Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction (1911.09886v1)

Abstract: A relation tuple consists of two entities and the relation between them, and often such tuples are found in unstructured text. There may be multiple relation tuples present in a text and they may share one or both entities among them. Extracting such relation tuples from a sentence is a difficult task and sharing of entities or overlapping entities among the tuples makes it more challenging. Most prior work adopted a pipeline approach where entities were identified first followed by finding the relations among them, thus missing the interaction among the relation tuples in a sentence. In this paper, we propose two approaches to use encoder-decoder architecture for jointly extracting entities and relations. In the first approach, we propose a representation scheme for relation tuples which enables the decoder to generate one word at a time like machine translation models and still finds all the tuples present in a sentence with full entity names of different length and with overlapping entities. Next, we propose a pointer network-based decoding approach where an entire tuple is generated at every time step. Experiments on the publicly available New York Times corpus show that our proposed approaches outperform previous work and achieve significantly higher F1 scores.

Effective Modeling of Encoder-Decoder Architecture for Joint Entity and Relation Extraction

The paper authored by Tapas Nayak and Hwee Tou Ng addresses a significant challenge in the domain of NLP: the extraction of relation tuples from unstructured text. Relation tuples, comprising two entities and the relation between them, play a crucial role in information extraction tasks. This work critiques the prevalent pipeline approach, which separates the tasks of Named Entity Recognition (NER) and relation classification, thus potentially missing interactions among entities within a sentence.

Research Contributions

The authors propose two novel approaches leveraging encoder-decoder architectures for joint extraction of entities and relations. This marks a departure from conventional methods by seeking to capture interactions between relation tuples inherently within the model. The primary strategies introduced are:

1. Word-level Decoding with a New Representation Scheme: This approach conceptualizes relation tuples in a manner akin to machine translation, where entities and relations are generated word by word. A unique encoding scheme enables the model to manage multiple and overlapping entities effectively.
2. Pointer Network-based Decoding: This approach utilizes the pointer network mechanism, which generates an entire tuple per time step. By predicting the start/end positions of entities directly, it aligns closer to the natural format of relation tuples and enhances the decoding efficiency.

Experimental Evaluation

Empirical results are presented using the New York Times (NYT) corpus. The proposed methods notably advance the state-of-the-art performance in joint entity and relation extraction, achieving significantly higher F1 scores than existing models like CopyR, HRL, and others. The analysis highlights the trade-off between the two methods: the word-level decoding exhibits proficient recall due to its granularity, while the pointer network-based model benefits from reduced computational overhead and increased suitability to tuple generation tasks.

Discussion

In the system's architecture, the encoder-decoder model faces typical challenges such as varied entity lengths, the presence of overlapping entities, and ineffective generation of full entity names. The innovative use of a masking-based copy mechanism ensures that entities are extracted accurately from input sentences, thereby overcoming limitations seen in prior models like CopyR.

The introduction of a pointer network within the encoder-decoder paradigm particularly stands out due to its efficiency in terms of training time and resource consumption. This method's design enables it to potentially scale to document-level extraction tasks, promising avenues for future exploration.

Implications and Future Work

The implications of this research are two-fold: theoretically, it provides a more integrated approach to entity and relation extraction, emphasizing the need for systems that inherently consider interactions between elements. Practically, it points towards more efficient extraction models usable in real-world applications, especially in scenarios involving large document collections.

Future research directions could include expanding the models to handle document-level extraction tasks, where the current sentence-level focus might limit application. Additionally, investigating models' adaptability to different languages or domains could extend their utility in broader contexts.

In conclusion, Nayak and Ng present a compelling argument for shifting towards joint extraction architectures. Through their rigorous experimentation and thoughtful consideration of the intricacies involved in tuple extraction, their contributions represent a meaningful step forward in the field of relation extraction.