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1. Humans Optional? Automatic Large-Scale Test Collections for Entity, Passage, and Entity-Passage Retrieval

   https://doi.org/10.1007/s13222-020-00334-y  

   Dietz, Laura Dalton (March 2020, DatenbankSpektrum)

   Schaer, Philipp Berberich (Ed.)

   Manually creating test collections is a time-, effort-, and cost-intensive process. This paper describes a fully automatic alternative for deriving large-scale test collections, where no human assessments are needed. The empirical experiments confirm that automatic test collection and manual assessments agree on the best performing systems. The collection includes relevance judgments for both text passages and knowledge base entities. Since test collections with relevance data for both entity and text passages are rare, this approach provides a cost-efficient way for training and evaluating ad hoc passage retrieval, entity retrieval, and entity-aware text retrieval methods. 

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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. Neural Entity Context Models

   Oza, Pooja; Chatterjee, Shubham; Dietz, Laura (December 2023, ACM)

   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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4. LATTE: Latent Type Modeling for Biomedical Entity Linking

   Zhu, Ming; Celikkaya, Busra; Bhatia, Parminder; Reddy, Chandan K. (January 2020, AAAI Conference on Artificial Intelligence (AAAI))

   Entity linking is the task of linking mentions of named entities in natural language text, to entities in a curated knowledge-base. This is of significant importance in the biomedical domain, where it could be used to semantically annotate a large volume of clinical records and biomedical literature, to standardized concepts described in an ontology such as Unified Medical Language System (UMLS). We observe that with precise type information, entity disambiguation becomes a straightforward task. However, fine-grained type information is usually not available in biomedical domain. Thus, we propose LATTE, a LATent Type Entity Linking model, that improves entity linking by modeling the latent fine-grained type information about mentions and entities. Unlike previous methods that perform entity linking directly between the mentions and the entities, LATTE jointly does entity disambiguation, and latent fine-grained type learning, without direct supervision. We evaluate our model on two biomedical datasets: MedMentions, a large scale public dataset annotated with UMLS concepts, and a de-identified corpus of dictated doctor’s notes that has been annotated with ICD concepts. Extensive experimental evaluation shows our model achieves significant performance improvements over several state-of-the-art techniques. 

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5. Which entities are relevant for the story?

   Oza, Pooja; Dietz, Laura (May 2021, CEUR workshop proceedings)

   Campos, Ricardo; Jorge, Alípio Mário; Jatowt, Adam; Bhatia, Sumit; Finlayson, Mark (Ed.)

   A crucial step in the construction of any event story or news report is to identify entities involved in the story, such entities can come from a larger background knowledge graph or from a text corpus with entity links. Along with recognizing which entities are relevant to the story, it is also important to select entities that are relevant to all aspects of the story. In this work, we model and study different types of links between the entities with the goal of identifying which link type is most useful for the entity retrieval task. Our approach demonstrates the e 

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