基于机器学习的古典诗词作者的判别研究

主要基于机器学习的NaveBaye等方法,对诗词文本采用向量空间模型来表示,首次提出了中国古代诗人李白和杜甫作品判别计算模型,并采用了信息增益作为特征选择的依据,结合爬山法完善了计算模型,该模型已经在精典语料《全唐诗》的机器学习基础上得到实现,获得较好的诗人作品判别效果,准确度可达到98.3%,该方法已成功地推广到古典诗词的风格流派的机器判别上[1],准确度达到88.5%。本文提出了古典诗词研究的技术方向。     

NLP心理疗法——一种简单快速的心理咨询与治疗方法

NLP心理疗法是从完形疗法发展而来的一种,目前正被越来越多地应用于心理咨询与心理治疗的临床工作。本文主要介绍了NLP心理疗法的主要思想、基本原理和常用治疗技巧,以及把NLP疗法与精神分析进行了简要比较。     

Question answering from structured knowledge sources

We present an implemented approach for domain-restricted question answering from structured knowledge sources, based on robust semantic analysis in a hybrid NLP system architecture. We perform question interpretation and answer extraction in an architecture that builds on a lexical-conceptual structure for question interpretation, which is interfaced with domain-specific concepts and properties in a structured knowledge base. Question interpretation involves a limited amount of domain-specific inferences, and accounts for higher-level quantificational questions. Question interpretation and answer extraction are modular components that interact in clearly defined ways. We derive so-called proto queries from the linguistic representations, which provide partial constraints for answer extraction from the underlying knowledge sources. The search queries we construct from proto queries effectively compute minimal spanning trees from the underlying knowledge sources. Our approach naturally extends to multilingual question answering, and has been developed as a prototype system for two application domains: the domain of Nobel prize winners, and the domain of Language Technology, on the basis of the large ontology underlying the information portal LT World.     

NLP简快心理治疗方法的模式整合及其优势

NLP简快心理疗法于20世纪70年代创立于美国,80～90年代得到完善、发展和逐步推广。它吸取了精神分析、行为主义及人本主义流派的理论和治疗方法并加以整合优化,形成了以检定语言模式、思考与暗示语言模式、激发潜意识与自我暗示模式为优势和特点的心理咨询与治疗的方法,对心理咨询和治疗理论与实践的发展起到了积极的作用。