中文术语抽取若干问题研究
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摘要
术语,作为专业知识的集中载体,它的创建、普及和消亡,动态展现了一个学科的发展、演变历程。专业术语数据库作为一种知识源,能够为各类研究人员便捷地获取专业知识提供重要支持。术语自动抽取是构建专业术语库的关键技术,同时也是自然语言处理领域中的一项基本课题,为包括机器翻译、文档摘要、信息检索、文本分类、词典编纂等在内的诸多自然语言研究起到支撑作用。
本文突破了名词短语的限制,接纳更多非名词性结构的专业术语,拓宽了语言规则。结合实证分析和机器学习策略,分别从结构完整性、领域相关度和词语搭配三个方面展开研究,主要工作包括：
1.以词为最小语言单位,构建一个涵盖四万余条计算机专业术语的数据库。针对不同长度术语的分布特性,结合机器学习方法从多角度提炼出术语结构的词法特征。丰富语言规则的同时,扩大了规则覆盖面,提高术语抽取的召回率。
2.针对单词型术语结构简单,边界清晰的特征,提出一种基于模糊聚类的识别算法。将术语识别过程成功转化为二值分类任务,无需专业辞典和诸多语料库的支持,实现单词型术语的自动聚合标注。
3.不同于已有方法中采用单一父串到多子串的归并策略,本文从单一子串与多父串之间的逆向映射关系出发,提出了一种基于独立性统计的子串归并算法,以此判断候选术语的结构完整性。实验表明,在O(n)的时间内,该算法不仅可以删除普通子串,还能有效过滤由公共子串造成的干扰,将候选术语集有效缩减29.44%。
4.以非名词性词语的构词能力为研究对象,提出了“词汇活跃度”(Word Active Degree, WAD)的概念。同时结合词汇间粘合度,分析短语内部词语的搭配特征,过滤掉非良性搭配和局部成分过分活跃的短语。实验表明,采用WAD作为词语搭配评判标准,对由动宾短语和介词短语引发的错误有较强的识别能力,正确率高达99.97%。
5.根据术语和非术语在语料库中变化趋势的分布差异性,结合局部及全局特征,提出了一种基于分布变化特征的领域相关度计算方法。实验表明,该方法不仅能够大幅降低计算复杂度,还可以显著提升低频术语和基础术语在输出结果中的排序。
As the carriers of domain knowledge, the creation, popularization and extinction of terms show the dynamical development and evolution process of a subject. Taking the part of knowledge source, domain term databases could offer a convenient and quick manner to acquire professional knowledge. Automatic term extraction is not only one of the critical technology of domain term database construction, but also a basic topic in nature language processing, and provides support with many other researches, such as machine translation, information retrieval, automatic abstrcting, text classification, dictionary compilation and so on.
In this dissertation, the author makes a breakthrough at the restriction of noun phrases, accepts more different structures, and widens the linguistical rules. Combining with empirical analysis and machine learning strategies, the researches focus on term structure integrality, domain relevance and collocation, and get the following achievement:
Firstly, a computer term database containing more than 40,000 items is constructed, which takes word as the minimal linguistical unit. Based on the distribution features of terms with different length, some morphological rules of term structure are concluded by machine learning methods. As the result of enriching the linguistical rules, the coverage of rules is enlarged and the recall is improved.
Secondly, a single-word term recognition approach based on fuzzy clustering is proposed, according to the simple structure and the unambiguous boundary. The recognition process is turned into classification task. Dispensing with specific dictionaries and many other corpora, the single-word terms could be automatically tagged by the clustering algorithm.
Thirdly, a substring reduction algorithm based on the independency statistic is proposed to estimate the structure integrality of candidates. Unlike the current methods adopting the mapping relations from single parent-string to many substring, this algorithm attempts to catch the links between a string with its parent-strings. Validated by the experiments,29.44%of the candidates are filtering in time. Besides of the ordinary fragmentary substrings, the common substring noisy can also be recognized.
Fourthly, a conception of word active degree is proposed to evaluate the collocation ability of non-noun words. Integrated with cohesion between words, the parameter could measure the collocation appropriateness of the words in a phrase and delete the ill-collocation or the phrases with excessively active segment. Validated by the experiments, the WAD has a strong ability to distinguish the errors cased by verb-object phrases and preposition phrases, and the precision reaches 99.97%.
