A prevalent approach of entity-oriented systems involves retrieving relevant entities by harnessing knowledge graph embeddings. These embeddings encode entity information in the context of the knowledge graph and are static in nature. Our goal is to generate entity embeddings that capture what renders them relevant for the query. This differs from entity embeddings constructed with static resource, for example, E-BERT. Previously, ~\citet{dalton2014entity} demonstrated the benefits obtained with the Entity Context Model, a pseudo-relevance feedback approach based on entity links in relevant contexts. In this work, we reinvent the Entity Context Model (ECM) for neural graph networks and incorporate pre-trained embeddings. We introduce three entity ranking models based on fundamental principles of ECM: (1) \acl{GAN}, (2) Simple Graph Relevance Networks, and (3) Graph Relevance Networks. \acl{GAN} and Graph Relevance Networks are the graph neural variants of ECM, that employ attention mechanism and relevance information of the relevant context respectively to ascertain entity relevance. Our experiments demonstrate that our neural variants of the ECM model significantly outperform the state-of-the-art BERT-ER ~\cite{10.1145/3477495.3531944} by more than 14\% and exceeds the performance of systems that use knowledge graph embeddings by over 101\%. Notably, our findings reveal that leveraging the relevance of the relevant context is more effective at identifying relevant entities than the attention mechanism. To evaluate the efficacy of the models, we conduct experiments on two standard benchmark datasets, DBpediaV2 and TREC Complex Answer Retrieval. To aid reproducibility, our code and data are available. https://github.com/TREMA-UNH/neural-entity-context-models
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Entity Retrieval Using Fine-Grained Entity Aspects
Using entity aspect links, we improve upon the current state-of-the-art in entity retrieval. Entity retrieval is the task of retrieving relevant entities for search queries, such as "Antibiotic Use In Livestock". Entity aspect linking is a new technique to refine the semantic information of entity links. For example, while passages relevant to the query above may mention the entity "USA", there are many aspects of the USA of which only few, such as "USA/Agriculture", are relevant for this query. By using entity aspect links that indicate which aspect of an entity is being referred to in the context of the query, we obtain more specific relevance indicators for entities. We show that our approach improves upon all baseline methods, including the current state-of-the-art using a standard entity retrieval test collection. With this work, we release a large collection of entity-aspect-links for a large TREC corpus.
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- Award ID(s):
- 1846017
- NSF-PAR ID:
- 10300273
- Date Published:
- Journal Name:
- Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)
- Page Range / eLocation ID:
- 1662 to 1666
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Hauff, Claudia Curry (Ed.)Given a text with entity links, the task of entity aspect linking is to identify which aspect of an entity is referred to in the context. For example, if a text passage mentions the entity "USA'', is USA mentioned in the context of the 2008 financial crisis, American cuisine, or else? Complementing efforts of Nanni et al (2018), we provide a large-scale test collection which is derived from Wikipedia hyperlinks in a dump from 01/01/2020. Furthermore, we offer strong baselines with results and broken-out feature sets to stimulate more research in this area. Data, code, feature sets, runfiles and results are released under a CC-SA license and offered on our aspect linking resource web page http://www.cs.unh.edu.unh.idm.oclc.org/~dietz/eal-dataset-2020/more » « less
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Abstract Natural language processing (NLP) techniques can enhance our ability to interpret plant science literature. Many state-of-the-art algorithms for NLP tasks require high-quality labelled data in the target domain, in which entities like genes and proteins, as well as the relationships between entities, are labelled according to a set of annotation guidelines. While there exist such datasets for other domains, these resources need development in the plant sciences. Here, we present the Plant ScIenCe KnowLedgE Graph (PICKLE) corpus, a collection of 250 plant science abstracts annotated with entities and relations, along with its annotation guidelines. The annotation guidelines were refined by iterative rounds of overlapping annotations, in which inter-annotator agreement was leveraged to improve the guidelines. To demonstrate PICKLE’s utility, we evaluated the performance of pretrained models from other domains and trained a new, PICKLE-based model for entity and relation extraction (RE). The PICKLE-trained models exhibit the second-highest in-domain entity performance of all models evaluated, as well as a RE performance that is on par with other models. Additionally, we found that computer science-domain models outperformed models trained on a biomedical corpus (GENIA) in entity extraction, which was unexpected given the intuition that biomedical literature is more similar to PICKLE than computer science. Upon further exploration, we established that the inclusion of new types on which the models were not trained substantially impacts performance. The PICKLE corpus is, therefore, an important contribution to training resources for entity and RE in the plant sciences.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3404835.3463035
