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1. PubMed-OA-Extraction-dataset

   https://doi.org/10.5281/zenodo.6330817  

   Sheng, Jiasheng (January 2022, Zenodo)

   This is the train-test-validation dataset for pubmed open-access articles keyphrase extraction task. The small_* file contains the all articles that have 5to 25 extractive keyphrases (keyphrase in the article that is inside the abstract of the article).</p> 

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2. Lightweight multi-language syntax transformation with parser parser combinators

   https://doi.org/10.1145/3314221.3314589  

   van Tonder, Rijnard; Le Goues, Claire (July 2019, Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation)

   Automatically transforming programs is hard, yet critical for automated program refactoring, rewriting, and repair. Multi-language syntax transformation is especially hard due to heterogeneous representations in syntax, parse trees, and abstract syntax trees (ASTs). Our insight is that the problem can be decomposed such that (1) a common grammar expresses the central context-free language (CFL) properties shared by many contemporary languages and (2) open extension points in the grammar allow customizing syntax (e.g., for balanced delimiters) and hooks in smaller parsers to handle language-specific syntax (e.g., for comments). Our key contribution operationalizes this decomposition using a Parser Parser combinator (PPC), a mechanism that generates parsers for matching syntactic fragments in source code by parsing declarative user-supplied templates. This allows our approach to detach from translating input programs to any particular abstract syntax tree representation, and lifts syntax rewriting to a modularly-defined parsing problem. A notable effect is that we skirt the complexity and burden of defining additional translation layers between concrete user input templates and an underlying abstract syntax representation. We demonstrate that these ideas admit efficient and declarative rewrite templates across 12 languages, and validate effectiveness of our approach by producing correct and desirable lightweight transformations on popular real-world projects (over 50 syntactic changes produced by our approach have been merged into 40+). Our declarative rewrite patterns require an order of magnitude less code compared to analog implementations in existing, language-specific tools. 

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3. A biologically plausible parser

   https://doi.org/10.1162/tacl_a_00432  

   Mitropolsky, Daniel; Collins, Michael J.; Papadimitriou, Christos H. (December 2021, Transactions of the Association for Computational Linguistics)
4. A biologically plausible parser

   Daniel Mitropolsky, Michael Collins (December 2021, Transactions of the Association for Computational Linguistics)

   We describe a parser of English effectuated by biologically plausible neurons and synapses, and implemented through the Assembly Calculus, a recently proposed computational framework for cognitive function. We demonstrate that this device is capable of correctly parsing reasonably nontrivial sentences.1 While our experiments entail rather simple sentences in English, our results suggest that the parser can be extended beyond what we have implemented, to several directions encompassing much of language. For example, we present a simple Russian version of the parser, and discuss how to handle recursion, embedding, and polysemy. 

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5. PubMed Author-assigned Keyword Extraction (PubMedAKE) Benchmark

   https://doi.org/10.1145/3511808.3557675  

   Sheng, Jiasheng; Gero, Zelalem; Ho, Joyce C. (October 2022, CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management)

   With the ever-increasing abundance of biomedical articles, improving the accuracy of keyword search results becomes crucial for ensuring reproducible research. However, keyword extraction for biomedical articles is hard due to the existence of obscure keywords and the lack of a comprehensive benchmark. PubMedAKE is an author-assigned keyword extraction dataset that contains the title, abstract, and keywords of over 843,269 articles from the PubMed open access subset database. This dataset, publicly available on Zenodo, is the largest keyword extraction benchmark with sufficient samples to train neural networks. Experimental results using state-of-the-art baseline methods illustrate the need for developing automatic keyword extraction methods for biomedical literature. 

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