探索开放领域关键词抽取的未来：OpenKP

探索开放领域关键词抽取的未来：OpenKP

OpenKPAutomatically 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.项目地址:https://gitcode.com/gh_mirrors/op/OpenKP

在自然语言处理的世界里，理解文档的关键信息是构建智能系统的重要基石。为此，我们向您推荐一个创新的开源项目——OpenKP（OpenKeyPhrase）。这个大规模的开放域关键词提取数据集旨在推动这一领域的研究和应用，将帮助您更好地理解并提炼网页内容的核心要点。

项目介绍

OpenKP是一个专门针对开放网络环境设计的大型关键词抽取消息数据集。它包含了148,124个真实世界的网页，每个网页都由专业人员人工标注了最相关的1到3个关键词。这项工作是基于EMNLP-IJCNLP 2019上发表的论文《开放域网络关键词抽取：超越语言建模》展开的，并与MSMARCO项目家族紧密关联，为Bing等搜索引擎的核心文档理解提供了支持。

技术分析

关键词抽取被定义为识别能够概括文档主题的1-n个短语的任务。在OpenKP中，重点放在了通用网络领域，涵盖各种真实的网页内容。数据集经过专家评审，确保了标注的质量和一致性。通过收集多样化的网页URL，再由专业的评委进行浏览和标注，OpenKP提供了一个既广泛又真实的数据样本。

应用场景

OpenKP适用于多个实际场景，包括：

1. 文档检索和推荐: 提取关键信息以快速定位相关文档。
2. 搜索引擎优化: 帮助确定网页排名和提高搜索质量。
3. 新闻摘要和自动化报告: 快速生成文档概要。
4. 知识图谱构建: 作为构建结构化知识的基础。

项目特点

• 大规模: 涵盖148,124个真实世界网页，具有广泛的领域多样性。
• 高质量标注: 由专业人员进行人工标注，确保了关键词的相关性。
• 无语言生成需求: 注释者仅复制文本，无需生成新的语言表达。
• 全面覆盖: 数据来自Bing索引和MSMARCO问答数据集，反映真实搜索情境。

通过参与OpenKP项目，您不仅能得到对自然语言处理深入的理解，还能利用这些数据提升您的算法性能，解决实际问题。无论是研究人员还是开发者，OpenKP都是一个不可多得的学习和实践平台，欢迎加入，共同推动关键词抽取技术的发展。

OpenKPAutomatically 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.项目地址:https://gitcode.com/gh_mirrors/op/OpenKP