基于高阶词汇依存的短语结构树重排序模型

在句法分析中,已有研究工作表明,词汇依存信息对短语结构句法分析是有帮助的,但是已有的研究工作都仅局限于使用一阶的词汇依存信息.提出了一种使用高阶词汇依存信息对短语结构树进行重排序的模型,该模型首先为输入句子生成有约束的搜索空间(例如,N-best句法分析树列表或者句法分析森林),然后在约束空间内获取高阶词汇依存特征,并利用这些特征对短语结构候选树进行重排序,最终选择出最优短语结构分析树.在宾州中文树库上的实验结果表明,该模型的最高F1值达到了85.74%,超过了目前在宾州中文树库上的最好结果.另外,在短语结构分析树的基础上生成的依存结构树的准确率也有了大幅提升.     

油气领域科技信息查重技术研究与应用

论文针对油气管道领域科技信息管理中科技项目重复立项的突出问题,研究和分析管道科技项目信息相似的特性指标和要素,通过信息化技术手段,实现相似度检测,为科技立项的高质量提供保障。论文利用领域专业性特点,通过创建领域同义词词林对现有词林进行针对性的补充扩展;通过分析获取句子依存结构信息,并利用依存路径更准确刻画整体语义;在基于知网与词林结合方式计算词汇相似度的基础上,融合句子依存结构信息计算文本相似度。分别在通用文本数据集和专业领域文本数据集上进行了实验,结果表明论文方法在通用文本数据集上达到了78.64%正确率,在专业领域文本数据集上的正确率为71%。该方法应用于油气管道领域科技信息相似度检测,较好地满足了应用要求。     

融合格框架的基于语块的依存树到串日汉统计机器翻译模型

该文提出了一种融合格框架的日汉基于语块的依存树到串统计机器翻译模型。其基本思想是从日语依存分析树获取格框架,在翻译模型的规则抽取及解码中,以日语格框架作为约束条件,指导依存树的句法结构重排,调整日语和汉语的句法结构差异,实现格框架与日汉依存树到串模型的融合。实验结果表明,该文提出的方法可有效改善日汉统计机器翻译的句法结构调序和词汇翻译,同时,还可有效提高日汉统计机器翻译的译文质量。     

基于句法结构约束的模糊限制信息范围检测

模糊限制信息检测用于区分模糊限制信息与事实信息,提高抽取信息的真实性和可靠性。模糊限制信息范围的界定具有依赖于语义和句法结构的特点,是模糊限制信息检测的一个难点。该文提出一种基于句法结构约束的模糊限制信息范围检测方法,基于依存结构树和短语结构树构建决策树,获取句法结构约束集,用于产生句法结构约束特征,并加入到条件随机域模型中进行模糊限制信息范围检测。实验采用CoNLL-2010共享任务数据集,在标准的模糊限制语标注语料上,获得了70.28%的F值,比采用普通的句法结构特征提高了4.22%。     

结合上下文和依存句法信息的中文短文本情感分析

依存句法分析旨在从语言学的角度分析句子的句法结构。现有的研究表明,将这种类似于图结构的数据与图卷积神经网络(Graph Convolutional Network, GCN)进行结合,有助于模型更好地理解文本语义。然而,这些工作在将依存句法信息处理为邻接矩阵时,均忽略了句法依赖标签类型,同时也未考虑与依赖标签相关的单词语义,导致模型无法捕捉到文本中的深层情感特征。针对以上问题,提出了一种结合上下文和依存句法信息的中文短文本情感分析模型(Context and Dependency Syntactic Information, CDSI)。该模型不仅利用双向长短期记忆网络(Bidirectional Long Short-Term Memory, BiLSTM)提取文本的上下文语义,而且引入了一种基于依存关系感知的嵌入表示方法,以针对句法结构挖掘不同依赖路径对情感分类任务的贡献权重,然后利用GCN针对上下文和依存句法信息同时建模,以加强文本表示中的情感特征。基于SWB,NLPCC2014和SMP2020-EWEC数据集进行验证,实验表明CDSI模型能够有效融合语句中的语义以及句法结构信息...     

基于语义依存的中文本体非分类关系抽取方法

为了解决中文本体非分类关系抽取问题,提出了基于语义依存分析的非分类关系抽取方法.利用语义角色标注和依存语法分析思想,分析得到了文本句子的语义依存结构,提取其中具有语义依存关系的动词框架,通过计算语义相似度,发现了动词框架中概念间的非分类关系和关系名称.实验结果表明该方法能够有效地实现非分类关系的抽取和关系的语义标注.     

