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1. Deep transfer learning for multi-source entity linkage via domain adaptation

   https://doi.org/10.14778/3494124.3494131  

   Jin, Di; Sisman, Bunyamin; Wei, Hao; Dong, Xin Luna; Koutra, Danai (November 2021, Proceedings of the VLDB Endowment)

   Multi-source entity linkage focuses on integrating knowledge from multiple sources by linking the records that represent the same real world entity. This is critical in high-impact applications such as data cleaning and user stitching. The state-of-the-art entity linkage pipelines mainly depend on supervised learning that requires abundant amounts of training data. However, collecting well-labeled training data becomes expensive when the data from many sources arrives incrementally over time. Moreover, the trained models can easily overfit to specific data sources, and thus fail to generalize to new sources due to significant differences in data and label distributions. To address these challenges, we present AdaMEL, a deep transfer learning framework that learns generic high-level knowledge to perform multi-source entity linkage. AdaMEL models the attribute importance that is used to match entities through an attribute-level self-attention mechanism, and leverages the massive unlabeled data from new data sources through domain adaptation to make it generic and data-source agnostic. In addition, AdaMEL is capable of incorporating an additional set of labeled data to more accurately integrate data sources with different attribute importance. Extensive experiments show that our framework achieves state-of-the-art results with 8.21% improvement on average over methods based on supervised learning. Besides, it is more stable in handling different sets of data sources in less runtime. 

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2. Entity Retrieval Using Fine-Grained Entity Aspects

   https://doi.org/10.1145/3404835.3463035  

   Chatterjee, Shubham; Dietz, Laura (July 2021, Proceedings of The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR))

   null; null; null (Ed.)

   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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3. Injecting Entity Types into Entity-Guided Text Generation

   https://doi.org/10.18653/v1/2021.emnlp-main.56  

   Dong, Xiangyu; Yu, Wenhao; Zhu, Chenguang; Jiang, Meng (January 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing)

   Recent successes in deep generative modeling have led to significant advances in natural language generation (NLG). Incorporating entities into neural generation models has demonstrated great improvements by assisting to infer the summary topic and to generate coherent content. To enhance the role of entity in NLG, in this paper, we aim to model the entity type in the decoding phase to generate contextual words accurately. We develop a novel NLG model to produce a target sequence based on a given list of entities. Our model has a multi-step decoder that injects the entity types into the process of entity mention generation. Experiments on two public news datasets demonstrate type injection performs better than existing type embedding concatenation baselines. 

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4. Entity Embeddings for Entity Ranking: A Replicability Study

   https://doi.org/10.1007/978-3-031-28241-6_8  

   Oza, Pooja; Dietz, Laura (April 2023, Springer)

   Knowledge Graph embeddings model semantic and struc- tural knowledge of entities in the context of the Knowledge Graph. A nascent research direction has been to study the utilization of such graph embeddings for the IR-centric task of entity ranking. In this work, we replicate the GEEER study of Gerritse et al. [9] which demonstrated improvements of Wiki2Vec embeddings on entity ranking tasks on the DBpediaV2 dataset. We further extend the study by exploring additional state-of-the-art entity embeddings ERNIE [27] and E-BERT [19], and by including another test collection, TREC CAR, with queries not about person, location, and organization entities. We confirm the finding that entity embeddings are beneficial for the entity ranking task. Interestingly, we find that Wiki2Vec is competitive with ERNIE and E-BERT. Our code and data to aid reproducibility and further research is available at https://github.com/poojahoza/E3R-Replicability 

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5. A Generative Entity-to-Entity Stance Detection Framework

   Zhang, X.; Beauchamp, N.; Wang, L. (January 2022, Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP))