Boosting Named Entity Recognition with Neural Character Embeddings (1505.05008v2)

Abstract: 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).

Enhancing Named Entity Recognition with Neural Character Embeddings

This paper investigates a novel approach to Named Entity Recognition (NER) through the incorporation of neural character embeddings, utilizing the CharWNN deep neural network architecture. Unlike traditional state-of-the-art NER systems reliant on handcrafted features and outputs derived from auxiliary NLP tasks, this research proposes a language-independent system that exclusively leverages automatically learned features. CharWNN adopts both word-level and character-level representations to perform sequential classification, a methodology previously demonstrated effective in the domain of POS tagging.

Key Contributions and Experimental Evaluations

The paper's primary contribution lies in the CharWNN architecture, which extends the work of Collobert et al. (2011) by integrating a convolutional layer specifically designed to extract character-level representations. This configuration facilitates effective feature extraction and enhances language independence by minimizing dependency on manually engineered features.

The authors conducted extensive experimentation using two annotated corpora: HAREM I for Portuguese and SPA CoNLL-2002 for Spanish. The results showed a significant performance uplift brought about by the character embeddings. Remarkably, CharWNN demonstrated a 7.9-point improvement over the state-of-the-art system in F1-score for the HAREM I corpus across the total scenario, and a 7.2-point increase in the selective scenario, underscoring the model's robustness and adaptability.

Implications and Comparative Analysis

The comparative analysis with other neural architectures like CharNN and WNN and traditional systems like AdaBoost for the SPA CoNLL-2002 corpus and ETL CMT for HAREM I highlighted the competitiveness of the character-level embeddings. Notably, CharWNN achieved state-of-the-art results without resorting to leveraging gazetteer-based features, as demonstrated by a comparison between CharWNN and an AdaBoost-based system for Spanish NER.

Furthermore, the paper underscores the pivotal role of unsupervised pre-training of word embeddings. The pre-training process provided a substantial performance increase, notably 13.2 points in F1-score for the Portuguese dataset, suggesting a future direction where the amalgamation of large-scale unsupervised embedding pre-training can be further optimized.

Future Directions and Theoretical Considerations

This work provides a understructure for future exploration of character-level embeddings in other NLP tasks. Given the demonstrated effectiveness across multiple languages, future research could investigate the model's performance and adaptability in handling increasingly complex and diverse linguistic structures. Moreover, the integration with transfer learning paradigms could be a promising direction, especially for low-resource languages where corpus availability is limited.

In conclusion, this paper presents a robust neural architecture for NER tasks that challenges the existing reliance on handcrafted features. This approach not only paves the way for more resource-efficient NER systems but also enriches the theoretical understanding of the integration and utility of multi-level word representations in neural networks.