Resumen: Este art́ıculo presenta un resumen de la tarea compartida y taller TweetLID, organizado junto a SEPLN 2014. Resume brevemente el proceso de colección y anotación de datos, el desarrollo y evaluación de la tarea compartida, y por último, los resultados obtenidos por los participantes

dos algoritmos de reconocimiento de lenguaje. Palabras clave: Reconocimiento de lenguaje, lenguaje de tweets. Abstract: This paper describes the methodology used for the SEPLN 14 shared task of tweet language identification (TweetLID), as explained on (Iñaki San Vicente, 2014). The system consists

. Finalmente, realizamos un análisis de los resultados obtenidos. Palabras clave: tweets, identificación de idioma, pesado de rasgos Abstract: This paper describes the language identification method presented in Twitter Language Identification Workshop (TweetLID-2014). The proposed method represents tweets

-specific data from Twitter, but it is unclear how well LID performs on short, informal texts in low-resource languages. We address this question by annotating and releasing a large collection of tweets in nine languages, focusing on confusable languages using the Cyrillic, Arabic, and Devanagari scripts

We study the problem of language vari-ant identification, approximated by the problem of labeling tweets from Spanish speaking countries by the country from which they were posted. While this task is closely related to “pure ” language iden-tification, it comes with additional com-plications. We

Language identification plays a major role in natural language processing ap-plications and is commonly used as a pre-processing phase. Typical tech-niques employ n-gram models and re-quire text parsing and cleanup such as punctuation removal, white-space nor-malization, etc. This paper investi

with noise, we propose a novel approach named NIRMAL, which automatically identifies software relevant tweets from a collection or stream of tweets. Our approach is based on language modeling which learns a statistical model based on a training corpus (i.e., set of documents). We make use of a subset

and Google+), but such information could be useful for targeting a specific audience for advertising, for personalizing content, and for legal investigation. These procedures, known as authorship identification, provide veritable information about the tweet author, for example, the gender of the author

Recently a number of requirements engineering languages and methods have flourished that do not only address what questions but also why, who and when questions. The objective of this paper is twofold: (i) to assess the strengths and weaknesses of one of these methodologies on a non

-dependent, and dynamic nature. Information extraction from tweets is typically performed in a pipeline, comprising consecutive stages of language identification, tokenisation, part-of-speech tagging, named entity recognition and entity disambiguation (e.g. with respect to DBpedia). In this work, we describe a new