Elements of XPath-like languages in problems of constructing semantic XML models of natural language texts

The subject of the research is the possibility of using XPath-like micro-languages of programming in the generation systems of programs of the PGEN ++ class for the selection and completion of XML-models describing the plan for solving the original problem, according to which the solver program is generated. It is supposed to build such models according to the description of the problem in natural language, thus, we are talking about elements of artificial intelligence technologies. XPath-like language works in the layer of regular-logical expressions (highlighting elements of the primary XML document), performing primary processing of the data obtained in the layer of grammatical parsing of the source text. In addition, XPath-like elements are used to render the final XML models. The standard natural language parsing libraries are used. Non-standard XPath query language extensions are used. For the first time, the idea of expanding the XPath query language to the level of an algorithmic language by introducing the minimum required number of syntactic elements is proposed. It is also proposed to use syntactic elements with an XPath-like structure as both generating and controlling weak constraints of the process of direct inference of final semantic XML models.

Agile-Model-Based Sentiment Analysis From Social Media

To study state-of-the art associated to Twitter mining replica as well as prognostic analytic by means of Agile Software Engineering. To recognize sentiment analysis by means of agile knowledge. To obtain as well as analysis given repository for classifying sentiments into positive, negative and neutral emotions. Analysing of all the tweets obtained from the twitter keywords as positive, negative or neutral opinions and comparing all the keywords to judge which keyword is better, there is a requirement to improve from the conventional ways of sentiment analysis. This paper emphasizes on the implementation of an algorithm for automatic classification of text into positive, negative or neutral by fetching the live tweets from twitter server by using twitter API. Graphical representation of the sentiment for the purpose of comparison in the form of pie chart and bar graph. Scan the twitter and fetching the Live Tweets from Twitter server using Twitter4J Advance Java Interface and implementing the Stanford NLP Library (Natural Language Parsing) using Advance Java for classifying the tweets into positive, negative and neutral tweets.

Parsing

This chapter introduces key concepts and techniques for natural-language parsing: that is, finding the grammatical structure of sentences. The chapter introduces the fundamental algorithms for parsing with context-free (CF) phrase structure grammars, how these deal with ambiguous grammars, and how CF grammars and associated disambiguation models can be derived from syntactically annotated text. It goes on to consider dependency analysis, and outlines the main approaches to dependency parsing based both on manually written grammars and on learning from text annotated with dependency structures. It finishes with an overview of techniques used for parsing with grammars that use feature structures to encode linguistic information.