歧义结构理解中的依存距离最小化倾向

用依存句法分析汉语歧义结构发现人脑在句法加工时倾向选择最小化依存距离的句法结构。该发现从依存理论角度解释了以往依照短语结构句法分析潜在歧义结构“VP+N1+的+N2”无法说明心理学实验结果的原因,找到了歧义结构实时阅读过程中倾向选择特定句法结构的语言学依据。最小化依存距离的认知机制是降低言语工作记忆成本的有效方法,是言语理解过程中的重要机制之一。     

基于HNC理论和依存句法的句子相似度计算

句子相似度计算是自然语言处理的重要研究内容。运用自然语言处理的概念层次网络（HNC）理论和依存句法理论提出一种句子相似度的计算方法。该方法认为句子的相似度是由词语的语义相似度和句法结构相似度共同决定的,利用HNC理论词汇层面联想的概念表述体系来计算词语之间的相似度,利用依存句法理论来获取句子中词语的词语搭配和构成特征,与现有典型的句子相似度算法和人工判断进行了比较。实验结果表明,该方法能够较好地反应句子之间的语义差别,是一种可行有效的方法。     

融合深度BiGRU与全局图卷积的方面级情感分析模型

现有基于深度学习的方面级情感分析模型需要考虑如何提取深层次的语义信息,其次通过依存树提取句法结构时可能存在信息丢失与数据稀疏问题.针对以上问题,本文提出了基于深度双向门控循环单元与全局双向图卷积网络的神经网络模型(DBG-GBGCN).该模型通过深度双向门控循环单元捕获深层次的语义特征,得到上下文的隐层表示.然后将依存树的邻接矩阵转变为带有全局句法信息的全局矩阵,将此矩阵与上下文的隐层表示一起输入至双向图卷积网络进行特征融合,最后经过掩码层和注意力层得到一个包含深层语义特征与句法结构信息结合的分类特征.实验结果证明,该模型在5个公开数据集上的准确率与F1值均比对比模型有着一定的提升.     

汉语语义依存分析

语义依存分析建立在依存理论基础上,是一种深层的语义分析理论.同时融合了句子的依存结构和语义信息,更好地表达了句子的结构与隐含信息.在许多高层次的研究和应用上,语义依存分析都大有用武之地.语义依存分析主要面临两方面的难题,一是语义体系的确定,其次是自动语义依存分析算法.将重点从语义体系的确定以及自动语义依存分析算法的角度上对语义依存分析进行系统的介绍.     

SyMSS: A syntax-based measure for short-text semantic similarity

Sentence and short-text semantic similarity measures are becoming an important part of many natural language processing tasks, such as text summarization and conversational agents. This paper presents SyMSS, a new method for computing short-text and sentence semantic similarity. The method is based on the notion that the meaning of a sentence is made up of not only the meanings of its individual words, but also the structural way the words are combined. Thus, SyMSS captures and combines syntactic and semantic information to compute the semantic similarity of two sentences. Semantic information is obtained from a lexical database. Syntactic information is obtained through a deep parsing process that finds the phrases in each sentence. With this information, the proposed method measures the semantic similarity between concepts that play the same syntactic role. Psychological plausibility is added to the method by using previous findings about how humans weight different syntactic roles when computing semantic similarity. The results show that SyMSS outperforms state-of-the-art methods in terms of rank correlation with human intuition, thus proving the importance of syntactic information in sentence semantic similarity computation.     

基于最大熵模型的观点句主观关系提取

提出一种提取中文观点句中评价对象和评价词主观匹配关系的方法。分析观点句中评价词和评价对象的词性、词语位置,通过句法分析获取语义特征,将2类特征应用于最大熵模型,提取观点句的主观关系。实验结果证明,与取距离评价词语最近的词作为评价对象的Baseline方法相比,该方法大幅度提高了准确率和F测试值。     

Elementary Dependency Trees for Identifying Corpus-Specific Semantic Classes

Elementary dependency relationships between words within parse trees produced by robust analyzers on a corpus help automate the discovery of semantic classes relevant for the underlying domain. We introduce two methods for extracting elementary syntactic dependencies from normalized parse trees. The groupings which are obtained help identify coarse-grain semantic categories and isolate lexical idiosyncrasies belonging to a specific sublanguage. A comparison shows a satisfactory overlapping with an existing nomenclature for medical language processing. This symbolic approach is efficient on medium size corpora which resist to statistical clustering methods but seems more appropriate for specialized texts. This revised version was published online in July 2006 with corrections to the Cover Date.     

