Leading-edge Natural language processing
Natural language processing abbreviated as NLP is a sub-field of Artificial Intelligence that deals with automatic manipulation of speech in different forms. Everbody knows that machines don’t understand human language but what if they could? This question gave rise to more and more research in the field so much so that almost every next year a new research paper is published describing a new methodology or a new model aiming to make the process more and better as well as accurate. As of now, Google has come with the BERT which is the state of art model till 2020, still there is so much research going in the field that it won’t be a surprise if a new better model comes along in 2020.
Application of NLP
Sentiment Analysis is relevant mining of content that recognizes emotional data in the source material. In today's world a sentiment analyser is usally used over different social media sites. This is because there is a plethora of data being posted everyday and analysing this huge data has proven to be very useful in recent years. Deriving emotions behind this large textual data is called sentiment analysis.
Text Classification is a technique in which we assign targets or categories to textual data in accordance with the context of the data. This method is included in the fundamentals of NLP techniques. Sentiment analysis is actually an application of text classification. Other applications of text classification include spam detection, also a faster emergency response system can be made by classifying panic conversations on social media.
Email spam and malware filtering
When spammer starts sending spam emails it prevents the user from making full and good use of time, storage capacity and network bandwidth. The huge volume of spam emails flowing through the computer networks has destructive effects on the memory space of email servers, communication bandwidth, CPU power and user time. Machine learning methods of recent are being used to successfully detect and filter spam emails.
Chatbots are computer programs that interact with users using natural languages. This technology started in the 1960s; the aim was to see if chatbot systems could fool users that they were real humans. However, chatbot systems are not only built to mimic human conversation and entertain users. Chatbots could be used to meet some specific needs of a business.
Effort to access other language documents leads to the development of a machine translation system which involves lots of heterogeneous features and its implementations. Information professionals have widely used the advantages of machine translation for satisfying their user's needs. Machine Translation methods are different and each has its own benefits and drawback. No translations tools can generate an exact version of source language but give the gist of information which can utilize to find the type of information contained in the source text. Sometimes, it is necessary to perform post-editing by in-house linguistic after generating translation output with a translation engine.
Algorithms of question-answering in a computer system oriented on input and logical processing of text information are presented. A knowledge domain under consideration is the social behavior of a person. A database of the system includes an internal representation of natural language sentences and supplemental information. The answer Yes or No is formed for a general question. A special question containing an interrogative word or group of interrogative words permits to find a subject, object, place, time, cause, purpose and way of action or event. Answer generation is based on identification algorithms of persons, organizations, machines, things, places, and times.
Named Entity Recognition
Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognize named entities. Despite recent achievements, we still face limitations with correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results.
The speech recognition system at its core translates the spoken utterances to text. There are various real-life examples of speech recognition systems. For example, Amazons Alexa, which takes the speech as input and translates it into text. The advantage of using a speech recognition system is that it overcomes the barrier of literacy.
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