External Lexical Information for Multilingual Part-of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven useful for improving the accuracy of statistical part-of-speech taggers. Here we compare the performances of four systems on datasets covering 16 languages, two of these systems being feature-based (MEMMs and CRFs) and two of them being neural-based (bi-LSTMs). We show that, on average, all four approaches perform similarly and reach state-of-the-art results. Yet better performances are obtained with our feature-based models on lexically richer datasets (e.g. for morphologically rich languages), whereas neural-based results are higher on datasets with less lexical variability (e.g. for English). These conclusions hold in particular for the MEMM models relying on our system MElt, which benefited from newly designed features. This shows that, under certain conditions, feature-based approaches enriched with morphosyntactic lexicons are competitive with respect to neural methods

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015

Methods for Amharic part-of-speech tagging

The paper describes a set of experiments involving the application of three state-of- the-art part-of-speech taggers to Ethiopian Amharic, using three different tagsets. The taggers showed worse performance than previously reported results for Eng- lish, in particular having problems with unknown words. The best results were obtained using a Maximum Entropy ap- proach, while HMM-based and SVM- based taggers got comparable results

Discovery of sensitive data with natural language processing

The process of protecting sensitive data is continually growing and becoming increasingly important, especially as a result of the directives and laws imposed by the European Union. The effort to create automatic systems is continuous, but in most cases, the processes behind them are still manual or semi-automatic. In this work, we have developed a component that can extract and classify sensitive data, from unstructured text information in European Portuguese. The objective was to create a system that allows organizations to understand their data and comply with legal and security purposes. We studied a hybrid approach to the problem of Named Entities Recognition for the Portuguese language. This approach combines several techniques such as rule-based/lexical-based models, machine learning algorithms and neural networks. The rule-based and lexical-based approaches were used only for a set of specific classes. For the remaining classes of entities, SpaCy and Stanford NLP tools were tested, two statistical models – Conditional Random Fields and Random Forest – were implemented and, finally, a Bidirectional- LSTM approach as experimented. The best results were achieved with the Stanford NER model (86.41%), from the Stanford NLP tool. Regarding the statistical models, we realized that Conditional Random Fields is the one that can obtain the best results, with a f1-score of 65.50%. With the Bi-LSTM approach, we have achieved a result of 83.01%. The corpora used for training and testing were HAREM Golden Collection, SIGARRA News Corpus and DataSense NER Corpus.O processo de preservação de dados sensíveis está em constante crescimento e cada vez apresenta maior importância, proveniente especialmente das diretivas e leis impostas pela União Europeia. O esforço para criar sistemas automáticos é contínuo, mas o processo é realizado na maioria dos casos de forma manual ou semiautomática. Neste trabalho desenvolvemos um componente de Extração e Classificação de dados sensíveis, que processa textos não-estruturados em Português Europeu. O objetivo consistiu em criar um sistema que permite às organizações compreender os seus dados e cumprir com fins legais de conformidade e segurança. Para resolver este problema, foi estudada uma abordagem híbrida de Reconhecimento de Entidades Mencionadas para a língua Portuguesa. Esta abordagem combina técnicas baseadas em regras e léxicos, algoritmos de aprendizagem automática e redes neuronais. As primeiras abordagens baseadas em regras e léxicos, foram utilizadas apenas para um conjunto de classes especificas. Para as restantes classes de entidades foram utilizadas as ferramentas SpaCy e Stanford NLP, testados dois modelos estatísticos — Conditional Random Fields e Random Forest – e por fim testada uma abordagem baseada em redes neuronais – Bidirectional-LSTM. Ao nível das ferramentas utilizadas os melhores resultados foram conseguidos com o modelo Stanford NER (86,41%). Através dos modelos estatísticos percebemos que o Conditional Random Fields é o que consegue obter melhores resultados, com um f1-score de 65,50%. Com a última abordagem, uma rede neuronal Bi-LSTM, conseguimos resultado de f1-score de aproximadamente 83,01%. Para o treino e teste das diferentes abordagens foram utilizados os conjuntos de dados HAREM Golden Collection, SIGARRA News Corpus e DataSense NER Corpus

Model Transfer for Tagging Low-resource Languages using a Bilingual Dictionary

Cross-lingual model transfer is a compelling and popular method for predicting annotations in a low-resource language, whereby parallel corpora provide a bridge to a high-resource language and its associated annotated corpora. However, parallel data is not readily available for many languages, limiting the applicability of these approaches. We address these drawbacks in our framework which takes advantage of cross-lingual word embeddings trained solely on a high coverage bilingual dictionary. We propose a novel neural network model for joint training from both sources of data based on cross-lingual word embeddings, and show substantial empirical improvements over baseline techniques. We also propose several active learning heuristics, which result in improvements over competitive benchmark methods.Comment: 5 pages with 2 pages reference. Accepted to appear in ACL 201