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1. Fine-Grained Named Entity Recognition with Distant Supervision in COVID-19 Literature

   https://doi.org/10.1109/BIBM49941.2020.9313126  

   Wang, Xuan ; Song, Xiangchen ; Li, Bangzheng ; Zhou, Kang ; Li, Qi ; Han, Jiawei ( December 2020 , BIBM'20, IEEE Int. Conf. on Bioinformatics and Biomedicine, Dec 2020)

   null (Ed.)

   Biomedical named entity recognition (BioNER) is a fundamental step for mining COVID-19 literature. Existing BioNER datasets cover a few common coarse-grained entity types (e.g., genes, chemicals, and diseases), which cannot be used to recognize highly domain-specific entity types (e.g., animal models of diseases) or emerging ones (e.g., coronaviruses) for COVID-19 studies. We present CORD-NER, a fine-grained named entity recognized dataset of COVID-19 literature (up until May 19, 2020). CORD-NER contains over 12 million sentences annotated via distant supervision. Also included in CORD-NER are 2,000 manually-curated sentences as a test set for performance evaluation. CORD-NER covers 75 fine-grained entity types. In addition to the common biomedical entity types, it covers new entity types specifically related to COVID-19 studies, such as coronaviruses, viral proteins, evolution, and immune responses. The dictionaries of these fine-grained entity types are collected from existing knowledge bases and human-input seed sets. We further present DISTNER, a distantly supervised NER model that relies on a massive unlabeled corpus and a collection of dictionaries to annotate the COVID-19 corpus. DISTNER provides a benchmark performance on the CORD-NER test set for future research. 

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2. A Query Understanding Framework for Earth Data Discovery

   https://doi.org/10.3390/app10031127  

   Li, Yun ; Jiang, Yongyao ; Goldstein, Justin C. ; Mcgibbney, Lewis J. ; Yang, Chaowei ( February 2020 , Applied Sciences)

   One longstanding complication with Earth data discovery involves understanding a user’s search intent from the input query. Most of the geospatial data portals use keyword-based match to search data. Little attention has focused on the spatial and temporal information from a query or understanding the query with ontology. No research in the geospatial domain has investigated user queries in a systematic way. Here, we propose a query understanding framework and apply it to fill the gap by better interpreting a user’s search intent for Earth data search engines and adopting knowledge that was mined from metadata and user query logs. The proposed query understanding tool contains four components: spatial and temporal parsing; concept recognition; Named Entity Recognition (NER); and, semantic query expansion. Spatial and temporal parsing detects the spatial bounding box and temporal range from a query. Concept recognition isolates clauses from free text and provides the search engine phrases instead of a list of words. Name entity recognition detects entities from the query, which inform the search engine to query the entities detected. The semantic query expansion module expands the original query by adding synonyms and acronyms to phrases in the query that was discovered from Web usage data and metadata. The four modules interact to parse a user’s query from multiple perspectives, with the goal of understanding the consumer’s quest intent for data. As a proof-of-concept, the framework is applied to oceanographic data discovery. It is demonstrated that the proposed framework accurately captures a user’s intent. 

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3. Learning to Rank Entities for Set Expansion from Unstructured Data

   https://doi.org/10.1145/3409256.3409811  

   Yu, Puxuan ; Rahimi, Razieh ; Huang, Zhiqi ; Allan, James ( September 2020 , Proceedings of the ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR 2020))

   null (Ed.)

   Entity set expansion (ESE) refers to mining ``siblings'' of some user-provided seed entities from unstructured data. It has drawn increasing attention in the IR and NLP communities for its various applications. To the best of our knowledge, there has not been any work towards a supervised neural model for entity set expansion from unstructured data. We suspect that the main reason is the lack of massive annotated entity sets. In order to solve this problem, we propose and implement a toolkit called {DBpedia-Sets}, which automatically extracts entity sets from any plain text collection and can provide a large number of distant supervision data for neural model training. We propose a two-channel neural re-ranking model {NESE} that jointly learns exact and semantic matching of entity contexts. The former accepts entity-context co-occurrence information and the latter learns a non-linear transformer from generally pre-trained embeddings to ESE-task specific embeddings for entities. Experiments on real datasets of different scales from different domains show that {NESE} outperforms state-of-the-art approaches in terms of precision and MAP, where the improvements are statistically significant and are higher when the given corpus is larger. 

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4. 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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5. Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains

   https://doi.org/10.1609/AAAI.V38I17.29933  

   Zhang, Yu ; Zhang, Yunyi ; Shen, Yanzhen ; Deng, Yu ; Popa, Lucian ; Shwartz, Larisa ; Zhai, ChengXiang ; Han, Jiawei ( March 2024 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Wooldridge, Michael J ; Dy, Jennifer G ; Natarajan, Sriraam (Ed.)

   Accurately typing entity mentions from text segments is a fundamental task for various natural language processing applications. Many previous approaches rely on massive human-annotated data to perform entity typing. Nevertheless, collecting such data in highly specialized science and engineering domains (e.g., software engineering and security) can be time-consuming and costly, without mentioning the domain gaps between training and inference data if the model needs to be applied to confidential datasets. In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i.e., those without seed entities). To solve this problem, we propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus using the contextualized representations of pre-trained language models. It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types. Extensive experiments on two datasets covering four domains demonstrate the effectiveness of SEType in comparison with various baselines. Code and data are available at: https://github.com/yuzhimanhua/SEType.

    

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