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Chinese-Synonyms|中文同义词数据集|语料库数据集
收藏github2021-11-21 更新2024-05-31 收录
下载链接：
https://github.com/jaaack-wang/Chinese-Synonyms
下载链接
链接失效反馈资源简介：
本同义词语料库内容基于多个权威来源，如在线成语词典、哈工大同义词词林扩展版等，通过严格的筛选和处理，形成了包含18,589条同义词语例的语料库，以及两个扩展版本，分别包含52,157条同义词语例。
This synonym corpus is derived from multiple authoritative sources, such as online idiom dictionaries and the extended version of the Harbin Institute of Technology Synonym Forest. Through rigorous screening and processing, it has formed a corpus containing 18,589 synonym examples, along with two extended versions, each containing 52,157 synonym examples.
创建时间：
2021-11-02
原始信息汇总
数据集概述
数据来源
- 在线成语词典
- 哈工大同义词词林扩展版
- 汉语大辞典的近义词大全
- 在线成语词典
- 在线近义词查询
数据处理
- 对于哈工大同义词词林，只取至少“成对出现”的同义词，不取单独的“同义词”。
- 当一个词在不同来源有不同同义词时，取这些同义词的并集。
- 哈工大同义词词林中，每组同义词的第一个词定义为目标词，其余词汇为目标词的同义词。
数据集内容
- synonyms.json：包含18,589条同义词语例，以字典形式保存。
- synonyms_expanded_narrow.json：包含52,157条同义词语例，每个目标词的同义词被视为另一个目标词，原本的目标词则变为其同义词的同义词。
- synonyms_expanded_broad.json：同样包含52,157条同义词语例，预设同义词间的广泛联系，认定目标词的同义词之间也存在同义词联系。
数据集特点
- Narrow扩展版：对同义词的定义较保守，更可靠，但可能无法联系一些潜在的同义词对。
- Broad扩展版：尽可能广泛地组建同义词网络，但可能包含一些不成立的同义词对。
应用建议
- 针对一词多义现象，建议使用统计学或机器学习的方式构建语言模型，以利用语言上下文进行词义消歧。
AI搜集汇总
数据集介绍
构建方式
Chinese-Synonyms数据集的构建基于多个权威来源，包括在线成语词典、哈工大同义词词林扩展版、汉语大辞典的近义词大全等。特别地，对于哈工大同义词词林，仅选取成对出现的同义词，并剔除孤立的词汇。在处理不同来源的同义词时，采用并集策略以确保数据的全面性。此外，将每组同义词中的首个词汇定义为目标词，其余词汇作为其同义词，最终形成包含18,589条同义词语例的字典形式数据集。
使用方法
使用Chinese-Synonyms数据集时，用户可以直接访问synonyms.json文件获取基础的同义词对，或选择synonyms_expanded_narrow.json和synonyms_expanded_broad.json以获取扩展的同义词网络。对于多义词的处理，建议结合上下文信息或采用统计学或机器学习模型进行语义消歧，以提高同义词检索的准确性。数据集适用于自然语言处理、文本分析和语言模型构建等多种应用场景。
背景与挑战
背景概述
Chinese-Synonyms数据集是由主要研究人员基于多个权威来源构建的同义词语料库，包括在线成语词典、哈工大同义词词林扩展版、汉语大辞典的近义词大全等。该数据集的核心研究问题是如何系统地收集和整理中文同义词，以支持自然语言处理和语言学研究。通过整合不同来源的数据，研究人员构建了一个包含18,589条同义词对的初始数据集，并进一步扩展为两个版本，分别包含52,157条同义词对。这些数据集的创建不仅丰富了中文同义词资源，还为语言模型和语义分析提供了宝贵的数据支持。
当前挑战
Chinese-Synonyms数据集在构建过程中面临多项挑战。首先，同义词的定义和识别在不同语境下可能存在差异，尤其是在处理一词多义现象时，如何准确匹配同义词成为一个难题。其次，数据集的扩展版本（narrow和broad）在处理同义词关系时，分别采取了保守和广泛的方法，但这两种方法在实际应用中可能引入不准确或冗余的同义词对。此外，数据集的构建依赖于多个来源，如何有效整合和验证这些来源的数据，确保数据的一致性和可靠性，也是一项重要挑战。
常用场景
经典使用场景
Chinese-Synonyms数据集在自然语言处理领域中，常用于构建中文同义词词典，支持文本相似度计算、语义检索以及词义消歧等任务。通过该数据集，研究者能够构建高效的同义词扩展模型，提升中文文本处理系统的语义理解能力。
解决学术问题
该数据集解决了中文自然语言处理中同义词识别与扩展的难题，尤其是在多义词的语境下，如何准确匹配同义词的问题。通过提供丰富的同义词对，该数据集为语义相似度计算、词义消歧等研究提供了坚实的基础，推动了中文信息处理技术的发展。
实际应用
在实际应用中，Chinese-Synonyms数据集被广泛用于搜索引擎优化、智能问答系统、文本摘要生成等领域。通过引入同义词扩展，系统能够更精准地理解用户查询意图，提升检索结果的相关性和用户体验。
数据集最近研究
最新研究方向
在自然语言处理领域，中文同义词数据集的研究正逐步深入，尤其是在多义词的同义词识别与语境排歧方面。随着深度学习技术的进步，研究者们开始利用统计学和机器学习模型，如BERT和GPT系列，来构建更为精准的语言模型，以解决一词多义带来的同义词识别难题。此外，同义词网络的构建与扩展也成为研究热点，尤其是在如何平衡同义词网络的广度与精度方面，研究者们提出了多种扩展策略，如窄扩展与广扩展，以期在不同应用场景中实现更高效的同义词匹配。这些研究不仅推动了中文自然语言处理技术的发展，也为跨语言语义理解提供了新的思路。
以上内容由AI搜集并总结生成
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