Natural Language Processing
By: Gauri Mhaske
Tags: Machine Learning, Data Science, Naturallanguageprocessing, Computer Science

Natural language process is an application of artificial intelligence where machines are programmed in such a way that they can understand human language and derive meaning from it in a cognitive manner. The main objective of NLP is to program machines and make them capable of reading, understanding and ciphering the human language in a manner that is accurate and beneficial to the human kind.

Natural language processing attempts on applying algorithm to extract knowledge and meaning from unstructured language data. Data generated from tweets, declaration, chats are the data that is often used in NLP. This data is messy hard to decipher and even harder to manipulate nevertheless it is made possible with the help of NLP. The entire field of NLP can be divided into 2 segments. — language processing and language generation. Language processing refers to the analysis of language with the intention of extracting accurate meaning representation, while language generation refers to the construction of understandable language phrases from a representation. The function of Natural Language Processing is equivalent to the role of reader/listener, while the function of Natural Language Generation is that of the writer/speaker. While in theory their functions might look alike Language, generation plans its goal and what system is supposed to generate at each step of interaction.

Brief history of NLP-:

Research in natural language processing has been happening for several decades dating back to the late 1940s. Machine translation (MT) was the first computer-based Research done in natural language processing. Machine translation (MT) was the primary computer-based application associated with NLP. NLP was first implemented in the form of machine translation during World war 2 to crack enemy code. Early work in MT took the simplistic approach, Systems were developed by used dictionary-lookup for appropriate words for translation and reordered the words after translation to fit the word-order rules of the target language, without taking into account the ambiguity intrinsic in natural language which gave poor results. In 1957 when Chomsky published his work on syntactic structure emphasizing on generative grammar the field gained better insight. During this era various NLP applications started to emerge like speech recognition etc. By the late 1980s, symbolic approaches had been implemented to deal with many significant problems in NLP and statistical approaches proved to be complementary in many respects to symbolic approaches. In the last ten years of the millennium, the sector was growing rapidly. This can be attributed to: a) increased availability of huge amounts of electronic text;

b) availability of computers with increased speed and memory;

c) the arrival of the web

Technology

Syntactic and semantic analysis are the 2 main techniques used in NLP. Let’s look at some syntactic analysis and techniques used in it in detail.

Lemmatization -: This technique involves taking various forms of a word and reducing it to its base form. It standardizes synonyms to single word and then extracts its meaning.

Word segmentation: a large piece of text is taken and broken into distinct pieces.

Parsing: The grammatical analysis of sentences is done in this technique.

Stemming: varied words are broken down to their root form.

Morphological segmentation: — Complex word are divided into smaller units called morphemes of word.

Sentence breaking-: boundaries of larger sentences are broken down into smaller units.

Following are some of semantic analysis technique.

Named entity recognition (NER): Parts of text are labelled and classified in smaller groups.eg of such groups is names.

Word sense disambiguation: In this method word is recognized and given a meaning.

Natural language generation -: Database is used to derive semantic methods to understand complex words.

Natural Language processing is considered one of the most difficult techniques due its complex nature. It is not an easy task to pass information to computer using natural language as language is tricky some aspects can pose a major challenge like sarcastic remarks, idioms etc. The ambiguous and imprecise nature of our language makes it difficult for the system to comprehend it.

Nevertheless, with the proper training data set and programming various programs can smoothly run with the help of NLP.

Applications of NLP

· NLP has vast application throughout the field. NLP enables to detect and predict if a person has any disease based on electronic health records and speech of the subject. The ability of such programs is in healthcare being looked into from diseases like depression to heart conditions.

· Organization perform sentimental analysis to know opinion of their consumers through social media like tweets comments etc. about their products and help them enhance their merchandises catering to their preferences.

· Emails apps like yahoo google etc filter spam email into spam section even before it entering your Inbox.

· NLP group at MIT created a fake news detector where using sentimental analysis it was studied whether the source of news was reliable or not should it be trusted doing so biased news channels were weeded out.

· Various assistants like Siri, Alexa are an application of NLP they respond to voice prompts convert audio into text and even re converting the understand our command and give us the required answer using various NLP techniques. They are capable of performing regular as well as complex commands like making call, finding cafes or even weather forecasting.

· NLP is also used by financial traders as being updated with the latest trends and knowing what is the buzz is right now can be very important which helps them make decisions on where to invest etc.

· as we have seen NLP has a vast area of applications and it can be used and incorporated into many different technologies.

Future of Natural language processing

Natural Language Processing plays a critical role in supporting machine-human interactions. With more research being done in this field more ground breaking discoveries are expected in this field which will improve the functionality of our gadgets and applications.