A PHRASE GRAMMAR-BASED CONCEPTUAL INDEXING PARADIGM

ABSTRACT

Present day information retrieval systems largely ignore the issues of lexical and compositional semantics, and rely mainly on some statistical measures for choosing or evolving an indexing scheme. This has been the reason for the decreasing precision in their responses, given an exponentially increasing number of Web pages. The work reported in this paper addresses this issue from a linguistic point of view. We show that the detection of domain-specific phrases can capture the task-specific semantics of documents. We introduce the notion of n*-gram formalism to characterize the domain-specific phrases and their variants, taking a few sample domains. A method to construct a phrase grammar from a small set of documents is proposed. A method of conceptual indexing based on the phrase grammar has also been proposed. In order to demonstrate the effectiveness of the proposed method, we have designed a versatile system that can perform concept-based retrieval, in addition to several document-processing tasks, such as text classification, extraction-based summarization, context tracking, and semantic tagging. Collectively, the system can address the semantic content of documents. Considering the fact that an average user prefers highly relevant results in the top-ranked subset to an exhaustively retrieved set, it is shown that the proposed system performs better in that it retrieves documents that are more conceptually relevant than those retrieved by Google, and at 95% confidence level.     

混合词汇特征和LDA的语义相关度计算方法

文本语义相关度计算在自然语言处理、语义信息检索等方面起着重要作用,以Wikipedia为知识库,基于词汇特征的ESA（Explicit Semantic Analysis）因简单有效的特点在这些领域中受到学术界的广泛关注和应用。然而其语义相关度计算因为有大量冗余概念的参与变成了一种高维度、低效率的计算方式,同时也忽略了文本所属主题因素对语义相关度计算的作用。引入LDA（Latent Dirichlet Allocation）主题模型,对ESA返回的相关度较高的概念转换为模型的主题概率向量,从而达到降低维度和提高效率的目的;将JSD距离（Jensen-Shannon Divergence）替换余弦距离的测量方法,使得文本语义相关度计算更加合理和有效。最后对不同层次的数据集进行算法的测试评估,结果表明混合词汇特征和主题模型的语义相关度计算方法的皮尔逊相关系数比ESA和LDA分别高出3%和9%以上。     

Domain structure-based transfer learning for cross-domain word representation

Cross-domain word representation aims to learn high-quality semantic representations in an under-resourced domain by leveraging information in a resourceful domain. However, most existing methods mainly transfer the semantics of common words across domains, ignoring the semantic relations among domain-specific words. In this paper, we propose a domain structure-based transfer learning method to learn cross-domain representations by leveraging the relations among domain-specific words. To accomplish this, we first construct a semantic graph to capture the latent domain structure using domain-specific co-occurrence information. Then, in the domain adaptation process, beyond domain alignment, we employ Laplacian Eigenmaps to ensure the domain structure is consistently distributed in the learned embedding space. As such, the learned cross-domain word representations not only capture shared semantics across domains, but also maintain the latent domain structure. We performed extensive experiments on two tasks, namely sentiment analysis and query expansion. The experiment results show the effectiveness of our method for tasks in under-resourced domains.     

Emphasizing Essential Words for Sentiment Classification Based on Recurrent Neural Networks

With the explosion of online communication and publication, texts become obtainable via forums, chat messages, blogs, book reviews and movie reviews. Usually, these texts are much short and noisy without sufficient statistical signals and enough information for a good semantic analysis. Traditional natural language processing methods such as Bow-of-Word (BOW) based probabilistic latent semantic models fail to achieve high performance due to the short text environment. Recent researches have focused on the correlations between words, i.e., term dependencies, which could be helpful for mining latent semantics hidden in short texts and help people to understand them. Long short-term memory (LSTM) network can capture term dependencies and is able to remember the information for long periods of time. LSTM has been widely used and has obtained promising results in variants of problems of understanding latent semantics of texts. At the same time, by analyzing the texts, we find that a number of keywords contribute greatly to the semantics of the texts. In this paper, we establish a keyword vocabulary and propose an LSTM-based model that is sensitive to the words in the vocabulary; hence, the keywords leverage the semantics of the full document. The proposed model is evaluated in a short-text sentiment analysis task on two datasets: IMDB and SemEval-2016, respectively. Experimental results demonstrate that our model outperforms the baseline LSTM by 1%~2% in terms of accuracy and is effective with significant performance enhancement over several non-recurrent neural network latent semantic models (especially in dealing with short texts). We also incorporate the idea into a variant of LSTM named the gated recurrent unit (GRU) model and achieve good performance, which proves that our method is general enough to improve different deep learning models.     

