COACH: A tutor based on active schemas

The Coach system, a computer simulation of a human tutor, was constructed with the goal of obtaining a better understanding of how a tutor interprets the student's behavior, diagnoses difficulties, and gives advice. Coach gives advice to a student who is learning a simple computer programming language. Its intelligence is based on a hierarchy of active schemas that represent the tutor's general concepts and on more specific information represented in a semantic network. The coordination of conceptually guided and data-driven processing enables the Coach system to interpret student behavior, recognize errors, and give advice to the student.     

一种基于语义分析的汉语语音识别纠错方法

汉语语音识别的研究越来越重视与语言处理的结合,语音识别已经不是单纯的语音信号处理。N-gram语言模型应用到语音识别系统中,大大增强了系统的正确率和稳定性,但它也有其自身的局限性,使得语音识别出现许多语法和语义的错误结果。本文分析了语音识别产生语音和文字方面的错误的原因和类型,在概念层次网络语言模型的基础上提出了一种基于语句语义分析和混淆音矩阵的语音识别纠错方法。通过三个发音人、5万字的声音语料和216句实验语句的纠错测试,本文的纠错系统在纠正语义搭配型错误方面有比较好的表现,可克服N-gram语言模型带来的一些缺陷。本文提出的纠错方法还可以融合到语音识别系统中,以便更好地为语音识别的纠错处理服务。     

Toward Spoken Human–Computer Tutorial Dialogues

Oral discourse is the primary form of human–human communication, hence, computer interfaces that communicate via unstructured spoken dialogues will presumably provide a more efficient, meaningful, and naturalistic interaction experience. Within the context of learning environments, there are theoretical positions supporting a speech facilitation hypothesis that predicts that spoken tutorial dialogues will increase learning more than typed dialogues. We evaluated this hypothesis in an experiment where 24 participants learned computer literacy via a spoken and a typed conversation with AutoTutor, an intelligent tutoring system with conversational dialogues. The results indicated that (a) enhanced content coverage was achieved in the spoken condition; (b) learning gains for both modalities were on par and greater than a no-instruction control; (c) although speech recognition errors were unrelated to learning gains, they were linked to participants' evaluations of the tutor; (d) participants adjusted their conversational styles when speaking compared to typing; (e) semantic and statistical natural language understanding approaches to comprehending learners' responses were more resilient to speech recognition errors than syntactic and symbolic-based approaches; and (f) simulated speech recognition errors had differential impacts on the fidelity of different semantic algorithms. We discuss the impact of our findings on the speech facilitation hypothesis and on human–computer interfaces that support spoken dialogues.     

基于轻语义λ-演算的汉语陈述句灵活语序研究

目前,自然语言处理已经从句法、语法层面走向轻语义层面。对于汉语陈述句的处理,传统的方法是采用Lambek演算来进行处理。但是传统的Lambek演算无法处理汉语中的灵活语序问题,而现有的方法,如加入模态词、新连接词等,又因为其进一步使得本已是NP-hard的Lambek演算时间复杂度变大,并不适合当前的计算机处理。基于此,该文提出了λ-Lambek演算,即采用Lambek演算来对汉语陈述句进行句法演算,并通过Curry-Howard对应理论与λ-演算来对汉语陈述句进行轻语义模型的构建。λ-Lambek演算不仅能够对汉语陈述句进行轻语义演算,而且还能对汉语陈述句灵活语序进行处理。     

基于自然语言计算模型的汉语理解系统

本文首先给出了一种自然语言计算模型,该模型把自然语言交流过程划分为三个层次:语言形式,表层语义和深层语义,从而将自然语言理解抽象为一个复合函数UP(s,k),依据这个模型,我们设计了一个汉语理解系统,这个系统具有良好的扩展性和可移植性,该系统采用汉语语义结构文法来分析汉语句子,把语法分析和语义分析有机地结合在一起,文中形式定义了词语的深层语义以及深层语义的基本运算,给出了分析器、理解器以及生成器的算法。     

Exploiting convergence to improve natural language understanding

Convergence is the phenomenon in human dialogue whereby participants adopt characteristics of each other's speech. Communicants are unaware of this occurring. If it were possible to invoke such a phenomenon in a natural language interface it would provide a means of keeping user inputs within the range of lexical and syntactic coverage of the system, while keeping the dialogue ‘natural’ in the sense of requiring no more conscious effort in observing conventions of format than human-human dialogue.

A ‘Wizard of Oz’ study was conducted to test the feasibility of this technique. Subjects were required to type queries into what they thought was a natural language database querying system. On completion of input the system presented a paraphrase for confirmation by subjects before presenting the answer. The paraphrases were constructed using particular terms and syntactic structures. Subjects began to use these terms and structures spontaneously in subsequent queries.

Observation of convergence in human-computer dialogue suggests that the technique can be incorporated in user interfaces to improve communication. The implementation issues for natural language dialogue are discussed, and other applications of the technique in HCI are outlined.     

