自然语言处理在文本情感分析领域应用综述

文本情感分析已经逐渐成为自然语言处理（NLP）的重要内容,并在系统推荐、用户情感信息获取,为政府、企业提供舆情参考等领域越来越占据重要地位。通过文献调研的方式,对情感分析领域的方法进行对比和综述。首先,从时间、方法等维度对情感分析的方法进行文献调研;然后,对情感分析的主要方法、应用场景进行归纳总结和对比;最后,在此基础上分析每种方法的优缺点。根据分析结果可以知道,在面对不同的任务场景,主要有三种情感分析的方法：基于情感字典的情感分析法、基于机器学习的情感分析法和基于深度学习的情感分析法,基于多策略混合的方法成为改进的趋势。文献调研表明,文本情感分析的技术方法还有改进的空间,在电子商务、心理治疗、舆情监控方面有较大市场和发展前景。     

New adaptive compressors for natural language text

Semistatic byte‐oriented word‐based compression codes have been shown to be an attractive alternative to compress natural language text databases, because of the combination of speed, effectiveness, and direct searchability they offer. In particular, our recently proposed family of dense compression codes has been shown to be superior to the more traditional byte‐oriented word‐based Huffman codes in most aspects. In this paper, we focus on the problem of transmitting texts among peers that do not share the vocabulary. This is the typical scenario for adaptive compression methods. We design adaptive variants of our semistatic dense codes, showing that they are much simpler and faster than dynamic Huffman codes and reach almost the same compression effectiveness. We show that our variants have a very compelling trade‐off between compression/decompression speed, compression ratio, and search speed compared with most of the state‐of‐the‐art general compressors. Copyright © 2008 John Wiley & Sons, Ltd.     

Extracting causation knowledge from natural language texts

SEKE is a semantic expectation‐based knowledge extraction system for extracting causation knowledge from natural language texts. It is inspired by human behavior on analyzing texts and capturing information with semantic expectations. The framework of SEKE consists of different kinds of generic templates organized in a hierarchical fashion. There are semantic templates, sentence templates, reason templates, and consequence templates. The design of templates is based on the expected semantics of causation knowledge. They are robust and flexible. The semantic template represents the target relation. The sentence templates act as a middle layer to reconcile the semantic templates with natural language texts. With the designed templates, SEKE is able to extract causation knowledge from complex sentences. Another characteristic of SEKE is that it can discover unseen knowledge for reason and consequence by means of pattern discovery. Using simple linguistic information, SEKE can discover extraction pattern from previously extracted causation knowledge and apply the newly generated patterns for knowledge discovery. To demonstrate the adaptability of SEKE for different domains, we investigate the application of SEKE on two domain areas of news articles, namely the Hong Kong stock market movement domain and the global warming domain. Although these two domain areas are completely different, in respect to their expected semantics in reason and consequence, SEKE can effectively handle the natural language texts in these two domains for causation knowledge extraction. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 327–358, 2005.     

Intelligent information processing for building university knowledge base

There are many ready-to-use software solutions for building institutional scientific information platforms, most of which have functionality well suited to repository needs. However, there have already been discussions about various problems with institutional digital libraries. As a remedy, an approach that is researcher-centric (rather than document-centric) has been proposed recently in some systems. This paper is devoted to research aimed at tools for building knowledge bases for university research. We focus on the AI methods that have been elaborated and applied practically within our platform for building such knowledge bases. In particular we present a novel approach to data acquisition and the semantic enrichment of the acquired data. In addition, we present the algorithms applied in the real life system for experts profiling and retrieval.     

基于自然语言处理的文本泄密自动检测技术

因文本信息泄密导致的危害越来越严重,但传统的泄密检测还停留在人工查看,效率低且易造成二次泄密.针对以上问题,采用文本相似度自动比较和数据加密方法,提出了一种基于自然语言处理的文本泄密自动检测技术.在实际应用中,因检测粒度过粗可能导致漏检,采用基于自然段落和语句的相似度检测方法,方便疑似段落和语句的自动定位,最后设计并实现了一个文本泄密检测系统.实验结果表明,该技术能很好地应用于涉密文本泄密的检测,具有保密、人工干预少、效率高、疑似段落定位等特点.     

A natural language interface to a clinical data base management system

Communication with a clinical data base in a natural language has been viewed for years as a desired objective to improve clinical decision making. This paper describes an exploratory system to interface medical personnel to an existing data base management system without a need for a formal query language. The methods involve contextual and syntactic analysis with emphasis on semantic interpretation of limited medical terminology in the area of kidney failure therapy. System testing by physicians resulted in modifications and improvements. A limited system of graphical displays and statistical analyses was integrated into the system to help physicians in the interpretations of clinical trends and observations.     

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.     