Finally, according to the distribution diversity between terms and non-terms, a domain relevance measure based on the local distribution variety feature is proposed, combining with the whole coverage feature. Validated by the experiments, this method could efficiently improve the rank of low-frequency term and base term with low computational complexity.
本文突破了名词短语的限制,接纳更多非名词性结构的专业术语,拓宽了语言规则。结合实证分析和机器学习策略,分别从结构完整性、领域相关度和词语搭配三个方面展开研究,主要工作包括：
1.以词为最小语言单位,构建一个涵盖四万余条计算机专业术语的数据库。针对不同长度术语的分布特性,结合机器学习方法从多角度提炼出术语结构的词法特征。丰富语言规则的同时,扩大了规则覆盖面,提高术语抽取的召回率。
2.针对单词型术语结构简单,边界清晰的特征,提出一种基于模糊聚类的识别算法。将术语识别过程成功转化为二值分类任务,无需专业辞典和诸多语料库的支持,实现单词型术语的自动聚合标注。
3.不同于已有方法中采用单一父串到多子串的归并策略,本文从单一子串与多父串之间的逆向映射关系出发,提出了一种基于独立性统计的子串归并算法,以此判断候选术语的结构完整性。实验表明,在O(n)的时间内,该算法不仅可以删除普通子串,还能有效过滤由公共子串造成的干扰,将候选术语集有效缩减29.44%。
4.以非名词性词语的构词能力为研究对象,提出了“词汇活跃度”(Word Active Degree, WAD)的概念。同时结合词汇间粘合度,分析短语内部词语的搭配特征,过滤掉非良性搭配和局部成分过分活跃的短语。实验表明,采用WAD作为词语搭配评判标准,对由动宾短语和介词短语引发的错误有较强的识别能力,正确率高达99.97%。
5.根据术语和非术语在语料库中变化趋势的分布差异性,结合局部及全局特征,提出了一种基于分布变化特征的领域相关度计算方法。实验表明,该方法不仅能够大幅降低计算复杂度,还可以显著提升低频术语和基础术语在输出结果中的排序。
As the carriers of domain knowledge, the creation, popularization and extinction of terms show the dynamical development and evolution process of a subject. Taking the part of knowledge source, domain term databases could offer a convenient and quick manner to acquire professional knowledge. Automatic term extraction is not only one of the critical technology of domain term database construction, but also a basic topic in nature language processing, and provides support with many other researches, such as machine translation, information retrieval, automatic abstrcting, text classification, dictionary compilation and so on.
In this dissertation, the author makes a breakthrough at the restriction of noun phrases, accepts more different structures, and widens the linguistical rules. Combining with empirical analysis and machine learning strategies, the researches focus on term structure integrality, domain relevance and collocation, and get the following achievement:
Firstly, a computer term database containing more than 40,000 items is constructed, which takes word as the minimal linguistical unit. Based on the distribution features of terms with different length, some morphological rules of term structure are concluded by machine learning methods. As the result of enriching the linguistical rules, the coverage of rules is enlarged and the recall is improved.
Secondly, a single-word term recognition approach based on fuzzy clustering is proposed, according to the simple structure and the unambiguous boundary. The recognition process is turned into classification task. Dispensing with specific dictionaries and many other corpora, the single-word terms could be automatically tagged by the clustering algorithm.
Thirdly, a substring reduction algorithm based on the independency statistic is proposed to estimate the structure integrality of candidates. Unlike the current methods adopting the mapping relations from single parent-string to many substring, this algorithm attempts to catch the links between a string with its parent-strings. Validated by the experiments,29.44%of the candidates are filtering in time. Besides of the ordinary fragmentary substrings, the common substring noisy can also be recognized.
Fourthly, a conception of word active degree is proposed to evaluate the collocation ability of non-noun words. Integrated with cohesion between words, the parameter could measure the collocation appropriateness of the words in a phrase and delete the ill-collocation or the phrases with excessively active segment. Validated by the experiments, the WAD has a strong ability to distinguish the errors cased by verb-object phrases and preposition phrases, and the precision reaches 99.97%.
Finally, according to the distribution diversity between terms and non-terms, a domain relevance measure based on the local distribution variety feature is proposed, combining with the whole coverage feature. Validated by the experiments, this method could efficiently improve the rank of low-frequency term and base term with low computational complexity.
引文
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