Boosting Named Entity Recognition with Neural Character Embeddings

Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features and on the output of other NLP tasks such as part-of-speech (POS) tagging and text chunking. In this work we propose a language-independent NER system that uses automatically learned features only. Our approach is based on the CharWNN deep neural network, which uses word-level and character-level representations (embeddings) to perform sequential classification. We perform an extensive number of experiments using two annotated corpora in two different languages: HAREM I corpus, which contains texts in Portuguese; and the SPA CoNLL-2002 corpus, which contains texts in Spanish. Our experimental results shade light on the contribution of neural character embeddings for NER. Moreover, we demonstrate that the same neural network which has been successfully applied to POS tagging can also achieve state-of-the-art results for language-independet NER, using the same hyperparameters, and without any handcrafted features. For the HAREM I corpus, CharWNN outperforms the state-of-the-art system by 7.9 points in the F1-score for the total scenario (ten NE classes), and by 7.2 points in the F1 for the selective scenario (five NE classes).Comment: 9 page

Classifying Relations by Ranking with Convolutional Neural Networks

Relation classification is an important semantic processing task for which state-ofthe-art systems still rely on costly handcrafted features. In this work we tackle the relation classification task using a convolutional neural network that performs classification by ranking (CR-CNN). We propose a new pairwise ranking loss function that makes it easy to reduce the impact of artificial classes. We perform experiments using the the SemEval-2010 Task 8 dataset, which is designed for the task of classifying the relationship between two nominals marked in a sentence. Using CRCNN, we outperform the state-of-the-art for this dataset and achieve a F1 of 84.1 without using any costly handcrafted features. Additionally, our experimental results show that: (1) our approach is more effective than CNN followed by a softmax classifier; (2) omitting the representation of the artificial class Other improves both precision and recall; and (3) using only word embeddings as input features is enough to achieve state-of-the-art results if we consider only the text between the two target nominals.Comment: Accepted as a long paper in the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015

As formas do epílogo no teatro de Cervantes

IX Congresso Brasileiro de Hispanistas realizado nos dias 22 a 25 agosto 2016Como se sabe, os dramaturgos espanhóis dos séculos XVI e XVII compunham suas obras tomando como base as “teorias” clássicas no que diz respeito aos recursos poéticos e retóricos. Para isso recorriam a obras como a Poética e a Retórica de Aristóteles e também de Quintiliano, entre outros. No entanto, também é sabido que alguns autores dessas centúrias começaram a criar e utilizar novas técnicas e recursos, no momento de composição de suas peças, como foi o caso de Lope de Vega. Miguel de Cervantes, ao contrário, foi um escritor que, por um bom tempo, preferiu manter vivas as técnicas provenientes das preceptivas clássicas. Mais adiante, porém, ele começa a introduzir, pouco a pouco, novos recursos em suas obras. Diante disso, nesse texto, pretendemos mostrar as diferentes formas de epílogos empregadas por Cervantes para a construção das peças La Numancia, publicada provavelmente entre os anos 1580 e 1585, e das Ocho comedias, publicadas em 1615. Dessa forma, verificaremos se as variações existentes entre esses encerramentos estão em conformidade com a adoção de novas técnicasUNILA­-UNIOEST

Credit risk modelling using multi-state markov models

A Dissertation as a partial requirement to obtain the degree of Master in Statistics and Information Management, specialization in Risk Analysis and ManagementThis paper is devoted to credit risk modelling issues concerning mortgage commercial loans. Mortgage loans are one of the most popular type of loans provided by credit institutions. Like in the case of other loans, the main concern of institutions providing this type of product is a potential inability to recover the amount assigned to their clients (credit risk). In order to prevent possible losses for credit institutions resulting from clients entering in default, it is therefore crucial to study the behaviour of risky clients. This issue can be addressed through several models, namely through the multi-state Markov model, despite it constituting a more unusual approach in the context of dealing with credit risk modelling. The multi-state Markov model is a useful way of describing a process in which an individual moves through a series of states (finite number) in continuous time. By fitting this model to the loans of risky clients, it is possible to estimate the mean sojourn time in each state before a transition occurs, as well as the transition probabilities between the different states assumed by the contracts, therefore providing a relevant modelling framework for event history data. The present work relies upon 2008-13 databases from one of the biggest American companies that act in the secondary mortgage market, the Fannie Mae. Results show that with the application of the multi-state Markov model, contracts signed during 2013 are more propitious to a scenario of recovery when compared to those referring to the year 2008