Joint entity recognition and relation extraction as a multi-head selection problem (1804.07847v3)

Abstract: State-of-the-art models for joint entity recognition and relation extraction strongly rely on external NLP tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.

Joint Entity Recognition and Relation Extraction as a Multi-Head Selection Problem

The paper by Giannis Bekoulis et al. centers on a joint model aimed at improving the process of entity recognition and relation extraction in unstructured texts. This work proposes an innovative approach by framing the tasks of entity recognition and relation extraction under a unified model without the reliance on external tools or manually extracted features. This paper targets researchers specializing in NLP, information extraction, and deep learning, offering a structured solution to an ongoing challenge in the field.

Entity recognition and relation extraction are fundamental operations in information extraction, necessary for applications like knowledge base population and question answering systems. Traditionally, these tasks have been handled separately through a pipeline model consisting of named entity recognition (NER) followed by relation extraction (RE). However, prevailing pipeline models suffer from error propagation between tasks and miss the benefit of shared information that a joint approach could harness, as some prior works have demonstrated with state-of-the-art performance using joint models.

Bekoulis et al. critique preceding joint models for their dependence on hand-crafted features and external NLP tools, which are often unreliable across different languages and contexts. This paper introduces a neural model that bypasses these dependencies by integrating a Conditional Random Field (CRF) for the NER task and a multi-head selection framework for the RE task. By treating relation extraction as a multi-label classification problem, where each token can simultaneously participate in multiple relations, the model provides a more comprehensive extraction mechanism.

The proposed model yields significant performance improvements over both feature-based neural approaches and those that do not rely on pre-calculated NLP features. The empirical results span diverse datasets, including news (ACE04, CoNLL04), biomedical (ADE), and real estate (DREC) domains, across English and Dutch languages. In particular, on the biomedical ADE dataset, the model surpassed previous models by a notable margin in both NER and RE tasks, validating the potential of this approach in contexts where NLP tools traditionally exhibit weaker performance.

Among the technical innovations of the paper, notable is the use of character embeddings, enhancing the feature representation of words and capturing morphological features such as prefixes or suffixes within a language. The paper shows that character embeddings lead to an increase in overall F1 performance by approximately 2%. Furthermore, the adaptation of the multi-head selection problem allows this model to support multiple relations per entity, addressing typical constraints observed in other neural architectures like those using softmax to predict single labels.

Moreover, experiments confirm the efficacy of character-based embeddings and the CRF for NER, both components contributing to a robust architecture demonstrated superior in various linguistic and contextual setups. The discussion in the paper extends beyond empirical results, suggesting the utility of embedding pre-training to potentially enhance the entity recognition module further.

Future research, following this work, could explore strategies like pre-training to boost entity recognition and developing more efficient inference techniques to optimize quadratic relation scoring. Such advances would continue to enhance NLP framework capabilities, potentially widening the model's applicability and efficiency in real-world tasks.

In summary, the model presented in this paper addresses fundamental challenges in NLP with a unique architectural approach that eschews dependency on unreliable external tools, delivering consistent and improved performance across diverse datasets and task scenarios. This advancement reinforces the adaptability and scalability of end-to-end deep learning models in information extraction tasks, proposing a viable pathway for ongoing research and application in NLP.