基于自然语言理解的故障诊断方法研究

为解决知识规则推理故障诊断系统对用户输入限制的问题,提出了一种基于自然语言理解的二次回溯语义分词故障诊断方法,采用词频作为分词主要依据对用户自然语言输入进行分词切分,同时按邻近关系组词进行二次回溯处理,计算出故障征兆与规则的语义相似度,从而获得与故障字典的最佳匹配结果,使诊断系统不仅具备对用户自然语言输入处理的能力,而且具有自学习和诊断多故障现象的功能,将该方法应用于某大型设备故障诊断专家系统,取得了较好的诊断效果.     

Terminological inconsistency analysis of natural language requirements

Context: Terminological inconsistencies owing to errors in usage of terms in requirements specifications could result into subtle yet critical problems in interpreting and applying these specifications into various phases of SDLC.Objective: In this paper, we consider special class of terminological inconsistencies arising from term-aliasing, wherein multiple terms spread across a corpus of natural language text requirements may be referring to the same entity. Identification of such alias entity-terms is a difficult problem for manual analysis as well as for developing tool support.Method: We consider the case of syntactic as well as semantic aliasing and propose a systematic approach for identifying these. Identification of syntactic aliasing involves automated generation of patterns for identifying syntactic variances of terms including abbreviations and introduced-aliases. Identification of semantic aliasing involves extracting multidimensional features (linguistic, statistical, and locational) from given requirement text to estimate semantic relatedness among terms. Based upon the estimated relatedness and standard language database based refinement, clusters of potential semantic aliases are generated. Results of these analyses with user refinement lead to generation of entity-term alias glossary and unification of term usage across requirements.Results: A prototype tool was developed to assess the effectiveness of the proposed approach for an automated analysis of term-aliasing in the requirements given as plain English language text. Experimental results suggest that approach is effective in identifying syntactic as well as semantic aliases, however, when aiming for higher recall on larger corpus, user selection is necessary to eliminate false positives.Conclusion: This proposed approach reduces the time-consuming and error-prone task of identifying multiple terms which might be referring to the same entity to a process of tool assisted identification of such term-aliases.     

融入句法感知表示进行句法增强的语义解析

在自然语言处理（NLP）中,句法信息是完整句子中词汇与词汇之间的句法结构关系或者依存关系,是一种重要且有效的参考信息。语义解析任务是将自然语言语句直接转化成语义完整的、计算机可执行的语言。在以往的语义解析研究中,少有采用输入源的句法信息来提高端到端语义解析效率的工作。为了进一步提高端到端语义解析模型的准确率和效率,提出一种利用输入端句法依存关系信息来提高模型效率的语义解析方法。该方法的基本思路是先对一个端到端的依存关系解析器进行预训练;然后将该解析器的中间表示作为句法感知表示,与原有的字词嵌入表示拼接到一起以产生新的输入嵌入表示,并将得到的输入嵌入表示用于端到端语义解析模型;最后采用转导融合学习方式进行模型融合。实验对比了所提模型和基准模型Transformer以及过去十年的相关工作。实验结果表明,在ATIS、GEO、JOBS数据集上,融入依存句法信息感知表示以及转导融合学习的语义解析模型分别实现了89.1%、90.7%、91.4%的最佳准确率,全面超过了Transformer,验证了引入句法依存关系信息的有效性。     

Lexical and pragmatic disambiguation and re-interpretation in connectionist networks

Lexical and pragmatic ambiguity is a major source of uncertainty in natural language understanding. Symbolic models can make high-level inferences necessary for understanding text, but handle ambiguity poorly, especially when later context requires a re-interpretation of the input. Structured connectionist networks, on the other hand, can use their graded levels of activation to perform lexical disambiguation, but have trouble performing the variable bindings and inferencing necessary for language understanding. We have previously described a structured connectionist model, ROBIN, which overcomes many of these problems and allows the massively-parallel application of a large class of general knowledge rules. This paper describes how ROBIN uses these abilities and the contextual evidence from its semantic networks to disambiguate words and infer the most plausible plan/goal analysis of the input, while using the same mechanism to smoothly re-interpret the input if later context makes an alternative interpretation more likely. We present several experiments illustrating these abilities and comparing them to those of other connectionist models, and discuss several directions in which we are extending the model.     

拼写纠正在拼音输入法中的应用

中文输入法一直是中文语言研究的一个难题,文中以拼音整句输入法为基础,提出了在中文输入过程中的拼写自动修改,通过对用户输入过程中所犯各种错误的分析,建立了一种有效可行的打字模型,通过收集用户真实输入的数据,统计得到用户的打字模型的参数;同时基于大量的中文文本,训练得到一个强大的中文语言模型,并与中文的打字模型相结合,采用类似语音识别的技术,修改用户输入中的各种错误,并得到最适合的汉字。同时,拼写纠正不仅可以进行用户自适应,而且还适用于各种语言。     

Using SPIN for verification of multiagent data analysis

The paper presents an approach to formal verification of multiagent data analysis algorithms for ontology population. The system agents correspond to information items of the input data and the rule of ontology population and data processing. They determine values of information objects obtained at the preliminary phase of the analysis. The agents working in parallel check the syntactic and semantic consistency of tuples of information items. Since the agents operate in parallel, it is necessary to verify some important properties of the system related to it, such as the property that the controller agent correctly determines the system termination. In our approach, the SPIN model checking tool is used. The agent protocols are written in the Promela language (the input language of the tool), and the properties of the multiagent data analysis system are expressed in liner time logic (LTL). We carried out several experiments to check this model in various modes of the tool and for various numbers of agents.     

