Natural Language Processing (NLP) Techniques and Applications Overview

What is Natural Language Processing(NLP)?

Natural Language Processing (NLP) 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 NLP is to fill the gap how the people communicate (natural language) and what the computer understands (machine language).

There are three different levels of linguistic analysis done before performing NLP-

• Syntax — What part of the given text is grammatically right.
• Semantics — What is the meaning of the given text?
• Pragmatics — What is the purpose of the 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 NLP lies in the fact that humans deceive into believing that they are talking to humans instead of computers.

Importance of Natural Language Processing 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)

Process of Natural Language Processing

In case the text is composed of speech, the speech-to-text conversion is performed.

The mechanism of Natural Language Processing involves two processes -

• Natural Language Understanding
• Natural Language Generation

Natural Language Understanding

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.

Difference Between NLP and Text Mining

Natural language processing 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.

Natural language processing 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 NLP 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.

NLP 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.

Applications of Deep Learning in NLP

Deep Learning Algorithms NLP Usage

Neural Network — NN (feed)

• Part-of-speech Tagging
• Tokenization
• Named Entity Recognition
• Intent Extraction

Recurrent Neural Networks -(RNN)

• Machine Translation
• Question Answering System
• Image Captioning

Recursive Neural Networks

• Parsing sentences
• Sentiment Analysis
• Paraphrase detection
• Relation Classification
• Object detection

Convolutional Neural Network -(CNN)

• Sentence/ Text classification
• Relation extraction and classification
• Spam detection
• Categorization of search queries
• Semantic relation extraction

Difference Between Classical NLP & Deep Learning NLP

Image Source — blog.aylien.com

NLP For Log Analysis and Log Mining

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.

You May also Love to Read Understanding Log Analytics, Log Mining & Anomaly Detection

What is Log Analysis?

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.

Role of NLP in Log Analysis & Log Mining

Natural Language processing 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.

Overview of Natural Language Processing 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.

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.

You May also Love to Read Log Analytics With Deep Learning & Machine Learning

Diving into Natural Language Processing Applications

Natural language processing is a complex field and is the intersection of Artificial Intelligence, computational linguistics, and computer science.

Getting started with Natural Language Processing

The user needs to import a file containing text written. Then the user should perform the following steps for natural language processing.

Sentence Segmentation

Mark met the president. He said:”Hi! What’s up -Alex?”

• Sentence 1 — Mark met the president.
• Sentence 2 — He said: “Hi! What’s up — Alex?”

Tokenization

My phone tries to ‘charging’ from ‘discharging’ state.

Stemming/Lemmatization

Drinking, Drank, Drunk

Part-of-Speech tagging

If you build it he will come.

• IN — prepositions and subordinating conjunctions.
• PRP — Personal Pronoun
• VBP — Verb Noun 3rd person singular present form.
• PRP- Personal pronoun
• MD — Modal Verbs
• VB — Verb base form

Parsing

Mark and Joe went into a bar.

• (S(NP(NP Mark) and (NP(Joe))
• (VP(went (PP into (NP a bar))))

Named Entity Recognition

Let’s meet Alice at 6 am in India.

• Let’s meet Alice at 6 am in India
• Person Time Location

Coreference resolution

Mark went into the mall. He thought it was a shopping mall.

• Sentence segmentation — It identifies sentence boundaries in the given text, i.e., where one sentence ends and where another sentence begins. Sentences are often marked ended with punctuation mark ‘.’
• Tokenization — It identifies different words, numbers, and other punctuation symbols.
• Stemming — It strips the ending of words like ‘eating’ is reduced to ‘eat.’
• Part of speech (POS) tagging — It assigns each word in a sentence its own part-of-speech tag such as designating word as noun or adverb.
• Parsing — It involves dividing given text into different categories. To answer a question like this part of sentence modify another part of the sentence.
• Named Entity Recognition — It identifies entities such as persons, location and time within the documents.
• Co-Reference resolution — It is about defining the relationship of given the word in a sentence with a previous and the next sentence.

Key Application Areas 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 NLP every day.

Given the input text, the task is to write a summary of text discarding irrelevant points.

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 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.

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.

Build, Deploy and Manage Intelligent Chatbots to interact naturally with a user on Website, Apps, Slack, Facebook Messenger and more. XenonStack Chatbot Solutions uses Cognitive Intelligence that enables bot to see, hear, and interpret in more human ways.

Originally published at https://www.xenonstack.com on February 18, 2020.