings of the EACL 2014 workshop on language analysis in social media (LASM 2014), pp 17–25Lui M, Lau JH, Baldwin T (2014) Automatic detection and language identification of multilingual documents. Trans Assoc Comput Linguist 2:27–40Nguyen D, Dogruoz AS (2014) Word level language identification in online multilingual communication. In: Proceedings of the 2013 conference on empirical methods in natural language processingO’Connor B, Krieger M, Ahn D (2010) Tweetmotif: exploratory search and topic summarization for twitter. In: Cohen WW, Gosling S (eds) Proceedings of the fourth international conference on weblogs and social media, ICWSM 2010, Washington, DC. The AAAI Press, 23–26 May 2010Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830Pla F, Hurtado L-F (2014) Political tendency identification in twitter using sentiment analysis techniques. In: Proceedings of COLING 2014, the 25th international conference on computational linguistics: technical papers. Dublin City University and Association for Computational Linguistics, Dublin, pp 183–192Prager JM (1999) Linguini: language identification for multilingual documents. J Manage Inf Syst 16(3):71–101Ramón Quevedo J, Luaces O, Bahamonde A (2012) Multilabel classifiers with a probabilistic thresholding strategy. 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An O(n log n)-Time Algorithm for the Restricted Scaffold Assignment

The assignment problem takes as input two finite point sets S and T and establishes a correspondence between points in S and points in T, such that each point in S maps to exactly one point in T, and each point in T maps to at least one point in S. In this paper we show that this problem has an O(n log n)-time solution, provided that the points in S and T are restricted to lie on a line (linear time, if S and T are presorted).Comment: 13 pages, 8 figure