Natural Language Components Research Articles

Performance Evaluation of Natural Language Text Watermarking using Encryption Techniques

in communication technologies have made it easier to distribute and communicate information effectively. The increasing use of Internet has caused the information to be paperless and all the working to be electronic as compared to the conventional paper distribution. As the information is available on internet, it is liable to many kinds of threats like illegal copying, distribution, tampering, authentication etc. Till now, the electronic information can be secured by using different techniques like steganography, cryptography, watermarking. In text watermarking various techniques are implemented for English, Chinese, Turkish and Arabic language text using different methods. This paper includes an improved text watermarking technique for English language text documents. This paper proposes a technique which uses natural language components and UMARAM encryption technique. This study has focussed on grammatical rules like conjunctions, pronouns and modal verbs to generate encrypted watermark message. The performance of proposed technique is compared with AES encryption technique. It has been concluded that UMARAM algorithm is robust against content modifications and at the same time, is capable of detecting tampering attacks. The resulted technique is tested over various text documents to check the effectiveness of the algorithm.

An End‐to‐End Conversational Second‐Screen Application for TV Program Discovery

In this article, we report on a multiphase R&D effort to develop a conversational second screen application for TV program discovery. Our goal is to share with the community the breadth of artificial intelligence (AI) and natural language (NL) technologies required to develop such an application along with learnings from target end-users. We first give an overview of our application from the perspective of the end-user. We then present the architecture of our application along with the main AI and NL components, which were developed over multiple phases. The first phase focuses on enabling core functionality such as effectively finding programs matching the user’s intent. The second phase focuses on enabling dialog with the user. Finally, we present two user studies, corresponding to these two phases. The results from both studies demonstrate the effectiveness of our application in the target domain.

Toward Mobile Robots Reasoning Like Humans

Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent developments using this architecture on a fielded mobile robot platform operating in unknown urban environments. We report a summary of extensive outdoor experiments; the results suggest that a multidisciplinary approach to robotics has the potential to create competent human-robot teams.

Pragmatics re-established on its feet

The “Mixed Game Model” as a linguistic theory conceptualizes the natural ability of human beings: “competence-in-performance”. The theory rests on a holistic view of language and dialogue that takes into account the physically and socially complex and more or less chaotic universe we live in. Competence-in-performance is thereby rooted in human biology. This ability enables people to communicate in an extremely context-sensitive manner. It rests on the subtle integration of three fundamental registers of human mental functioning in dialogic communication: perception (including emotional perception), cognition, and natural language. This explains why the theory is called the Mixed Game. The Mixed Game defines language as dialogue, conceptualizing the components of natural language along action and reaction. The book gives us a new foundation to the pragmatics of the human communication system, a rigorous systematic methodology to study all kinds of dialogues, from the simplest to the more sophisticated, from the most informal to the most institutional or professional. This book also suggests various theoretical and practical developments. It is useful not only for linguists, but also for all researchers in psychology, sociology, and more generally in all the human sciences.

Natural language processing in CLIME, a multilingual legal advisory system

This paper describes CLIME, a web-based legal advisory system with a multilingual natural language interface. CLIME is a ‘proof-of-concept’ system which answers queries relating to ship-building and ship-operating regulations. Its core knowledge source is a set of such regulations encoded as a conceptual domain model and a set of formalised legal inference rules. The system supports retrieval of regulations via the conceptual model, and assessment of the legality of a situation or activity on a ship according to the legal inference rules. The focus of this paper is on the natural language aspects of the system, which help the user to construct semantically complex queries using WYSIWYM technology, allow the system to produce extended and cohesive responses and explanations, and support the whole interaction through a hybrid synchronous/asynchronous dialogue structure. Multilinguality (English and French) is viewed simply as interface localisation: the core representations are language-neutral, and the system can present extended or local interactions in either language at any time. The development of CLIME featured a high degree of client involvement, and the specification, implementation and evaluation of natural language components in this context are also discussed.