Natural language grammar induction with a generative constituent-context model

We present a generative probabilistic model for the unsupervised learning of hierarchical natural language syntactic structure. Unlike most previous work, we do not learn a context-free grammar, but rather induce a distributional model of constituents which explicitly relates constituent yields and their linear contexts. Parameter search with EM produces higher quality analyses for human language data than those previously exhibited by unsupervised systems, giving the best published unsupervised parsing results on the ATIS corpus. Experiments on Penn treebank sentences of comparable length show an even higher constituent F₁ of 71% on non-trivial brackets. We compare distributionally induced and actual part-of-speech tags as input data, and examine extensions to the basic model. We discuss errors made by the system, compare the system to previous models, and discuss upper bounds, lower bounds, and stability for this task.     

电子词典与词汇知识表达

词汇知识的表达与取得是自然语言处理极须克服的问题,本论文提出一个初步的架构与常识的抽取机制。语言处理系统是以词为讯息处理单元,登录在词项下的讯息可以包括统计、语法、语义、常识等。语言分析系统利用〈词〉为引得取得输入语句中相关词汇的语法、语义、常识等信息,让语言处理系统有更好的聚焦能力,可以藉以解决分词歧义、结构的歧义。对于不易以人工整理取得的常识,本论文也提出计算机自动学习的策略,以渐进式的方式累积概念与概念之间的语义关系,来增进语言系统的分析能力。这个策略可行的几个关键技术,包括(1)未登录词判别及语法语义自动分类, (2)词义分析, (3)应用语法语义及常识的剖析系统。     

数据库NL界面上汉语查询的EAAD模型

本文给出一种旨在描述数据库ＮＬ界面上汉语查询的语法，语义结构的ＥＡＡＤ模型。通过该模型，数据库自然语言界面上的查询分析与理解，可以与其相应的背景知识 机地结合起来，ＥＡＡＤ模型适合于描述任意构形上的ＥＲ模型或与其相应的关系模型上的汉语查询，尤其是描述涉及多实体，多路径的查询的结构规律，有利于增强数据库ＮＬ界面的理解力和可移植性。     

Symbolic connectionism in natural language disambiguation

Natural language understanding involves the simultaneous consideration of a large number of different sources of information. Traditional methods employed in language analysis have focused on developing powerful formalisms to represent syntactic or semantic structures along with rules for transforming language into these formalisms. However, they make use of only small subsets of knowledge. This article describes how to use the whole range of information through a neurosymbolic architecture which is a hybridization of a symbolic network and subsymbol vectors generated from a connectionist network. Besides initializing the symbolic network with prior knowledge, the subsymbol vectors are used to enhance the system's capability in disambiguation and provide flexibility in sentence understanding. The model captures a diversity of information including word associations, syntactic restrictions, case-role expectations, semantic rules and context. It attains highly interactive processing by representing knowledge in an associative network on which actual semantic inferences are performed. An integrated use of previously analyzed sentences in understanding is another important feature of our model. The model dynamically selects one hypothesis among multiple hypotheses. This notion is supported by three simulations which show the degree of disambiguation relies both on the amount of linguistic rules and the semantic-associative information available to support the inference processes in natural language understanding. Unlike many similar systems, our hybrid system is more sophisticated in tackling language disambiguation problems by using linguistic clues from disparate sources as well as modeling context effects into the sentence analysis. It is potentially more powerful than any systems relying on one processing paradigm     

Natural language querying for video databases

The video databases have become popular in various areas due to the recent advances in technology. Video archive systems need user-friendly interfaces to retrieve video frames. In this paper, a user interface based on natural language processing (NLP) to a video database system is described. The video database is based on a content-based spatio-temporal video data model. The data model is focused on the semantic content which includes objects, activities, and spatial properties of objects. Spatio-temporal relationships between video objects and also trajectories of moving objects can be queried with this data model. In this video database system, a natural language interface enables flexible querying. The queries, which are given as English sentences, are parsed using link parser. The semantic representations of the queries are extracted from their syntactic structures using information extraction techniques. The extracted semantic representations are used to call the related parts of the underlying video database system to return the results of the queries. Not only exact matches but similar objects and activities are also returned from the database with the help of the conceptual ontology module. This module is implemented using a distance-based method of semantic similarity search on the semantic domain-independent ontology, WordNet.