Form Of Natural Language Research Articles

Geach, Aristotle and Predicate Logics

AbstractGeach's account of the Aristotelian logic of categorical sentences supplemented the views shared by Frege, Russell, Quine and others. I argue that this particular predicate logic approach and Geach's points apply to only one variety of natural language categorical sentences. For example, it takes the universal categorical as a universal conditional “If anything is a man, then it is mortal”. A different natural language form can and should be invoked: “Every man is a mortal.” Employing special restricted quantifiers in a version of predicate logic, we justify a full square of opposition and the rules of quality and quantity that Geach rejected.

A scalable approach for ideation in biologically inspired design

AbstractThis paper presents a bioinspiration approach that is able to scalably leverage the ever-growing body of biological information in natural-language format. The ideation tool AskNature, developed by the Biomimicry 3.8 Institute, is expanded with an algorithm for automated classification of biological strategies into the Biomimicry Taxonomy, a three-level, hierarchical information structure that organizes AskNature's database. In this way, the manual work entailed by the classification of biological strategies can be alleviated. Thus, the bottleneck is removed that currently prevents the integration of large numbers of biological strategies. To demonstrate the feasibility of building a scalable bioideation system, this paper presents tests that classify biological strategies from AskNature's reference database for those Biomimicry Taxonomy classes that currently hold sufficient reference documents.

Correlation between the Economy News and Stock Market in Turkey

Depending on the market strength and structure, it is a known fact that there is a correlation between the stock market values and the content in newspapers. The correlation increases in weak and speculative markets, while they never get reduced to zero in the strongest markets. This research focuses on the correlation between the economic news published in a highly circulating newspaper in Turkey and the stock market closing values in Turkey. In the research several feature extraction methodologies are implemented on both of the data sources, which are the stock market values and economic news. Since the economic news is in natural language format, the text mining technique, term frequency – inverse document frequency is implemented. On the other hand, the time series analysis methods like random walk, Bollinger band, moving average or difference are applied over the stock market values. After the feature extraction step, the classification methods are built on the well-known classifiers support vector machine, k-nearest neighborhood and decision tree. Moreover, an ensemble classifier based on majority voting is implemented on top of these classifiers. The success rates show that the results are satisfactory to claim the methods implemented in this study can be spread to future research with similar data sets from other countries.

Importance of Problem Statement in Solving Industry Problems

Problem statements is a form of natural language used in any problem solvings [17]. A problem statement is described in terms of scope, structure and its purpose. Analysis in problem statement is a critical element of a structured problem solving that has not been included in or explicated by traditional stage models of the process. This paper details out that an analysis stage is needed to develop the implications of a problem's definition and to direct the selection and pursuit of solution strategies. This paper also discusses the importance of problem statement and how a new method of structure is being modeled for an effecient and effective problem solving in industrial environment. Strategies for future research on problem analysis are discussed

Domain ontology for scenario-based hazard evaluation

Among the scenario-based hazard evaluation procedures, Hazard and Operability Analysis (HAZOP) is a structured and systematic technique for process hazard examination and risk management widely used around the world. Information used and produced during HAZOP studies is recorded in the form of textual natural language documents. Consequently, reusing this knowledge is limited by the difficulties in finding, retrieving, auditing, sharing, and analyzing HAZOP-related information. Other limitations are faced in terms of computer-based extraction and reasoning of knowledge. To resolve such issues, a domain ontology called Scenario Object Model (SOM) is proposed in this paper, which can be used to represent the contents and structures of hazard evaluation information. Automatic reasoning and semi-quantitative algorithms can be implemented on SOM. Computer-aided automatic hazard evaluation and transferring, auditing, and sharing of HAZOP information have been realized effectively through the use of SOM.

An Immune Based Patient Anomaly Detection using RFID Technology

Online product reviews is considered as a major informative resource which is useful for both customers and manufacturers. The online reviews are unstructured-free-texts in natural language form. The task of manually scanning through huge volume of review is very tedious and time consuming. Therefore it is needed to automatically process the online reviews and provide the necessary information in a suitable form. In this paper, we dedicate our work to the task of classifying the reviews based on the opinion, i.e. positive or negative opinion. This paper mainly addresses using ensemble approach of Support Vector Machine (SVM) for opinion mining. Ensemble classifier was examined for feature based product review dataset for three different products. We showed that proposed ensemble of Support Vector Machine is superior to individual baseline approach for opinion mining in terms of error rate and Receiver operating characteristics Curve.
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 Key words: Opinion, Classification, Machine Learning.

AN EXPLANATION FACILITY FRAMEWORK FOR RULE-BASED SYSTEMS

Rule engines, business rule management systems and other rule-based systems used today widely utilize methods, techniques and technologies from the era of expert systems. Unfortunately, this doesn't seem to be the case when it comes to explanation facilities. Nowadays, the use of explanation facilities seems more important than ever. Business rule management systems control or constrain the behavior of business processes through business rules and an explanation of the inference process intended for the end user would be more than welcome. An explanation facility framework which was created in order to remedy this situation is presented in this paper. It is written in Java and is supposed to be a generic solution for modern rule-based systems. Besides being free and open-source, it is simple to use and can generate explanations in the form of natural language like sentences. Internationalization is also supported and explanations can be saved as textual, XML or PDF reports.