Information Extraction Strategies for Thai Documents

The development of an information extraction (IE) system for Thai documents raises a number of issues which are not important for IE in English and other European languages. We describe the characteristics of written Thai and the problem statements, and our approach to the Thai IE system. The structure of written Thai is highly ambiguous, which requires more sophisticated techniques than are necessary to perform comparable IE tasks in most European languages, and large amounts of domain knowledge to cope with these ambiguities. The basic characteristic of this system is to provide different natural language components to assess the surface structure of the documents. These components include word segmentation, specific lexical structure terms identification and part-of-speech tagger. Further analysis is to perform a shallow parsing based on the relevant regions that contain the specific trigger terms or patterns specified in the extraction templates. Finally, the information of interest is extracted from the grammar trees in corresponding to predefined concept definitions and returns the users with a list of answers responding to each concept.

Enhancing CASE Environments by Using Linguistics

In this paper it is argued that CASE environments could and should be enhanced considerably by using theories and knowledge from linguistics. The environments should 'know' about the language of their users and the domains they are used for. By basing the modeling techniques supported by the CASE tool on linguistic theories and by incorporating Natural Language parsing and generating tools, the CASE environment is able to handle the users' language in an accurate way. More specifically, the CASE environment deals with the meaning of words, instead of the meaningless strings themselves. These meanings, which are retrieved from an online lexicon, are linked to the words used in both the requirements documents as well as in the conceptual models in order to achieve a certain degree of consistency between the two of them. The base structure of these models is automatically derived by analyzing the textual requirements documents that describe the domain under consideration. This Natural Language analysis consists of parsing the texts and retrieving the word meanings that corresponds to concepts that may be of interest for modeling this domain accurately. Furthermore, the resulting models can be validated by people who are not familiar with the modeling notations used, by paraphrasing the models to Natural Language sentences. This paper mainly focuses on the profits gained by using linguistic knowledge in CASE environments, on the philosophy behind this approach and on three specific Natural Language components: the lexicon, Natural Language analysis and text generation for requirements validation. This article is based on the paper "Truly Intelligent CASE Environments Profit from Linguistics" [7], which was presented during the SEKE conference in Madrid (June 1997).

Uniform knowledge representation for language processing in the B2 system

We describe the natural language processing and knowledge representation components of B2, a collaborative system that allows medical students to practice their decision-making skills by considering a number of medical cases that differ from each other in a controlled manner. The underlying decision-support model of B2 uses a Bayesian network that captures the results of prior clinical studies of abdominal pain. B2 generates story-problems based on this model and supports natural language queries about the conclusions of the model and the reasoning behind them. B2 benefits from having a single knowledge representation and reasoning component that acts as a blackboard for intertask communication and cooperation. All knowledge is represented using a propositional semantic network formalism, thereby providing a uniform representation to all components. The natural language component is composed of a generalized augmented transition network parser/grammar and a discourse analyzer for managing the natural language interactions. The knowlege representation component supports the natural language component by providing a uniform representation of the content and structure of the interaction, at the parser, discourse, and domain levels. This uniform representation allows distinct tasks, such as dialog management, domain-specific reasoning, and meta-reasoning about the Bayesian network, to all use the same information source, without requiring mediation. This is important because there are queries, such as Why?, whose interpretation and response requires information from each of these tasks. By contrast, traditional approaches treat each subtask as a “black-box” with respect to other task components, and have a separate knowledge representation language for each. As a result, they have had much more difficulty providing useful responses.

A data management strategy for transportable natural language interfaces

This thesis focuses on the problem of designing a highly portable domain independent natural language interface for standard relational database systems. It is argued that a careful strategy for providing the natural language interface (NLI) with morphological, syntactic, and semantic knowledge about the subject of discourse and the database is needed to make the NLI portable from one subject area and database to another. There has been a great deal of interest recently in utilizing the database system to provide that knowledge. Previous approaches attempted to solve this challenging problem by capturing knowledge from the relational database (RDB) schema, but were unsatisfactory for the following reasons: 1.) RDB schemas contain referential ambiguities which seriously limit their usefulness as a knowledge representation strategy for NL understanding. 2.) Knowledge captured from the RDB schema is sensitive to arbitrary decisions made by the designer of the schema. In our work we provide a new solution by applying a conceptual model for database schema design to the design of a portable natural language interface. It has been our observation that the process used for adapting the natural language interface to a new subject area and database overlaps considerably with the process of designing the database schema. Based on this important observation, we design an enhanced natural language interface with the following significant features: complete independence of the linguistic component from the database component, economies in attaching the natural language and DB components, and sharing of knowledge about the relationships in the subject of discourse for database schema design and NL understanding.