Elementary Dependency Trees for Identifying Corpus-Specific Semantic Classes

Elementary dependency relationships between words within parse trees produced by robust analyzers on a corpus help automate the discovery of semantic classes relevant for the underlying domain. We introduce two methods for extracting elementary syntactic dependencies from normalized parse trees. The groupings which are obtained help identify coarse-grain semantic categories and isolate lexical idiosyncrasies belonging to a specific sublanguage. A comparison shows a satisfactory overlapping with an existing nomenclature for medical language processing. This symbolic approach is efficient on medium size corpora which resist to statistical clustering methods but seems more appropriate for specialized texts. This revised version was published online in July 2006 with corrections to the Cover Date.     

用于关系抽取的注意力图长短时记忆神经网络

关系抽取是信息获取中一项关键技术.句子结构树能够捕获单词之间的长距离依赖关系,已被广泛用于关系抽取任务中.但是,现有方法存在过度依赖句子结构树本身信息而忽略外部信息的不足.本文提出一种新型的图神经网络模型,即注意力图长短时记忆神经网络(attention graph long short term memory neu...     

Secondary predication and the lexical representation of verbs

In this paper it is assumed that syntactic structure is projected from the lexicon. The lexical representation, which encodes the linguistically relevant aspects of the meanings of words, thus determines and constrains the syntax. Therefore, if semantic analysis of syntactic structures is to be possible, it is necessary to determine the content and structure of lexical semantic representations. The paper argues for a certain form of lexical representation by presenting the problem of a particular non-standard structure, the verb phrase of the form V-NP-Adj corresponding to various constructions of secondary predication in English. It is demonstrated that the solution to the semantic analysis of this structure lies in the meaning of the structure's predicators, in particular the lexical semantic representation of the verb. Verbs are classified according to the configuration of the lexical semantic representations, whether basic or derived. It is these specific configurations that restrict the possibilities of secondary predication. Given the class of a verb, its relation to the secondary predicate is predictable; and the correct interpretation of the V-NP-Adj string is therefore possible.This work is based on papers presented to the 1988 meetings of the Canadian Linguistic Association and the Brandeis Workshop on Theoretical and Computational Issues in Lexical Semantics. I am grateful to the audiences at these two meetings for comments, and to Anna-Maria di Sciullo, Diane Massam, Yves Roberge and James Pustejovsky for helpful discussion. I also thank SSHRC for funding the research of which this work forms part.     

基于注意力网络的语体多元特征挖掘

针对大规模语料中不同语体的特征难以挖掘、需要大量专业知识和人力的问题，提出了一种自动挖掘能区分不同语体的特征的方法。首先，将语体表示成词、词类、标点符号、它们的2元、句法结构及多种组合特征；然后，使用注意力机制和多层感知机（MLP）的组合模型（如注意力网络）把语体分类成小说、新闻和课本，并在过程中自动地提取出能够帮助区分语体的重要特征；最后，通过对这些特征的进一步分析，可以得到不同语体的特点及一些语言学结论。实验结果显示，小说、新闻和课本在词、主题词、词的依存关系、词类、标点符号和句法结构都有显著的差异，进一步表明了人们在使用语言时因交际对象、目的、内容和环境的不同，对词汇、词类、标点和句法的运用上会自然地呈现出某种不同。     

Compositionality and lexical alignment of multi-word terms

The automatic compilation of bilingual lists of terms from specialized comparable corpora using lexical alignment has been successful for single-word terms (SWTs), but remains disappointing for multi-word terms (MWTs). The low frequency and the variability of the syntactic structures of MWTs in the source and the target languages are the main reported problems. This paper defines a general framework dedicated to the lexical alignment of MWTs from comparable corpora that includes a compositional translation process and the standard lexical context analysis. The compositional method which is based on the translation of lexical items being restrictive, we introduce an extended compositional method that bridges the gap between MWTs of different syntactic structures through morphological links. We experimented with the two compositional methods for the French–Japanese alignment task. The results show a significant improvement for the translation of MWTs and advocate further morphological analysis in lexical alignment.