Natural Language Processing (NLP) is concerned with the interrelationship between natural human language and computer devices.NLP is one of the important aspects of computer linguistics, which also belongs to the field of computer science and artificial intelligence. Text mining is similar to NLP in that it is concerned with recognizing interesting and important patterns in textual data.

But the two are still different. First of all, the two concepts are not clearly defined (like "data mining" and "data science"), and they intersect to varying degrees, depending on who you're talking to. I think it's easiest to differentiate by level of insight. If raw text is data, then text mining is information and NLP is knowledge, i.e. the relationship between syntax and semantics.

While NLP and text mining are not the same thing, they are still closely related: they deal with the same raw data types, and there is a lot of crossover in their use.

Our aim is not an absolute or relative definition of the two; it is important to recognize that the preprocessing of data is the same under both tasks.

The effort to disambiguate is an important aspect of text preprocessing; we want to preserve the original meaning while eliminating the noise.

Here are some of the main steps in the task of processing text:

1. Data collection

Acquiring or creating a corpus, the source of which can be anything from email inboxes, to English Wikipedia articles or company financial reports, or even the works of Shakespeare.

2. Data Preprocessing

Preprocessing on the raw text corpus in preparation for a text mining or NLP task

Data preprocessing is divided into several steps, some of which may or may not be applicable to the given task. But it is usually one of tokenization, normalization and substitution.

3. Data Mining and Visualization

No matter what type of data we have, mining and visualization is an important step in probing for patterns

Common tasks may include visualizing word counts and distributions, generating wordclouds, and performing distance measurements

4. Model Building

This is where text mining and NLP tasks conduct the This is the main part of the text mining and NLP tasks, including training and testing

Feature selection and engineering will also be performed when appropriate

Language models: finite state machines, Markov models, vector space modeling of word meanings

Machine Learning classifiers: Simple Bayes, Logistic Regression, Decision Trees, Support Vector Machines, Neural Networks

Sequence models: Hidden Markov models, Recurrent Neural Networks (RNNs), Long Short Term Memory Neural Networks (LSTMs)

5. Model Evaluation

Does the model meet expectations?

The metrics will vary with the type of text mining or NLP task

The above views are for reference only, and there are not many technologies in natural language text preprocessing that have better results in China, representative ones are such as: Dr. Zhang Huaping of BeiJing Technology's NLPIR Big Data Semantic Intelligent Analysis Technology. NLPIR Big Data Semantic Intelligent Analysis Platform is based on the comprehensive needs of Chinese language data mining, and is designed to meet the needs of the Chinese community. The NLPIR Big Data Semantic Intelligent Analysis Platform is based on the comprehensive needs of Chinese data mining, integrating the research results of network precision acquisition, natural language understanding, text mining and semantic search, and for the whole technology chain of Internet content processing **** enjoy the development of the platform.

If you're interested in the development of this platform, please contact us.