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afwBerlin's Projects

awesome-python icon awesome-python

A curated list of awesome Python frameworks, libraries, software and resources

computerscience icon computerscience

Free tech resources for faculty, students, and dev advocates for use in computer science courses, workshops, and hackathons.

docsify icon docsify

🃏 A magical documentation site generator.

extensionpack icon extensionpack

Public PowerShell script gallery to extend the functionality of ScriptRunner.

hyperscan icon hyperscan

High-performance regular expression matching library

openkp icon openkp

Automatically extracting keyphrases that are salient to the document meanings is an essential step to semantic document understanding. An effective keyphrase extraction (KPE) system can benefit a wide range of natural language processing and information retrieval tasks. Recent neural methods formulate the task as a document-to-keyphrase sequence-to-sequence task. These seq2seq learning models have shown promising results compared to previous KPE systems The recent progress in neural KPE is mostly observed in documents originating from the scientific domain. In real-world scenarios, most potential applications of KPE deal with diverse documents originating from sparse sources. These documents are unlikely to include the structure, prose and be as well written as scientific papers. They often include a much diverse document structure and reside in various domains whose contents target much wider audiences than scientists. To encourage the research community to develop a powerful neural model with key phrase extraction on open domains we have created OpenKP: a dataset of over 150,000 documents with the most relevant keyphrases generated by expert annotation.

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