Entity Linking for Queries by Searching Wikipedia Sentences
This addresses entity linking for queries, improving accuracy for search and information retrieval systems, but it is incremental as it builds on existing methods with enhancements.
The paper tackles entity linking in queries by searching Wikipedia sentences for similar ones and using their annotated entities as candidates, then ranking them with features like link-probability and word embeddings, achieving 75.0% F1 on ERD14 and 56.9% on GERDAQ datasets.
We present a simple yet effective approach for linking entities in queries. The key idea is to search sentences similar to a query from Wikipedia articles and directly use the human-annotated entities in the similar sentences as candidate entities for the query. Then, we employ a rich set of features, such as link-probability, context-matching, word embeddings, and relatedness among candidate entities as well as their related entities, to rank the candidates under a regression based framework. The advantages of our approach lie in two aspects, which contribute to the ranking process and final linking result. First, it can greatly reduce the number of candidate entities by filtering out irrelevant entities with the words in the query. Second, we can obtain the query sensitive prior probability in addition to the static link-probability derived from all Wikipedia articles. We conduct experiments on two benchmark datasets on entity linking for queries, namely the ERD14 dataset and the GERDAQ dataset. Experimental results show that our method outperforms state-of-the-art systems and yields 75.0% in F1 on the ERD14 dataset and 56.9% on the GERDAQ dataset.