从经典逻辑知识构建ASP知识库的新方法

回答集程序设计(ASP)是一种主流的非单调知识表示工具。为了能够在利用ASP求解问题过程中使用现有的以经典逻辑表示的知识,给出了一种把以谓词逻辑公式表示的约束型知识和定义型知识转化为ASP程序或知识库的新方法,并以实例说明了其有效性。该方法满足转化后ASP程序的回答集与原公式集的模型具有一一对应关系。在实际应用中,该方法提供了一项从现存的以谓词逻辑为表示语言的知识库,构建以ASP为知识表示语言的非单调知识库的技术。     

EarlyData knowledge base for material decisions in building design

To make use of the great opportunities for emission reduction in early building design, future emissions need to be calculated when only geometric, but no detailed material information about a building is available. Currently, early design phase life cycle assessments (LCAs) are heavily reliant on assumptions of specific material choices, leading to single point emission values which suggest a precision not representative for an early design stage. By adding knowledge about possible locations and functions of materials within a building to life cycle inventory (LCI) data, the EarlyData knowledge base makes LCA data sets accessible and more transparent. Additionally, “generic building parts” are defined, which describe building parts independently of precise material choices as a combination of layers with specific functions. During evaluation, enriched LCI data and generic building parts enable assessment of a vast number of possible material combinations at once. Thus, instead of single value results for a particular material combination, ranges of results are displayed revealing the building parts with the greatest emission reduction potential. The application of the EarlyData tool is illustrated on a use case comparing a wood building and a concrete building. The database is developed with extensibility in mind, to include other criteria, such as (life cycle) costs.     

Expert system validation through knowledge base refinement

Knowledge base (KB) refinement is a suitable technique to support expert system (ES) validation. When used for validation, KB refinement should be guided not only by the number of errors to solve but also by the importance of those errors. Most serious errors should be solved first, even causing other errors of lower importance but assuring a neat validity gain. These are the bases for IMPROVER , a KB refinement tool designed to support ES validation. IMPROVER refines ES for medical diagnosis with this classification of error importance: false negative > false positive > ordering mismatch. IMPROVER has been used to support the validation of PNEUMON-IA , a real ES on the medical domain. After refinement, the ES validity has increased substantially. Detailed evidence of this improvement is provided, as well as examples of how the refinement process was performed. © 1996 John Wiley & Sons, Inc.     

Ensuring portability of Linux applications through standardization and knowledge base driven analysis

Problems with portability of applications across various Linux distributions is one of the major sore spots of independent software vendors (ISVs) wishing to support the Linux platform in their products. The source of the problem is that different distributions have different sets of system libraries that vary in the interfaces (APIs) provided. And the critical questions arise for ISVs such as “which distributions my application would run on?” or “what can I specifically do to make my application run on a greater number of distributions?”. This article refers to an industry-wide approach to mitigate the problem of Linux platform fragmentation through standardization of common interfaces—the Linux Standard Base (LSB) standard, the leading effort for the “single Linux specification”. The article shows how extending this approach with a knowledge base about the composition of real world Linux distributions can enable automatic portability analysis for Linux applications even if they use interfaces outside the scope of the standard. The knowledge base powered Linux Application Checker tool is described that can help answer the above questions by automatically analyzing the target application and confronting collected data about its external dependencies with what various distributions provide. Additionally, Linux Application Checker is an official tool approved by the Linux Foundation for certifying applications for compliance with the LSB standard.     

工艺设计知识库的建造与维护

文中根据工艺设计知识的特点,构造了层次化的知识表达、组织与知识库模型,分析了工艺知识库不一致的表现形式,并给出了相应的一致性验证算法,提出了基于广义决策表的知识库完备性检查方法。     

A survey on extraction of causal relations from natural language text

Knowledge and Information Systems - As an essential component of human cognition, cause–effect relations appear frequently in text, and curating cause–effect relations from text helps...     

Dynamic context generation for natural language understanding: a multifaceted knowledge approach

We describe a comprehensive framework for text understanding, based on the representation of context. It is designed to serve as a representation of semantics for the full range of interpretive and inferential needs of general natural language processing. Its most distinctive feature is its uniform representation of the various simple and independent linguistic sources that play a role in determining meaning: lexical associations, syntactic restrictions, case-role expectations, and most importantly, contextual effects. Compositional syntactic structure from a shallow parsing is represented in a neural net-based associative memory, where it then interacts through a Bayesian network with semantic associations and the context or "gist" of the passage carried forward from preceding sentences. Experiments with more than 2000 sentences in different languages are included.     

Fostering collective and individual learning through knowledge building

The purpose of this study was to design and examine a computer-supported knowledge-building environment and to investigate both collective knowledge-building dynamics and individual learning in the context of a tertiary education course in mainland China. The participants were 102 students in four intact Year-one tertiary business classes. Two classes experienced a knowledge-building environment (CKB) and the other two were taught using a regular project-based approach (RPBL). Data were obtained from interactions in the forum, writing quality, group-learning portfolios, and surveys. Quantitative analyses indicated that the knowledge-building groups outperformed the comparison groups on academic literacy assessed in terms of conceptual understanding and explanation, and obtained higher scores on beliefs about collaboration. Within-group analyses indicated that the students’ engagement in Knowledge Forum was a significant predictor of their academic literacy. Qualitative contrastive analyses of high- and low-performance groups identified different patterns of conceptual, metacognitive and social processes, and showed that student groups engaging in more collective and meta-discourse discourse moves performed better on individual scores in academic literacy. The implications of examining both collaborative dynamics and individual learning and designing computer-supported knowledge building for tertiary students are discussed.     

A neural net for extracting knowledge from natural language databases

A model of a fuzzy neuron, one which increases the computational power of the artificial neuron, turning it also into a symbolic processing device, is presented. The model proposes the synapsis to be symbolically and numerically defined, by means of the assignment of tokens to the presynaptic and postsynaptic neurons. The matching or concatenation compatibility between these tokens is used to decide about the possible connections among neurons of a given net. The strength of the compatible synapsis is made dependent on the amount of the available presynaptic and postsynaptic tokens. The symbolic and numeric processing capacity of the new fuzzy neuron is used to build a neural net (JARGON) to disclose the existing knowledge in natural language databases such as medical files, sets of interviews and reports about engineering operations.