Large vocabulary speech recognition using subword units

Research in large vocabulary speech recognition has been intensively carried out worldwide, in the past several years, spurred on by advances in algorithms, architectures and hardware. In the United States, the DARPA community has focused efforts on studying several continuous speech recognition tasks including Naval Resource Management, a 991 word task, ATIS (Air Travel Information System), a speech understanding task with an open vocabulary (in practice on the order of several thousand words) and a natural language component, and Wall Street Journal, a voice dictation task with a vocabulary on the order of 20,000 words. Although we have learned a great deal about how to build and efficiently implement large vocabulary speech recognition systems, there remain a whole range of fundamental questions for which we have no definitive answers. In this paper we review the basic structure of a large vocabulary speech recognition system, address the basic system design issues, discuss the considerations in the selection of training material, choice of subword unit, method of training and adaptation of models of subword units, integration of language model, and implementation of the overall system, and report on some recent results, obtained at AT&T Bell Laboratories, on the Resource Management task.

Propositional representation for graphical knowledge

Multi-media interfaces with a graphics and a natural language component can be viewed as a Natural Language Graphics systems without a host program. We will investigate a theory of Natural Language Graphics that is based on the notion of “Graphical Deep Knowledge” defined in this research. Graphical Deep Knowledge is knowledge that can be used for display purposes as well as reasoning purposes and we describe the syntax and semantics of its constructs. This analysis covers forms, positions, attributes, parts, classes, reference frames, inheritability, etc. Part hierarchies are differentiated into three sub-types. The usefulness of inheritance along part hierarchies is demonstrated, and criticism of inheritance-based knowledge representation formalisms with a bias towards class hierarchies is derived from this finding. The presented theory has been implemented as a generator program that creates pictures from knowledge structures, and as an augmented transition network grammar that creates knowledge structures from limited natural language input. The function of the picture generation program Tina as a user interface for a circuit board maintenance system and as part of a CAD-like layout system is demonstrated.

The second generation intelligent user interface for the crustal dynamics data information system

The National Space Science Data Center (NSSDC) has initiated an Intelligent Data Management (IDM) research effort which has as one of its components the development of an Intelligent User Interface (IUI). The intent of the IUI effort is to develop a friendly and intelligent user interface service that is based on database management system (DBMS), expert systems, natural language processing, and graphics technologies. This article presents the results of the project on the second generation interface to NASA's crustal dynamics database. In general, the system spans a local Sun 3/260 scientific workstation connected to the VAX 11/780 central system. Instead of using a database query language, the user uses the expert system to advise the user in the construction of English queries, which are then sent to either a local or a central natural language front-end to the database. On a systems level, the expert system interface is written in the Automated Reasoning Tool (ART), Lisp, C, and Fortran and resides on the Sun 3/260. Also, the Natural Language components are written using the THEMIS natural language front-end and the NLI DataTalker and are connected to the ORACLE and the Sybase DBMS, respectively. Lastly, the crustal dynamics database and THEMIS reside on the VAX 11/780.

The Readability of Mathematics Textbooks Revisited

All mathematics textbooks are written in more than one language. Each contains portions in a natural language, such as English, together with portions in one or more additional languages, such as HinduArabic numeration, various algebraic notational systems, the language of the sentential calculus, and the like. The mix of these languages varies greatly from book to book, topic to topic, and level to level. The natural language component often is replete with translations from one of the other symbol systems. For example, phrases such as if and only if, if … then, A and B are direct translations from the sentential calculus. Their precise meanings must be comprehended by the reader before he can hope to assign the author's intended meaning to the passage in which they occur.