In this article, I will delve deeper into Natural Language Processing ( NLP for short ) for data mining and especially for the Knowledge Graph and search engines . To start with, I would like to get into the basics of Natural Language Processing.
You can find a detailed collection of articles on the topic of Knowledge Graph, semantic SEO and entities in the associated article series .
Table of contents [ Hide ]
1 What is Natural Language Processing?
2 Processes and core components of Natural Language Processing
3 What is Google’s Natural Language Processing API?
3.1 Syntax analysis via the Google NLP API
3.2 Entity analysis via the Google NLP API
3.3 Sentiment analysis via the Google NLP API
3.4 Content classification via the Google NLP API
3.5 Tip for nerds!
4 NLP in Entity Analysis
5 Word Embedding and Natural Language Processing
6 Use of NLP in search
7 BERT: Natural Language Processing mexico phone number data for the interpretation of search queries and documents
8 Natural Language Processing is the most important methodology for identifying entities
9 Natural Language Processing for the construction of the Knowledge Graph
10 Conclusion: Building the Knowledge Graph via Wikipedia, Wikidata and Knowledge Vault
10.1 Entity-based indexing: From content index to entity index
10.2 How does Google process information from Wikipedia for the Knowledge Graph?
10.3 How can Google identify and interpret entities from unstructured content?
11 tools
11.1 Explosion.ai
11.2 projector.tensorflow.org
11.3 Chrome Extension for Entity Extraction from Websites
What is Natural Language Processing?
Natural Language Processing (NLP) is a process for the automatic analysis and representation of human language. Natural Language Processing attempts to capture natural language and process it computer-based using rules and algorithms. NLP uses different types of machine learning (supervised machine learning) and unsupervised machine learning) to recognize the content and structure of texts and spoken language based on statistical models and vector space analyses . Newer NLP approaches also deal with methods for text generation and labeling using reinforcement learning via semi- or weakly supervised machine learning .
In other words, Natural Language Processing ( NLP) is the process of analyzing text, creating relationships between words, understanding the meaning of those words, and deriving a better understanding of the meaning of the words in order to generate information, knowledge, or new text.
Natural Language Processing can be used for the following application areas:
speech recognition (text to speech & speech to text)
Segmentation of previously captured speech into individual words, sentences and phrases.
Recognizing the basic forms of words and capturing grammatical information
Recognizing the functions of individual words in a sentence (subject, verb, object, article, etc.)
Extraction of the meaning of sentences and parts of sentences or phrases such as adjective phrases (e.g. too long), prepositional phrases (e.g. to the river) or noun phrases (e.g. the too long party)
Recognizing sentence contexts, sentence relationships and entities.
Natural Language Processing can be used for linguistic text analysis , mood and opinion analysis ( sentiment analysis ), translations as well as for language assistants , chatbots and underlying question and answer systems .