Abstract
Web-based mining is a promising approach for entity translation. Traditional web-based mining methods construct spotting queries in a heuristic, one-step paradigm, which cannot resolve the diversity of entity names and is incapable of leveraging the information in the returned bilingual pages for iterative spotting query refinement. To resolve the above drawbacks, this paper proposes a reinforcement learning-based method, which models web-based entity translation mining as a Markov Decision Process (MDP). Specifically, we regard the query construction as a multi-turn, state-to-action mapping procedure, and learn a Dueling Deep Q-network based clue selection agent which can adaptively select spotting clues based on both short-term and long-term benefits. Experiments verified the effectiveness of our approach.
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Notes
- 1.
The BIO tags are widely used in the sequence labeling tasks, where B, I, and O refer to the beginning, inside and outside token of the chunks in target types.
- 2.
The translation pairs in the LDC datasets can be easily fetched from a specific website (http://www.ichacha.com), leading to almost static search results with varied queries. We therefore construct a new dataset. The person, company, and film names are correspondingly collected from Wikidata (https://www.wikidata.org), Forbes (https://www.forbes.com), and IMDB (http://www.imdb.com).
- 3.
Dataset and source code are available at https://github.com/lingyongyan/entity_translation.
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Acknowledge
This research work is supported by the National Key Research and Development Program of China under Grant No. 2017YFB1002104, the National Natural Science Foundation of China under Grants no. U1936207, Beijing Academy of Artificial Intelligence (BAAI2019QN0502), and in part by the Youth Innovation Promotion Association CAS (2018141).
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Yan, L., Han, X., Sun, L. (2021). Reinforcement Learning for Clue Selection in Web-Based Entity Translation Mining. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_6
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