Textual Requirement Analysis for UML Diagram Extraction by using NLP

Requirement analysis is the preliminary step in software development process. The requirements stated by the clients are analyzed and an abstraction of it, is created which is termed as requirement model. The automatic generation of UML diagram from natural language requirements is highly challenging and demanding very efficient methodology. Unified Modeling Language (UML) models are helpful for understanding the problems, communicating with application experts and preparing documentation. The static design view of the system can be modeled using a UML class diagram. System requirements stated by the user are usually in natural language form. This is an imprecise and inconsistent form which is difficult to be used by the developer for design UML model. We present a new methodology for generating UML diagrams or models from natural language problem statement or requirement specification. We have named our methodology as Requirement analysis and UML diagram extraction (RAUE). General Terms

USING BIOLOGICAL ANALOGIES FOR ENGINEERING PROBLEM SOLVING AND DESIGN

This paper summarizes various aspects of identifying and using biological analogies in engineering design. To avoid the immense as well as potentially biased task of creating a database specifically for this purpose, the chosen approach searches biological knowledge in natural language format, e.g., books, papers, etc., for instances of functional keywords describing the engineering problem. Strategies developed to facilitate this search as well how text descriptions of biological phenomena were used in problem solving are summarized. Case studies in design for remanufacture and microassembly are used to provide an overview of the method.

Biologically Meaningful Keywords for Functional Terms of the Functional Basis

Biology is recognized as an excellent source of analogies and stimuli for engineering design. Previous work focused on the systematic identification of relevant biological analogies by searching for instances of functional keywords in biological information in natural-language format. This past work revealed that engineering keywords could not always be used to identify the most relevant biological analogies as the vocabularies between biology and engineering are sufficiently distinct. Therefore, a retrieval algorithm was developed to identify potential biologically meaningful keywords that are more effective in searching biological text than corresponding engineering keywords. In our current work, we applied and refined the retrieval algorithm to translate functional terms of the functional basis into biologically meaningful keywords. The functional basis is widely accepted as a standardized representation of engineering product functionality. Therefore, our keywords could serve as a thesaurus for engineers to find biological analogies relevant to their design problems. We also describe specific semantic relationships that can be used to identify biologically meaningful keywords in excerpts describing biological phenomena. These semantic relations were applied as criteria to identify the most useful biologically meaningful keywords. Through a preliminary validation experiment, we observed that different translators were able to apply the criteria to identify biologically meaningful keywords with a high degree of agreement to those identified by the authors. In addition, we describe how fourth-year undergraduate mechanical engineering students used the biologically meaningful keywords to develop concepts for their design projects.

Multimodal Natural Language Query System For Processing And Analyzing Voice And Proximity-Based Queries

The disclosure provides a natural language query system and method for processing and analyzing multimodally-originated queries, including voice and proximity-based queries. The natural language query system includes a Web-enabled device including a speech input module for receiving a voice-based query in natural language form from a user and a location/proximity module for receiving location/proximity information from a location/proximity device. The query system also includes a speech conversion module for converting the voice-based query in natural language form to text in natural language form and a natural language processing module for converting the text in natural language form to text in searchable form. The query system further includes a semantic engine module for converting the text in searchable form to a formal database query and a database-look-up module for using the formal database query to obtain a result related to the voice-based query in natural language form from a database.

Study on Similar Engineering Decision Problem Identification Based On Combination of Improved Edit-Distance and Skeletal Dependency Tree with POS

To improve the efficiency of solution analyzing of a new engineering decision problem in decision support system (DSS), this paper utilizes sentence similarity computing to identify solved engineering decision problems in DSS which new problems users input are similar to. On the basis of existing methods on Chinese sentence similarity computing and analyzing the characteristics of engineering decision problems expressed in natural language form, an approach with part of speech (POS) for similar engineering decision problem identification is proposed combining the methods based on improved edit-distance and skeletal dependency tree. According to the approach, the sentence similarity is defined as a weighted combination of expression similarity and semantic similarity. Finally, an instance is presented to illuminate the approach and experiment results are shown to evaluate the accuracy of the approach. Compared with other methods, the approach gives a better performance in identifying similar engineering decision problems.

Vietnamese Knowledge Base development and exploitation

At present, there have been many Vietnamese researches focusing on developing Knowledge Base (KB) for Vietnamese and exploiting its data to support others applications in wide areas. The paper introduces an approach which applies Support Vector Machine (SVM) for Vietnamese KB (VKB) development and a new solution for user questions answering based on Dependent Grammar also VKB data exploitation. For further research, our primary targets in other are to improve VKB quality and answer-finding algorithms on faster text-searching solutions also lower run-time progress, and mechanism to generate answers in natural language format for found result of these algorithms.