基于中文维基百科链接结构与分类体系的语义相关度计算

自然语言词汇的语义相关度的计算需要获取大量的背景知识,而维基百科是当前规模最大的百科全书,其不仅是一个规模巨大的语料库,而且还是一个包含了大量人类背景知识和语义关系的知识库,研究表明,其是进行语义计算的理想资源,本文提出了一种将维基百科的链接结构和分类体系相结合计算中文词汇语义相关度的算法,算法只利用了维基百科的链接结构和分类体系,无需进行复杂的文本处理,计算所需的开销较小.在多个人工评测的数据集上的实验结果显示,获得了比单独使用链接结构或分类体系的算法更好的效果,在最好的情况下,Spearman相关系数提高了30.96％.     

使用概念描述的中文短文本分类算法

针对短文本特征较少而导致使用传统文本分类算法进行分类效果并不理想的问题,提出了一种使用了概念描述的短文本分类算法,该方法首先构建出全局的语义概念词表;然后,使用概念词表分别对预测短文本和训练短文本概念化描述,使得预测短文本在训练集中找出拥有相似概念描述的训练短文本组合成预测长文本,同时将训练集内部的短文本也进行自组合形成训练长文本;最后,再使用传统的长文本分类算法进行分类。实验证明,该方法能够有效挖掘短文本内部隐含的语义信息,充分对短文本进行语义扩展,提高了短文本分类的准确度。     

Extracting semantic relations to enrich domain ontologies

Domain ontologies facilitate the organization, sharing and reuse of domain knowledge, and enable various vertical domain applications to operate successfully. Most methods for automatically constructing ontologies focus on taxonomic relations, such as is-kind-of and is-part-of relations. However, much of the domain-specific semantics is ignored. This work proposes a semi-unsupervised approach for extracting semantic relations from domain-specific text documents. The approach effectively utilizes text mining and existing taxonomic relations in domain ontologies to discover candidate keywords that can represent semantic relations. A preliminary experiment on the natural science domain (Taiwan K9 education) indicates that the proposed method yields valuable recommendations. This work enriches domain ontologies by adding distilled semantics.     

Semantic integration of enterprise information systems using meta-metadata ontology

This paper proposes a non-domain-specific metadata ontology as a core component in a semantic model-based document management system (DMS), a potential contender towards the enterprise information systems of the next generation. What we developed is the core semantic component of an ontology-driven DMS, providing a robust semantic base for describing documents’ metadata. We also enabled semantic services such as automated semantic translation of metadata from one domain to another. The core semantic base consists of three semantic layers, each one serving a different view of documents’ metadata. The core semantic component’s base layer represents a non-domain-specific metadata ontology founded on ebRIM specification. The main purpose of this ontology is to serve as a meta-metadata ontology for other domain-specific metadata ontologies. The base semantic layer provides a generic metadata view. For the sake of enabling domain-specific views of documents’ metadata, we implemented two domain-specific metadata ontologies, semantically layered on top of ebRIM, serving domain-specific views of the metadata. In order to enable semantic translation of metadata from one domain to another, we established model-to-model mappings between these semantic layers by introducing SWRL rules. Having the semantic translation of metadata automated not only allows for effortless switching between different metadata views, but also opens the door for automating the process of documents long-term archiving. For the case study, we chose judicial domain as a promising ground for improving the efficiency of the judiciary by introducing the semantics in this field.     

