Browse algorithms related to text analysis:

Text analysis or natural language processing (NLP) is a computer in an intelligent and useful way, in order to understand meaningful methods, analysis and analysis from human language. By using NLP, developers can organize and build knowledge to execute automatic summarization, translation, named entity recognition, relationship extraction, emotion analysis, speech recognition and topic partitioning tasks. Reading: Introduction to Natural Language Processing

A priori analysis - as a name, a priori (before), we analyzed (space and time) before running a specific algorithm / system. So basically, this is a theoretical analysis of the algorithm. Under the assumption that all other factors (eg processor speed) are constant measure the impact on algorithm efficiency and implementation assumption. Apostiari analysis - Apostiari analysis algorithm for performing execution only on physical systems. In order to implement the selected algorithm we used a programming language to run on the target computer machine. This directly depends on system configuration and system-to-system variation

Algorithmic analysis is the process of analyzing the problematic algorithm based on the time and size (which realizes the memory size) to cope with the capability. However, the main problem is an analysis algorithm that requires time and performance. Normally, we will run the next type of analysis - to solve the problem, because we can run the program with limited memory space available, enough system, and vice versa, It is necessary to consider the complexity of time and space. In this case, we compare the bubble sort, sort merge. No additional memory bubble sort, no merge sort, but additional space is required. Compared to merge sort, when the program needs to run in a very limited memory environment, the time complexity of the bubble sort is high, but although we may need to use the bubble sort.

In each field of science, the necessity of an effective algorithm has its own problem. Normally research related issues in the field together. Some examples of classes include algorithms, algorithmic medicine, machine learning, encryption, algorithms that combine data compression and analysis techniques, numerical algorithms, graphic algorithms, string algorithms, computational geometric algorithms, merge and sort algorithms , Search algorithm. Fields often overlap, field algorithm advances can improve other fields (sometimes completely irrelevant fields) fields. For example, dynamic programming is now widely used to solve problems in many areas, to optimize the industry and the resource consumption of the present invention