OpenKP/URLS/evalURLs.tsv at master · microsoft/OpenKP · GitHub

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. - OpenKP/URLS/evalURLs.tsv at master · microsoft/OpenKP

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TTS/TTS/tts/layers/xtts/tokenizer.py, line 180, in expand_abbreviations_multilingual for regex, replacement in _abbreviations[lang]: KeyError: 'zh-cn'[Bug] · Issue #3189 · coqui-ai/TTS · GitHub

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