It is “the ability of machines to understand and interpret human language the way it is written or spoken.” The objective of NLP is to make computer/machines as intelligent as human beings in understanding language. The ultimate goal of it is to fill the gap how the people communicate (natural language) and what the computer understands (machine language).
How many types are there?
There are three different levels of linguistic analysis-
Syntax - What part of the given text is grammatically right.
Semantics - What is the meaning of the given text?
NLP deal with different aspects of language such as:
Phonology - It is a systematic organization of sounds in language.
Morphology - It is a study of words formation and their relationship with each other.
Approaches of NLP for understanding semantic analysis.
Distributional - It employs large-scale statistical tactics of Machine Learning and Deep Learning.
Frame-Based - The sentences which are syntactically different but semantically same are represented inside data structure (frame) for the stereotyped situation.
Theoretical - This approach builds on the idea that sentences refer to the real world (the sky is blue) and parts of the sentence can be combined to represent whole meaning.
Interactive Learning - It involves a pragmatic approach and the user is responsible for teaching the computer to learn the language step by step in an interactive learning environment.
The real success of it lies in the fact that humans deceive into believing that they are talking to humans instead of computers.
Importance of its Applications
With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time. Due to the presence of significant data (text) around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time. These tasks include other NLP applications like Automatic Summarization (to generate a summary of given text) and Machine Translation (translation of one language into another
What are the two best processes ?
In case the text is composed of speech, the speech-to-text conversion is performed. The mechanism of Natural Language Processing involves two processes -
NLU or Natural Language Understanding tries to understand the meaning of the given text. The nature and structure of each word inside text must be known for NLU. For understanding structure, NLU attempting to resolve following ambiguity present in natural language -
Lexical Ambiguity - Words have multiple meanings
Syntactic Ambiguity - Sentence is having multiple parse trees.
Semantic Ambiguity - Sentence having multiple meanings
Anaphoric Ambiguity - Phrase or word which is previously mentioned but has a different meaning.
Next, the sense of each word is understood by using lexicons (vocabulary) and set of grammatical rules. However, certain different words are having similar meaning (synonyms) and words having more than one meaning (polysemy).
Natural Language Generation
It is the process of automatically producing text from structured data in a readable format with meaningful phrases and sentences. The problem of natural language generation is hard to deal with. It is a subset of NLP Natural language generation divided into three proposed stages -
Text Planning - Ordering of the primary content in structured data is done.
Sentence Planning - The sentences are combined with structured data to represent the flow of information.
Realization - Grammatically correct sentences are produced finally to represent text.
Text Mining vs Natural Language Processing
It is responsible for understanding the meaning and structure of a given text. Text Mining or Text Analytics is a process of extracting hidden information inside text data through pattern recognition. It is used to understand the meaning (semantics) of given text data, while text mining is used to understand the structure (syntax) of given text data. As an example - I found my wallet near the bank. The task of it is to figure out in the end that ‘bank’ refers to a financial institute or ‘river bank.'
What is Big Data?
According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is described as big data is everything, quantified, and tracked.
Big Data For Natural Language Processing
Today around 80 % of total data is available in the raw form. Big Data comes from information stored in big organizations as well as enterprises. Examples include information about employees, company purchase, sale records, business transactions, the previous record of organizations, social media, etc. Though human uses language, which is ambiguous and unstructured to be interpreted by computers, yet with the help of NLP, this large unstructured data can be harnessed for evolving patterns inside data to know better the information contained in data. It can solve significant problems of the business world by using Big Data. Be it any business of retail, healthcare, business, financial institutions.
Deep Learning For NLP Applications
It uses a rule-based approach that represents Words as ‘One-Hot’ encoded vectors.
The traditional method focuses on syntactic representation instead of semantic representation.
Bag of words - classification model is unable to distinguish certain contexts.
3 Capability Levels of Deep Learning Intelligence
Expressibility - This quality describes how well a machine can approximate universal functions.
Trainability - How well and quickly a Deep Learning system can learn its problem.
Generalizability - How well the machine can perform predictions on data that it has not been trained.
There are of course other capabilities that also need to be considered in Deep Learning such as Interpretability, modularity, transferability, latency, adversarial stability, and security. But these are the main ones.
- Sentence/ Text classification - Relation extraction and classification - Spam detection - Categorization of search queries - Semantic relation extraction
What is the role of NLP in Log Analysis & Log Mining?
Its techniques are widely used in Log Analysis and Log Mining. The different techniques such as tokenization, stemming, lemmatization, parsing, etc. are used to convert log messages into structured form. Once logs are available in the well-documented form, log analysis, and log mining is performed to extract useful information and knowledge is discovered from the information. The example in case of error log caused due to server failure.
What is Log?
A collection of messages from different network devices and hardware in time sequence represents a log. Logs may be directed to files present on hard disks or can be sent over the network as a stream of messages to log collector. Logs provide the process to maintain and track the hardware performance, parameters tuning, emergency and recovery of systems and optimization of applications and infrastructure.
Log analysis is the process of extracting information from logs considering the different syntax and semantics of messages in the log files and interpreting the context with application to have a comparative analysis of log files coming from various sources for Anomaly Detection and finding correlations.
What is Log Mining?
Log Mining or Log Knowledge Discovery is the process of extracting patterns and correlations in logs to reveal knowledge and predict Anomaly Detection if any inside log messages.
What are the best Techniques?
Different methods used for performing log analysis are described below
It is one such technique which involves comparing log messages with messages stored in pattern book to filter out messages.
Normalization of log messages is done to convert different messages into the same format. This is done when different log messages have different terminology, but the same interpretation is coming from various sources like applications or operating systems.
Automated Text Classification & Tagging
Classification & Tagging of different log messages involves ordering of messages and tagging them with the various keywords for later analysis.
It is a kind of technique using Machine Learning Algorithms to discard uninteresting log messages. It is also used to detect an Anomaly in the ordinary working of systems.
What are the key Application of Natural Language Processing?
Apart from use in Big Data, Log Mining, and Log Analysis, it has other significant application areas. Although the term ‘NLP’ is not as popular as ‘big data’ ‘machine learning’ but we are using it every day.
Automatic Text Summarizer
Given the input text, the task is to write a summary of text discarding irrelevant points.
Sentiment-based Text Analysis
It is done on the given text to predict the subject of the text, eg, whether the text conveys judgment, opinion or reviews, etc
It is performed to categorize different journals, news stories according to their domain. Multi-document classification is also possible. A famous example of text classification is spam detection in emails. Based on the style of writing in the journal, its attribute can be used to detect its author's name.
Information extraction is something which proposes email program add events to the calendar automatically.
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Text Analytics Solutions
Text Analytics or Text Mining refers to the automatic extraction of high-value information from text. The extraction involves structuring the input text, discovering patterns in the structured data and interpreting the results. Text Mining process involves Machine Learning, Statistics, Data Mining, and Computational Linguistics. Sentiment Analysis Using Machine Learning, NLP, and Deep Learning At XenonStack, we process and analyze textual content and provide valuable insights by transforming the raw data into structured, usable information. XenonStack's Text Analytics Solutions offers Part-of-Speech (PoS) tagging, Clustering, Classification, Information Extraction, Sentiment Analysis and more.
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Sentiment Analysis helps to apprehend people's reaction to situations. Sentiment Analysis is used to predict person's emotions like angry, happy, sad, disgust etc. XenonStack offers Sentiment Analysis and Intent Analytics using Machine Learning, Natural Language Processing, Deep Learning, Supervised Learning Algorithms, Keras with Tensorflow. Enhance the customer experience through Sentiment Analysis in Business.
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