lunnada Goto Github PK

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lunnada's Projects

NLP 相关的一些文档、论文及代码, 包括主题模型(Topic Model)、词向量(Word Embedding)、命名实体识别(Named Entity Recognition)、文本分类(Text Classificatin)、文本生成(Text Generation)、文本相似性(Text Similarity)计算、机器翻译(Machine Translation)等，涉及到各种与nlp相关的算法，基于tensorflow 2.0。

nlp_ability

总结梳理自然语言处理工程师(NLP)需要积累的各方面知识，包括面试题，各种基础知识，工程能力等等，提升核心竞争力

nlp_course

YSDA course in Natural Language Processing

nlp_unsupervised_cluster_labeling

An NLP approach to cluster and label transcripts with minimum human intervention. TED talk transcript use.

notepad-plus-plus

Notepad++ official repository

onelinerhub

2k code solutions with clear explanation @ onelinerhub.com

online-hdp

Online inference for the Hierarchical Dirichlet Process. Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.

onlinejudge

open source online judge based on Vue, Django and Docker. | 青岛大学开源 Online Judge | QQ群 496710125 | [email protected]

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.