基于带约束语义文法的领域相关自然语言理解方法

开放域问答系统通常可以借助一些数据冗余方法来提高问答准确性,而对于缺乏大规模领域语料的领域相关问答系统来说,准确理解用户的意图成为这类系统的关键。该文首先定义了一种带约束语义文法,与本体等语义资源相结合,可以在词汇级、句法级、语义级对自然语言句子的解析过程进行约束,解决自然语言理解歧义问题;然后给出了一个高效的文法匹配算法,其首先依据定义的各种约束条件预先过滤一些规则,然后依据提出的匹配度计算模型对候选的规则进行排序,找到最佳匹配。为了验证方法的有效性,将方法应用到两个实际的应用领域的信息查询系统。实验结果表明,本系统提出的方法切实有效,系统理解准确率分别达到了82.4%和86.2%,MRR值分别达到了91.6%和93.5%。     

Semantic similarity measures for enhancing information retrieval in folksonomies

Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users’ collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy.     

An evaluative baseline for geo-semantic relatedness and similarity

In geographic information science and semantics, the computation of semantic similarity is widely recognised as key to supporting a vast number of tasks in information integration and retrieval. By contrast, the role of geo-semantic relatedness has been largely ignored. In natural language processing, semantic relatedness is often confused with the more specific semantic similarity. In this article, we discuss a notion of geo-semantic relatedness based on Lehrer’s semantic fields, and we compare it with geo-semantic similarity. We then describe and validate the Geo Relatedness and Similarity Dataset (GeReSiD), a new open dataset designed to evaluate computational measures of geo-semantic relatedness and similarity. This dataset is larger than existing datasets of this kind, and includes 97 geographic terms combined into 50 term pairs rated by 203 human subjects. GeReSiD is available online and can be used as an evaluation baseline to determine empirically to what degree a given computational model approximates geo-semantic relatedness and similarity.     

基于知网的个人微博语义相关度的聚类研究

聚类相关度大的个人微博有助于快速了解博主的专业兴趣和经历,目前的短文本聚类方法缺乏对于语义和句子相关度的充分考虑,提出了一种基于知网的个人微博语义相关度的聚类方法。其要点如下:(1)利用Skip-gram训练大量微博文本生成词汇向量;(2)根据词汇义原进行句内词汇消除歧义;(3)分别计算个人微博之间词汇和句子的相似度并将其综合得到博文相关度;(4)根据博文相关度进行个人微博的聚类。实验表明,相较于层次聚类法、密度聚类法,本文算法的准确度有明显提高。     

进程创建的语义及等价性

针对一个基于共享变量的带有进程创建的命令式语言,用变迁系统描述了它的结构操作语义,并用扩展的状态变迁迹模型定义了它的指称语义,在该模型下,状态变迁被区分为两种不同形式,分别表示发生在原进程和被创建进程中的状态变迁,这样便可以定义适当的语义复合运算,在对命令的指称进行复合时根据变迁类型的不同对变迁迹进行串行或交错连接,恰当地反映了进程的并发运行受创建命令在程序中的相对位置的限制,最后证明了这两个语义     

Explication and semantic querying of enterprise information systems

Many researches show that the ability of independent, heterogeneous enterprises’ information systems to interoperate is related to the challenges of making their semantics explicit and formal, so that the messages are not merely exchanged, but interpreted, without ambiguity. In this paper, we present an approach to overcome those challenges by developing a method for explication of the systems’ implicit semantics. We define and implement the method for the generation of local ontologies, based on the databases of their systems. In addition, we describe an associated method for the translation between semantic and SQL queries, a process in which implicit semantics of the EIS’s databases and explicit semantics of the local ontologies become interrelated. Both methods are demonstrated in the case of creating the local ontology and the semantic querying of OpenERP Enterprise Resource Planning system, for the benefit of the collaborative supply chain planning.     

