Growing Importance of Natural Language Processing
The impact of Natural Language Processing (NLP) on the efficiency and decision-making quality within a government & non-government organization cannot be overstated. In today’s time, information has become the main currency of the modern world. Hence, with ‘interpretation of data’ being the critical element, let’s look at understanding NLP and its importance in-depth.
What is Natural Language Processing (NLP)?
Natural Language Processing is referred to as a branch of artificial intelligence that deals with the communication between computers and humans utilizing the natural language. The fundamental objective of NLP is to read, decrypt and comprehend human languages in a manner that is valuable.
Significance and usage of NLP
a) Textual data in large volumes:
Natural language processing supports computers to communicate with humans in their own language and balances other language-related tasks. For instance, NLP makes it feasible for computers to read scripts, hear speech, decipher it, determine sentiments, and define which parts are important.
Today’s machinery can analyze more language-based information than humans, without exhaustion and in a reliable, unbiased way. Considering the astounding amount of unorganized data that are produced every day, from medical histories to social media, computerization will be essential to completely evaluate text and speech data proficiently.
b) Structuring an absolute unstructured data source:
Human language is remarkably complex and diverse. We express ourselves in infinite and diverse ways, both vocally and in writing. There are hundreds of languages and dialects & within each language is a distinct set of grammar and syntax guidelines, terms, and slang.
While supervised, unsupervised learning, and precisely deep learning, are now extensively used for molding human language, there’s a requirement for syntactic and semantic consideration with domain expertise as well, which do not necessarily exist in these machine-learning methods. Thus, Natural Language Process is imperative because it helps determine obscurity in language and adds beneficial numeric structure to the information for several downstream applications, like speech detection or text analytics.
Apart from the above, NLP also plays a crucial role in automatically categorizing and matching incoming data towards indicators and warnings being monitored. It also provides alerts on trending matters, keywords, or topics that may indicate emerging schemes, techniques, and procedures.
Hence, by adding NLP to military processes, it will indeed help to highlight trends in the data for further exploration by analysts and interpret complex circumstances by assessing future probabilities.
Natural Language Processing via 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.
The following are the 2 main techniques the system utilizes to complete Natural Language Processing tasks.
a) Syntactic analysis (Syntax):
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.
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.
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.