A natural-language approach to biomimetic design

AbstractThis paper summarizes various aspects of identifying and applying biological analogies in engineering design using a natural-language approach. To avoid the immense as well as potentially biased task of creating a biological database specifically for engineering design, the chosen approach searches biological knowledge in natural-language format, such as books and papers, for instances of keywords describing the engineering problem. Strategies developed to facilitate this search are identified, and how text descriptions of biological phenomena are used in problem solving is summarized. Several application case studies are reported to illustrate the approach. The value of the natural-language approach is demonstrated by its ability to identify relevant biological analogies that are not limited to those entered into a database specifically for engineering design.

Desiderata for ontologies to be used in semantic annotation of biomedical documents

A wealth of knowledge valuable to the translational research scientist is contained within the vast biomedical literature, but this knowledge is typically in the form of natural language. Sophisticated natural-language-processing systems are needed to translate text into unambiguous formal representations grounded in high-quality consensus ontologies, and these systems in turn rely on gold-standard corpora of annotated documents for training and testing. To this end, we are constructing the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-text biomedical journal articles that are being manually annotated with the entire sets of terms from select vocabularies, predominantly from the Open Biomedical Ontologies (OBO) library. Our efforts in building this corpus has illuminated infelicities of these ontologies with respect to the semantic annotation of biomedical documents, and we propose desiderata whose implementation could substantially improve their utility in this task; these include the integration of overlapping terms across OBOs, the resolution of OBO-specific ambiguities, the integration of the BFO with the OBOs and the use of mid-level ontologies, the inclusion of noncanonical instances, and the expansion of relations and realizable entities.

Symmetric Categorial Grammar

The Lambek-Grishin calculus is a symmetric version of categorial grammar obtained by augmenting the standard inventory of type-forming operations (product and residual left and right division) with a dual family: coproduct, left and right difference. Interaction between these two families is provided by distributivity laws. These distributivity laws have pleasant invariance properties: stability of interpretations for the Curry-Howard derivational semantics, and structure-preservation at the syntactic end. The move to symmetry thus offers novel ways of reconciling the demands of natural language form and meaning.

A knowledge-based question answering system for B2C eCommerce

The evolution of Business-to-Consumer (B2C) eCommerce has been formed through various generations. Last models of B2C eCommerce are comparative shopping systems that connect to multiple vendors’ databases and collect the information requested by the user. The comparative result obtained is then displayed in a tabular format in the user’s browser. Although this scenario is much better than the multiple manual site comparisons, user still needs to face inconsistent user interfaces when he is linked from the comparison site to the actual purchasing site for shopping. Therefore, user has to learn logics of each site’s user interface. In this paper, we propose a question answering system based on natural language processing techniques for retail (B2C) in eCommerce. This system gets a question in natural language formats, decomposes it to keywords, and extracts constraints automatically. Corresponding answers are then retrieved from the vendors’ Web sites by exploiting the question constraints.

Cutting the Strategy Diamond in High-Technology Ventures

This article presents the diamond, which extends received strategic management theory by integrating the positional view and the resource-based view, the formulation and implementation of strategy, and the firm's internal selection environment into one relatively simple conceptual framework of dynamic forces driving firm evolution. The article suggests that this framework can be helpful in stimulating further research to drive towards a dynamic theory of strategy. The strategy diamond facilitates understanding the dynamic interplays between strategy logic and strategy execution, and its jargon-free natural language form may facilitate strategic conversations and help inculcate a strategic mentality into organizations. This framework has been useful for senior executives in two major corporations. Four case studies highlight how the framework may help instill strategic discipline in high-technology ventures operating in extremely dynamic environments.

Cutting the Strategy Diamond in High-Technology Ventures

We present the diamond, which extends received strategic management theory by integrating the positional view and the resource-based view, the formulation and implementation of strategy, and the firm's internal selection environment into one relatively simple novel conceptual framework of dynamic forces driving firm evolution. We suggest that this framework can be helpful in stimulating further research to drive towards a dynamic theory of strategy. We show how the strategy diamond facilitates examining the dynamic interplays between strategy logic and strategy execution, and suggest that the jargon-free natural language form of the framework may facilitate strategic conversations and help inculcate a strategic mentality into organizations. We provide preliminary evidence that the framework has been useful for senior executives in two major corporations, and present systematic analysis of four case studies to highlight how the framework may help instill strategic discipline in high-technology ventures operating in extremely dynamic environments and seeking to achieve their minimum winning games.

Discovering semantic biomedical relations utilizing the Web

To realize the vision of a Semantic Web for Life Sciences, discovering relations between resources is essential. It is very difficult to automatically extract relations from Web pages expressed in natural language formats. On the other hand, because of the explosive growth of information, it is difficult to manually extract the relations. In this paper we present techniques to automatically discover relations between biomedical resources from the Web. For this purpose we retrieve relevant information from Web Search engines and Pubmed database using various lexico-syntactic patterns as queries over SOAP web services. The patterns are initially handcrafted but can be progressively learnt. The extracted relations can be used to construct and augment ontologies and knowledge bases. Experiments are presented for general biomedical relation discovery and domain specific search to show the usefulness of our technique.