Syntactic Analysis Of Text Research Articles

A Review for Semantic Analysis and Text Document Annotation Using Natural Language Processing Techniques

In today’s fast-growing world with rapid change in technology, everyone wants to read out the main part of the document or website in no time, with a certainty of an event occurring or not. However annotating text manually by domain experts, for example cancer researchers or medical practitioner becomes a challenge as it requires qualified experts, also the process of annotating data manually is time consuming. A technique of syntactic analysis of text which process a logical form S-V-O triples for each sentence is used. In the past years, natural language processing and text mining becomes popular as it deals with text whose purpose is to communicate actual information and opinion. Using Natural Language Processing (NLP) techniques and Text Mining will increase the annotator productivity. There are lesser known experiments has been made in the field of uncertainty detection. With fast growing world there is lot of scope in the various fields where uncertainty play major role in deciding the probability of uncertain event. However, syntactic analysis alone will not give desired results. Hence, it is required to use different techniques for the extraction of important information on the basis of uncertainty of verbs and highlight the sentence.

Development of knowledge­oriented system of machine translation based on the analytic­synthetic text processing

A method for automated syntactic text analysis based on the declarative representation of the rules of syntactic combinability was developed. In this method, in contrast to those existing, the tables of syntactic rules are used not only for the context analysis, but also for defining the subject, predicate, secondary parts of the sentence, as well as superphrase syntactic combinations. A method for software distribution of analytical-synthetic processing of a natural language text in machine translation systems was developed. The developed method, in contrast to the known methods, takes into account conditions of transition to parallel data processing both at the level of processing tasks and depending on the data type. The C# applications, where the developed methods for analytical-synthetic processing of multilingual Russian, Ukrainian and English texts were realized, were implemented by software. It was experimentally proved that the developed software for texts on military subject area allow reducing the number of errors of semantic character by 14–16 % in comparison with the existing machine translation systems through the automated text processing at the level of sign system and the introduction of super-phrase synthesis.

Using syntactic text analysis to estimate educational tasks’ difficulty and complexity

We suggest a routine for automatic estimation of complexity and difficulty of educational tasks. This routine is based on syntactic text analysis, phrases' predicative structures identification, and semantic network construction. Then we develop a mathematical model which employs a notion on semantic distance between notions---words to calculate the amount of knowledge in a semantic network. We show that the amount of knowledge in a semantic network is a measure in the set of all semantic networks, and the semantic distance makes this set the metric one.

Morpho-Syntactic Tagging of Text in “Baoule” Language Based on Hidden Markov Models (HMM)

The label text is a very important tool for the automatic processing of language. It is used in several applications such as morphological and syntactic text analysis, index-ing, retrieval, finished networks deterministic (in which all combinations of words that are accepted by the grammar are listed) or by statistical grammars (e.g., an n-gram in which the probabilities of sequences of n words in a specific order are given), etc. In this article, we developed a morphosyntactic labeling system language “Baoule” using hidden Markov models. This will allow us to build a tagged reference corpus and rep-resent major grammatical rules faced “Baoule” language in general. To estimate the parameters of this model, we used a training corpus manually labeled using a set of morpho-syntactic labels. We then proceed to an improvement of the system through the re-estimation procedure parameters of this model.