多层视频语义概念分析与理解

基于统计学理论,提出了一种视频多粒度语义分析的通用方法,使得多层次语义分析与多模式信息融合得到统一.为了对时域内容进行表示,首先提出一种具有时间语义语境约束的关键帧选取策略和注意力选择模型;在基本视觉语义识别后,采用一种多层视觉语义分析框架来抽取视觉语义;然后应用隐马尔可夫模型(HMM)和贝叶斯决策进行音频语义理解;最后用一种具有两层结构的仿生多模式融合方案进行语义信息融合.实验结果表明,该方法能有效融合多模式特征,并提取不同粒度的视频语义.     

Ontology and rule-based natural language processing approach for interpreting textual regulations on underground utility infrastructure

The nation’s massive underground utility infrastructure must comply with a multitude of regulations. The regulatory compliance checking of underground utilities requires an objective and consistent interpretation of the regulations. However, utility regulations contain a variety of domain-specific terms and numerous spatial constraints regarding the location and clearance of underground utilities. It is challenging for the interpreters to understand both the domain and spatial semantics in utility regulations. To address the challenge, this paper adopts an ontology and rule-based Natural Language Processing (NLP) framework to automate the interpretation of utility regulations – the extraction of regulatory information and the subsequent transformation into logic clauses. Two new ontologies have been developed. The urban product ontology (UPO) is domain-specific to model domain concepts and capture domain semantics on top of heterogeneous terminologies in utility regulations. The spatial ontology (SO) consists of two layers of semantics – linguistic spatial expressions and formal spatial relations – for better understanding the spatial language in utility regulations. Pattern-matching rules defined on syntactic features (captured using common NLP techniques) and semantic features (captured using ontologies) were encoded for information extraction. The extracted information elements were then mapped to their semantic correspondences via ontologies and finally transformed into deontic logic (DL) clauses to achieve the semantic and logical formalization. The approach was tested on the spatial configuration-related requirements in utility accommodation policies. Results show it achieves a 98.2% precision and a 94.7% recall in information extraction, a 94.4% precision and a 90.1% recall in semantic formalization, and an 83% accuracy in logical formalization.     

基于词项聚类的文本语义标签抽取研究

本研究主要解决在大量文本数据中 抽取 关键语义信息的问题。文本是自然语言的信息载体,在分析和处理文本信息时,由于目标与方式不同,对文本信息的特征表达方式也各不相同。已有的语义抽取方法往往是针对单篇文本的,忽略了不同文本间的语义联系。为此,文中提出了基于词项聚类的文本语义标签提取方法。该方法以语义抽取为目标,以Hinton的分布式表示假说为文本信息的表达方式,并以最大化语义标签与原文本数据间的语义相似度为目标,使用聚类算法对语义标签进行聚类。实验表明,所提方法由于是基于全体词汇表对语义信息分布进行聚类计算的,因此在语义丰富度和表达能力上相比很多现有方法具有更好的表现。     

神经网络与组合语义在文本相似度中的应用

为了更好地提高短文本语义相似度分析能力,提出了基于神经网络和组合语义的短文本语义相似度分析算法。利用神经网络构建词义表示模型,结合局部和全局上下文信息学习词语在实际语境下的表示;通过句法分析,得到文本的依存关系,并依此构建组合关系树,使用组合语义模型得到整个文本的语义表示;通过计算两个语义表示结果之间的相似性来计算文本的语义相似度。实验分析结果表明,该方法能够在一定程度上提高文本语义分析能力。     

一种基于复杂网络的短文本语义相似度计算

将传统的文本相似度量方法直接移植到短文本时,由于短文本内容简短的特性会导致数据稀疏而造成计算结果出现偏差。该文通过使用复杂网络表征短文本,提出了一种新的短文本相似度量方法。该方法首先对短文本进行预处理,然后对短文本建立复杂网络模型,计算短文本词语的复杂网络特征值,再借助外部工具计算短文本词语之间的语义相似度,然后结合短文本语义相似度定义计算短文本之间的相似度。最后在基准数据集上进行聚类实验,验证本文提出的短文本相似度计算方法在基于F-度量值标准上,优于传统的TF-IDF方法和另一种基于词项语义相似度的计算方法。