The Application of Natural Language Processing and Automated Scoring in Second Language Assessment

Natural language processing (NLP) is an area of research that is used to investigate the application of natural language and is the foundation of machine translation, natural language text processing, natural language generation, multilingual and cross language information retrieval, speech recognition, parsing, and expert systems. To understand natural language in order to build or select appropriate algorithms for processing, three major issues are called into attention: humans’ thought processes, the meaning of linguistic input in context, and world knowledge. These considerations have led to the development of various types of NLP tools for lexical and morphological analysis, semantic and discourse analysis, as well as knowledge-based approaches (c.f., Chowdhury, 2003). After decades of evolution and advancement, the current stage of NLP, as Xi (2010) pointed out, has allowed language testing researchers to apply its techniques in developing automated scoring systems for the purpose of language learning and assessment

Natural Language for Expert Systems: Comparisons with Database Systems

Do natural language database systems still provide a valuable environment for further work on natural language processing? Are there other systems which provide the same hard environment for testing, but allow us to explore more interesting natural language questions? In order to answer no to the first question and yes to the second (the position taken by our panel's chair), there must be an interesting language problem which is more naturally studied in some other system than in the database system. We are currently working on natural language for expert systems at Columbia and thus, expert systems provide a natural alternative environment to compare against the database system. The relatively recent success of expert systems in commercial environments (e.g., Stolfo and Vesonder 83, McDermott 81) indicates that they meet the criteria of a hard test environment. In our work, we are particularly interested in developing the ability to generate explanations that are tailored to the user of the system based on the previous discourse in order to do this in an interesting way. We assume that explanation will be part of natural language dialog with the system, allowing the user maximum flexibility in interacting with the system and allowing the system maximum opportunity to provide different explanations

Special Issue in Artificial Intelligence

Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8].Artificial intelligence (AI) is an interdisciplinary subject in science and engineering that makes it possible for machines to learn from data. Artificial Intelligence applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1, 3], support vector machine applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7] and big data analytics for natural disasters [8]

The crustal dynamics intelligent user interface anthology

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 is to develop a friendly and intelligent user interface service based on expert systems and natural language processing technologies. The purpose of such a service is to support the large number of potential scientific and engineering users that have need of space and land-related research and technical data, but have little or no experience in query languages or understanding of the information content or architecture of the databases of interest. This document presents the design concepts, development approach and evaluation of the performance of a prototype IUI system for the Crustal Dynamics Project Database, which was developed using a microcomputer-based expert system tool (M. 1), the natural language query processor THEMIS, and the graphics software system GSS. The IUI design is based on a multiple view representation of a database from both the user and database perspective, with intelligent processes to translate between the views

Logic programming and metadata specifications

Artificial intelligence (AI) ideas and techniques are critical to the development of intelligent information systems that will be used to collect, manipulate, and retrieve the vast amounts of space data produced by 'Missions to Planet Earth.' Natural language processing, inference, and expert systems are at the core of this space application of AI. This paper presents logic programming as an AI tool that can support inference (the ability to draw conclusions from a set of complicated and interrelated facts). It reports on the use of logic programming in the study of metadata specifications for a small problem domain of airborne sensors, and the dataset characteristics and pointers that are needed for data access

The Application of Natural Language Processing and Automated Scoring in Second Language Assessment

Natural language processing (NLP) is an area of research that is used to investigate the application of natural language and is the foundation of machine translation, natural language text processing, natural language generation, multilingual and cross language information retrieval, speech recognition, parsing, and expert systems. To understand natural language in order to build or select appropriate algorithms for processing, three major issues are called into attention: humans’ thought processes, the meaning of linguistic input in context, and world knowledge. These considerations have led to the development of various types of NLP tools for lexical and morphological analysis, semantic and discourse analysis, as well as knowledge-based approaches (c.f., Chowdhury, 2003). After decades of evolution and advancement, the current stage of NLP, as Xi (2010) pointed out, has allowed language testing researchers to apply its techniques in developing automated scoring systems for the purpose of language learning and assessment

Supporting the Development of Cyber-Physical Systems with Natural Language Processing: A Report

Software has become the driving force for innovations in any technical system that observes the environment with different sensors and influence it by controlling a number of actuators; nowadays called Cyber-Physical System (CPS). The development of such systems is inherently inter-disciplinary and often contains a number of independent subsystems. Due to this diversity, the majority of development information is expressed in natural language artifacts of all kinds. In this paper, we report on recent results that our group has developed to support engineers of CPSs in working with the large amount of information expressed in natural language. We cover the topics of automatic knowledge extraction, expert systems, and automatic requirements classification. Furthermore, we envision that natural language processing will be a key component to connect requirements with simulation models and to explain tool-based decisions. We see both areas as promising for supporting engineers of CPSs in the future

Natural language processing methods for knowledge management - Applying document clustering for fast search and grouping of engineering documents

Product development companies collect data in form of Engineering Change Requests for logged design issues, tests, and product iterations. These documents are rich in unstructured data (e.g. free text). Previous research affirms that product developers find that current IT systems lack capabilities to accurately retrieve relevant documents with unstructured data. In this research, we demonstrate a method using Natural Language Processing and document clustering algorithms to find structurally or contextually related documents from databases containing Engineering Change Request documents. The aim is to radically decrease the time needed to effectively search for related engineering documents, organize search results, and create labeled clusters from these documents by utilizing Natural Language Processing algorithms. A domain knowledge expert at the case company evaluated the results and confirmed that the algorithms we applied managed to find relevant document clusters given the queries tested

The development of a prototype intelligent user interface subsystem for NASA's scientific database systems

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 latter is to develop a friendly and intelligent user interface service that is based on expert systems and natural language processing technologies. The purpose is to support the large number of potential scientific and engineering users presently having need of space and land related research and technical data but who have little or no experience in query languages or understanding of the information content or architecture of the databases involved. This technical memorandum presents prototype Intelligent User Interface Subsystem (IUIS) using the Crustal Dynamics Project Database as a test bed for the implementation of the CRUDDES (Crustal Dynamics Expert System). The knowledge base has more than 200 rules and represents a single application view and the architectural view. Operational performance using CRUDDES has allowed nondatabase users to obtain useful information from the database previously accessible only to an expert database user or the database designer

Real World of Artificial Intelligence - A Review

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world