Метод семантичского поиска специалистов с определенным набором компетенций
The report deals with the methodology of building a system to perform search for specialists satisfying a defined set of competencies. The proposed search method is based on natural language texts analysis.
Proceeding of the 15th International Conference on Artificial Intelligence: Methodology, Systems, Applications , AIMSA 2012, Varna, Bulgaria, September 12-15, 2012.
This paper is an overview of the current issues and tendencies in Computational linguistics. The overview is based on the materials of the conference on computational linguistics COLING’2012. The modern approaches to the traditional NLP domains such as pos-tagging, syntactic parsing, machine translation are discussed. The highlights of automated information extraction, such as fact extraction, opinion mining are also in focus. The main tendency of modern technologies in Computational linguistics is to accumulate the higher level of linguistic analysis (discourse analysis, cognitive modeling) in the models and to combine machine learning technologies with the algorithmic methods on the basis of deep expert linguistic knowledge.
Compared with the area of spatial relations force interactions haven’t been in the limelight of attention of ontologists working on natural language processing. This article gives an example of text meaning representation based on the ontology and the lexicon of force interactions.
The paper describes a new method of constructing recommender systems with natural-language interface. This method is based on the theory of K-representations (knowledge representations) - a new theory of designing semantic-syntactic analyzers of natural language texts with the broad use of formal means for representing input, intermediary, and output data. The current version of the theory is set forth in a monograph (the author is V.A. Fomichov) published by Springer in 2010. The stated approach is implemented in the programming environment PHP + MySQL: an experimental recommender system has been developed.
The paper describes a new method of constructing semantic expansions of search requests about the achievements and failures of active systems (organizations, people) for improving the results of Web search. This method is based on the theory of K-representations (knowledge representations), proposed by V.A. Fomichov - a new theory of designing semantic-syntactic analysers of natural language texts with the broad use of formal means for representing input, intermediary, and output data. The method uses an original formal model of a goals base – a knowledge base containing the information about the goals of active systems. The stated approach is implemented with the help of the Web programming language Java: an experimental search system AOS (Aspect Oriented Search) has been developed and tested.
The paper describes a new method of constructing semantic expansions of search requests (of generalized character) for improving the results of Web search. This method is based on the theory of K-representations - a new theory of designing semantic-syntactic analyzers of natural language texts with the broad use of formal means for representing input, intermediary, and output data. The stated approach is implemented with the help of the programming language «Java»: an experimental search system AOS (Aspect Oriented Search) has been developed.
The CCIS series is devoted to the publication of proceedings of computer science conferences. Its aim is to efficiently disseminate original research results in informatics in printed and electronic form. While the focus is on publication of peer-reviewed full papers presenting mature work, inclusion of reviewed short papers reporting on work in progress is welcome, too. Besides globally relevant meetings with internationally representative program committees guaranteeing a strict peer-reviewing and paper selection process, conferences run by societies or of high regional or national relevance are also considered for publication.
Concept discovery is a Knowledge Discovery in Databases (KDD) research field that uses human-centered techniques such as Formal Concept Analysis (FCA), Biclustering, Triclustering, Conceptual Graphs etc. for gaining insight into the underlying conceptual structure of the data. Traditional machine learning techniques are mainly focusing on structured data whereas most data available resides in unstructured, often textual, form. Compared to traditional data mining techniques, human-centered instruments actively engage the domain expert in the discovery process. This volume contains the contributions to CDUD 2011, the International Workshop on Concept Discovery in Unstructured Data (CDUD) held in Moscow. The main goal of this workshop was to provide a forum for researchers and developers of data mining instruments working on issues with analyzing unstructured data. We are proud that we could welcome 13 valuable contributions to this volume. The majority of the accepted papers described innovative research on data discovery in unstructured texts. Authors worked on issues such as transforming unstructured into structured information by amongst others extracting keywords and opinion words from texts with Natural Language Processing methods. Multiple authors who participated in the workshop used methods from the conceptual structures field including Formal Concept Analysis and Conceptual Graphs. Applications include but are not limited to text mining police reports, sociological definitions, movie reviews, etc.