Articles

AbstractIn this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp). There are very few digital texts written in Shipibo-konibo and even less bilingual texts that can be aligned, hence we had to create a parallel corpus using both bilingual and …...

AbstractThis paper discusses the general methodology and important practical aspects of implementing a new bilingual lexical resource – the Russian-Tatar Socio-Political Thesaurus that is being developed on the basis of the Russian RuThes thesaurus format as a hierarchy of concepts viewed as units of thought. Each concept is linked with …...

AbstractThe recent failures of traditional poll models, like the predictions in United Kingdom with the Brexit, or in United States presidential elections with the victory of Donald Trump, have been noteworthy. With the decline of traditional poll models and the growth of the social networks, automatic tools are gaining popularity …...

AbstractDetecting which tweets describe a specific event and clustering them is one of the main challenging tasks related to Social Media currently addressed in the NLP community. Existing approaches have mainly focused on detecting spikes in clusters around specific keywords or Named Entities (NE). However, one of the main drawbacks …...

AbstractThis demo paper presents a system that builds a timeline with salient actions of a soccer game, based on the tweets posted by users. It combines information provided by external knowledge bases to enrich the content of tweets and applies graph theory to model relations between actions (e.g. goals, penalties) …...

AbstractIn this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We …...

AbstractIn this article we propose a stylistic analysis of Solomon Marcus’ non-scientific published texts, gathered in six volumes, aiming to uncover some of his quantitative and qualitative fingerprints. Moreover, we compare and cluster two distinct periods of time in his writing style: 22 years of communist regime (1967-1989) and 27 …...

AbstractThe automatic identification of discourse relations is still a challenging task in natural language processing. Discourse connectives, such as since or but, are the most informative cues to identify explicit relations; however discourse parsers typically use a closed inventory of such connectives. As a result, discourse relations signalled by markers …...

AbstractWe investigate parsing replicability across 7 languages (and 8 treebanks), showing that choices concerning the use of grammatical functions in parsing or evaluation, the influence of the rare word threshold, as well as choices in test sentences and evaluation script options have considerable and often unexpected effects on parsing accuracies. …...

AbstractIn this paper, we present an application for the automatic identification of the important moments that might occur during students’ collaborative chats. The moments are detected based on the input received from the user, who may choose to perform an analysis on the topics that interest him/her. Moreover, the application …...

AbstractRadiology reports express the results of a radiology study and contain information about anatomical entities, findings, measures and impressions of the medical doctor. The use of information extraction techniques can help physicians to access this information in order to understand data and to infer further knowledge. Supervised machine learning methods …...

AbstractWith the increasing number of communication platforms that offer variety of ways of connecting two interlocutors, there is a resurgence of chat-based dialog systems. These systems, typically known as chatbots have been successfully applied in a range of consumer and enterprise applications. A key technology in such chat-bots is robust …...

AbstractEmotion detection has a high potential positive impact on the benefit of business, society, politics or education. Given this, the main objective of our research is to contribute to the resolution of one of the most important challenges in textual emotion detection: emotional corpora annotation. This will be tackled by …...

AbstractNamed Entity Recognition (NER) poses new challenges in real-world documents in which there are entities with different roles according to their purpose or meaning. Retrieving all the possible entities in scenarios in which only a subset of them based on their role is needed, produces noise on the overall precision. …...

AbstractWe propose a novel discriminative ranking model that learns embeddings from multilingual and multi-modal data, meaning that our model can take advantage of images and descriptions in multiple languages to improve embedding quality. To that end, we introduce an objective function that uses pairwise ranking adapted to the case of …...

AbstractShallow text analysis (Text Mining) uses mainly Information Extraction techniques. The low resource languages do not allow application of such traditional techniques with sufficient accuracy and recall on big data. In contrast, Data Mining approaches provide an opportunity to make deep analysis and to discover new knowledge. Frequent pattern mining …...

AbstractWe propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from the typically much longer texts of questions and answers in a forum. We then use these shorter texts to train …...

AbstractWe present CHARCUT, a character-based machine translation evaluation metric derived from a human-targeted segment difference visualisation algorithm. It combines an iterative search for longest common substrings between the candidate and the reference translation with a simple length-based threshold, enabling loose differences that limit noisy character matches. Its main advantage is …...

AbstractWe present and apply two methods for addressing the problem of selecting relevant training data out of a general pool for use in tasks such as machine translation. Building on existing work on class-based language difference models [1], we first introduce a cluster-based method that uses Brown clusters to condense …...

AbstractBilingual sequence models improve phrase-based translation and reordering by overcoming phrasal independence assumption and handling long range reordering. However, due to data sparsity, these models often fall back to very small context sizes. This problem has been previously addressed by learning sequences over generalized representations such as POS tags or …...