Abstract
Extracting entities and relations for types of interest from text is important for knowledge graph construction. Previous methods of entity and relation extraction rely on human-annotated corpora and adopt an incremental pipeline, which require abundant human expertise and are vulnerable to errors cascading. In this paper, we present ROTATE, a novel approach for jointly bootstrapping entity and relation extraction with reinforcement learning. The bootstrapping process of ROTATE consists of three bootstrapping sub-processes, which extract head entity, tail entity, and relation, respectively. Each sub-process starts with a few seed instances, then generates patterns and expands the seed set to start the next iteration. In particular, we propose a joint pattern scoring strategy in which the scores of patterns in each bootstrapping sub-process also take the seed information of the other two sub-processes into account. Moreover, we introduce reinforcement learning to solve the semantic drift problem in the bootstrapping process by formulating the seed expansion problem as a sequential decision making problem, and design a reward function that considers both seed quality and quantity. Experimental results on a collection of sentences from news articles confirm the effectiveness of our approach.
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Notes
- 1.
nltk.tokenize.punkt.PunktSentenceTokenizer.
- 2.
nltk.tokenize.treebank.TreebankWordTokenizer.
- 3.
taggers/maxent treebank pos tagger/english.pickle.
- 4.
skip length of 5 tokens and vectors of 200 dimensions.
- 5.
https://code.google.com/p/word2vec/.
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Xia, M., Cheng, X., Su, S., Kuang, M., Li, G. (2022). Bootstrapping Joint Entity and Relation Extraction with Reinforcement Learning. In: Chbeir, R., Huang, H., Silvestri, F., Manolopoulos, Y., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2022. WISE 2022. Lecture Notes in Computer Science, vol 13724. Springer, Cham. https://doi.org/10.1007/978-3-031-20891-1_30
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