Book
Actes de la 18e conference sur le Traitement Automatique des Langues Naturels. Actes de la 15e Rencontre des Etudiants Cercheurs en Informatique pour le Traitement Automatique des Langues. France, Montpellie, 27th June - 1st July 2011
The book contains the proceedigs of the 18th International Conference on Automatic Processing of Natural Langage (France, Montpellie, 27th June - 1st July 2011).
The paper describes the structure and possible applications of the theory of K-representations (knowledge representations) in bioinformatics and in the development of a Semantic Web of a new generation. It is an original theory of designing semantic-syntactic analyzers of natural language (NL) 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 by V. Fomichov (Springer, 2010). The first part of the theory is a formal model describing a system consisting of ten operations on conceptual structures. This model defines a new class of formal languages – the class of SK-languages. The broad possibilities of constructing semantic representations of complex discourses pertaining to biology are shown. A new formal approach to developing multilingual algorithms of semantic-syntactic analysis of NL-texts is outlined. This approach is realized by means of a program in the language PYTHON.
The paper describes the structure and possible applications of the theory of K-representations (knowledge representations) in bioinformatics and in the development of a Semantic Web of a new generation. It is an original theory of designing semantic-syntactic analyzers of natural language (NL) 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 by V. Fomichov (Springer, 2010). The first part of the theory is a formal model describing a system consisting of ten operations on conceptual structures. This model defines a new class of formal languages – the class of SK-languages. The broad possibilities of constructing semantic representations of complex discourses pertaining to biology are shown. A new formal approach to developing multilingual algorithms of semantic-syntactic analysis of NL-texts is outlined. This approach is realized by means of a program in the language PYTHON.
The paper presents the rationale for the decisions that were taken in the set-up and further development of a learner corpus of student texts written in English by Russian learners of English, the only Russian learner corpus in the open access. The tool of manual expert annotation is in the focus of the present observations, and after introducing categorization of errors applied in annotation, the complicated cases that arose in annotation practices have been looked into followed by comparison of the annotation statistics over the three stages in the corpus development. For that purpose, texts annotated by different groups of participants in the process of two experiments were used to spot the problematic areas in annotation. The main pedagogical applications of the learner corpus in teaching EFL – the opportunities to create automated training exercises and placement and progress tests custom-made for specific groups of students - are outlined in the concluding part of the paper.
he paper presents a framework for fast text analytics developed during the Texterra project. Texterra is a technology for multilingual text mining based on novel text processing methods that exploit knowledge extracted from user-generated content. It delivers a fast scalable solution for text mining without the expensive customization. Depending on use-cases Texterra could be utilized as a library, extendable framework or scalable cloudbased service. This paper describes details of the project, use-cases and results of evaluation for all developed tools. Texterra utilizes Wikipedia as a primary knowledge source to facilitate text mining in arbitrary documents (news, blogs, etc). We mine the graph of Wikipedia’s links to compute semantic relatedness between all concepts described in Wikipedia. As a result, we build a semantic graph with more than 5 million concepts. This graph is exploited to interpret meanings and relationships of terms in text documents. In spite of large size, Wikipedia doesn’t contain information about many domain-specific concepts. In order to increase applicability of the technology we developed several automatic knowledge extraction tools. These tools include systems for knowledge extraction from MediaWiki resources and Linked Data resources, as well as system for knowledge base extension with concepts described in arbitrary text documents using original information extraction techniques. In addition, utilization of information from Wikipedia allows easily extend Texterra for support of new Natural languages. The paper presents evaluation of Texterra applied for different text processing tasks (part-of-speech tagging, word sense disambiguation, keyword extraction and sentiment analysis) for English and Russian.
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.
A framework for fast text analysis, which is developed as a part of the Texterra project, is described. Texterra provides a scalable solution for the fast text processing on the basis of novel methods that exploit knowledge extracted from the Web and text documents. For the developed tools, details of the project, use cases, and evaluation results are presented.
Subject index, or back-of-the-book index, is a device intended to provide an easy access to relevant fragments of a text document. Subject indexes usually contain particular single-word and multi-word terms from the corresponding documents. Such indexes are especially useful for reading large documents with specialized terminology, as well as educational texts in difficult scientific and technical areas. The central problem of back-of-the-book indexing is recognition of terms to be included into the index. The paper describes a method developed for extracting and filtering terms from a given educational scientific text, with the purpose of reliable term selection in computer indexing systems. The method is primarily based on rules with lexico-syntactic patterns representing linguistic information about terms and typical contexts of their usage in Russian scientific and educational texts; simple occurrences statistics of terms is used as well. Experimental evaluation of the method has shown a considerable increase of precision and recall of term extraction compared with the widely-used standard techniques.
Proceedings of Third Workshop "Computational linguistics and language science"
The present article continues the investigation of the Soqotri verbal system undertaken by the Russian-Soqotri fieldwork team. The article focuses on the so-called “weak” and “geminated” roots in the basic stem. The investigation is based on the analysis of full paradigms (perfect, imperfect and jussive) of more than 170 “weak” and “geminated” Soqotri verbs.