What are the NLP Techniques?
The arena of artificial intelligence has constantly anticipated machines being able to imitate the operation and abilities of the human mind. Language is contemplated as one of the most imperative achievements of humans that have fast-tracked the progress of humanity. So, it is not a surprise that there is abundant work being carried out to integrate language into the pitch of artificial intelligence in the mode of Natural Language Processing (NLP).
The following are the 2 main techniques any NLP system utilizes to complete Natural Language Processing tasks. Indeed, language is a suite of valid sentences, but what makes a sentence justifiable? Syntax and Semantics.
a) Syntactic analysis (Syntax): The syntax is the arrangement of words in a sentence to make grammatical sense. The system assesses how the natural language aligns with the grammatical rules, through diverse techniques such as Lemmatization, Parsing, Sentence-breaking, Word segmentation, and Stemming.
b) Semantics: A semantic-based natural language system has an in-built representation of knowledge that can be enriched and improved. Once the regulations that categorize this knowledge have been implemented, new knowledge can easily be added through automatic learning mechanisms such as deep learning. So, Semantic analysis is the process of understanding the meaning and interpretation of words, signs, and sentence structure. Hence, the system applies computer algorithms to capture the meaning and recognition of how words and sentences are composed of techniques such as Word Sense Disambiguation, Named Entity Recognition (NER), and Natural Language Generation.
Sunartek’s technology is used for National security, Intelligence, Marketing, Recommendation engines, Customer insights, knowledge engines, Relevant search, and Cognitive computing. The solution automates, simplifies, and accelerates human understanding from an infinite pool of intercepted textual data.
• Categorizing content: Positive or negative (E.g. sentiment analysis), by operation, intention, or objective; by industry or other classifications for investigations and trending.
• Assembling content: To discover essential topics of discourse and/or to identify new topics.
• Fact-extraction: To fill databases with systematic information for evaluation, visualization, trending, or notifications. • Relationship-extraction: To fill out graph-based databases to investigate real-world relationships.
In a nutshell, NLP has transformed the way we network with computers and it’ll continue to do so in the future. With more researches being carried out in this field, Sunartek expects to witness more innovations that will make machines smarter at distinguishing and understanding the human language.
What is Natural Language Processing (NLP)?
Natural language processing (NLP) is a subdivision of artificial intelligence that assists computers to understand, interpreting, and influencing human language. With the fundamental objective to read, decrypt and comprehend human languages in a manner that is valuable, its pursuit is to fill the gap between human communication and computer understanding.The following are the 2 main techniques any NLP system utilizes to complete Natural Language Processing tasks. Indeed, language is a suite of valid sentences, but what makes a sentence justifiable? Syntax and Semantics.
a) Syntactic analysis (Syntax): The syntax is the arrangement of words in a sentence to make grammatical sense. The system assesses how the natural language aligns with the grammatical rules, through diverse techniques such as Lemmatization, Parsing, Sentence-breaking, Word segmentation, and Stemming.
b) Semantics: A semantic-based natural language system has an in-built representation of knowledge that can be enriched and improved. Once the regulations that categorize this knowledge have been implemented, new knowledge can easily be added through automatic learning mechanisms such as deep learning. So, Semantic analysis is the process of understanding the meaning and interpretation of words, signs, and sentence structure. Hence, the system applies computer algorithms to capture the meaning and recognition of how words and sentences are composed of techniques such as Word Sense Disambiguation, Named Entity Recognition (NER), and Natural Language Generation.
Natural Language Processing from Sunartek
With the aim to support various industries namely corporates, banks, institutions, defense, and government security organizations, we at Sunartek offer Natural Language Processing (NLP) solutions that are designed to extract meaningful information from textual contents. The system employs multiple levels of text mining, text extraction, and NLP techniques.Sunartek’s technology is used for National security, Intelligence, Marketing, Recommendation engines, Customer insights, knowledge engines, Relevant search, and Cognitive computing. The solution automates, simplifies, and accelerates human understanding from an infinite pool of intercepted textual data.
Beneficial features:
• Extracting entities: Companies, people, dollar amounts, key initiatives etc.• Categorizing content: Positive or negative (E.g. sentiment analysis), by operation, intention, or objective; by industry or other classifications for investigations and trending.
• Assembling content: To discover essential topics of discourse and/or to identify new topics.
• Fact-extraction: To fill databases with systematic information for evaluation, visualization, trending, or notifications. • Relationship-extraction: To fill out graph-based databases to investigate real-world relationships.
In a nutshell, NLP has transformed the way we network with computers and it’ll continue to do so in the future. With more researches being carried out in this field, Sunartek expects to witness more innovations that will make machines smarter at distinguishing and understanding the human language.