01110000 01101100 01010100 01110110 01100101 01110010 01100001 As we have difficulty reading the above binary compared to reading a human language, similarly computers have difficulty understanding human speech that may contain variability of spoken word, language words, dialects, mispronunciation, speech impediments, etc.
What is Natural Language Processing (NLP)?
Machines can understand the human language. It could be in the form of speech/text. NLP uses ML (Machine Learning) to meet the objective of Artificial Intelligence. NLP's ultimate goal is to bridge how people communicate (natural language) and what computers can understand.
If we mathematically represent it contains the following terms:
NLP : NLP (Natural Language Processing) is in charge of entire processes such as decisions and actions.
NLG : NLG (Natural Language Generation) creates the human language text from the structured data that the system generates to answer.
A subset technique of Artificial Intelligence which is used to narrow the communication gap between the Computer and Human. Click to explore about, Evolution and Future of NLP
Why do we need Natural Language Processing?
Specific jobs, such as Automated Speech and Automated Text Writing, can be completed in less time using NLP. As there is a lot of data (text) available, let's take advantage of the computer's untiring willingness and ability to execute multiple algorithms to complete tasks quickly.
What are the challenges of Natural Language Processing?
Some of the challenges of NLP are given below:
Meaning-full Words: Text may contain Homographs such as a single spelling of a word that can have several meanings and Homonyms that two words that sound similar yet have a distinct meaning. Moreover, a complete sentence may have several grammatical syntax, desired message intent, and semantic meaning. It makes NLP extremely difficult. On the other hand, humans have a clear grip on the context of each word used and thus comprehend, but it is difficult for machines.
Parts of Speech and Phrase Structure: Language issues are not just words but become complicated in phrase and sentence structure. Such as a sentence may contain various parts of speech with different roles. When everything comes together, new challenges arise, such as grammatical conventions words dependency on each other.
Deal with Complex data: Over the years, technology has changed our world and everyday life by creating amazing tools and resources. There has been a tremendous increase in the amount of data we are generating, uploading, or utilizing through all these cycles. It isn't easy and getting more complex to deal with this abundant data. To study this data manually is not a duck soup. Analyzing the context behind the text and then analyzing it to gather or extract something valuable from it requires a lot of manual labour. Whether it is routing calls to that particular team in IVR or writing a summary or a brief description after going through long texts and papers needs human intervention. What if we make computers smart enough to understand human language, to solve this kind of problem, and to convert this hard work to smart work, NLP can be used.
It is very important to consider all the challenges before building an NLP system to take corrective measures to reduce such issues.
What are the solutions for Natural Language Processing?
Smart assistants like Siri and Alexa are now very common and can assist in almost all purposes in daily life.
The concept of how they work is not easy but understandable, which is Automatic language detection. The instructions are given in speech. Get our job done based on what we said. NLP enables the machine to understand your words and the context behind them. Machine learning algorithms and NLP are used here to process, "understand", and respond to human language, both written and spoken.
Natural language processing (NLP) is a field of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws on a wide range of fields, including computer science and computer languages, to bridge the gap between human communication and computer literacy. It makes the process of breaking language into such a comprehensible and valuable format that it is easy to understand for both computer programs and individuals.
NLP is important because it helps resolve language ambiguities and adds useful numerical structure to the data for many down-to-earth applications, such as speech recognition or text statistics. The basic functions of NLP include tokens and segregation, lemmatization/stemming, partial marking, language acquisition, and semantic relationship identification.
What are the Fundamentals of NLP for Text Analysis?
In NLP, text preprocessing is the first and most important step in building a model. Some basic steps that are performed are as follows:
Sentence Tokenization: Splitting of sentences in the paragraph.
Word Tokenization: Splitting words in a sentence.
Lower Case conversion: Converting upper case into lower case.
Removing Punctuations: In this phase, punctuation will be removed.
Stop Words Removal: Commonly used words like a, an, the, etc., are called stopwords, and they should be removed before applying NLP as they don’t help in distinguishing.
Stemming or Lemmatization: Stemming is a process of transforming a word to its root form.
Whereas lemmatization reduces the words to a word existing in the language. One of these two is done while preparing data for NLP. Mostly lemmatization is preferred because lemmatization does a morphological analysis of the words.
What are the best practices of Natural Language Processing (NLP)?
The best practices of Natural Language Processing are listed below:
Establish Good Team
It will decide the resources and skills that a project needs and who is responsible for training, testing, and updating the model. A team of 2-5 people is required for an enterprise use case. 1-2 people who have data science skills and can understand business goals. They will work on data, model, and output. Another half of the team will consist of subject matter experts representing the business.
A user-centred design approach is required for successful virtual agents. It's critical to learn as much as you can about your target end-users and how they'll want or need to communicate with your business through a conversational platform. What circumstances will drive users to engage with a conversational solution? What queries or requests will the solution be requested to answer? The answers to these questions will determine how your classifier is trained.
The intent is a goal in the user’s mind when he is writing a text or speaking something. It is very important to understand user and group examples accordingly. In most underperforming models, the most prevalent underlying issue is intents that are either too wide or too particular (or a combination of the two). It's not always clear the "proper" level of specificity to cover. There is an unavoidable degree of topic overlap in some domains. Consider intents to be the verb/action part of a statement in general.
Discarding Stop Words
Stopwords are the most regularly occurring words in a text which do not give any valuable information. There, this, where, and others are examples of stopwords.
Assess Volume and Distribution
The Watson Assistant documentation advises at least five examples per intent, while domain-specific solutions perform better at 15–20 examples for many enterprises. A significant difference in the number of training examples per intent can cause serious issues. Some intents can get by with just a few examples, while others may need a lot more. It can be difficult and time-consuming to strike the correct balance. The requirement for more instances within specific intents is frequently driven by term overlap.
To establish a baseline performance reading, keep your volume distribution within a specified range when training your initial model. As a starting point, aim for an average of 15 examples per intent, but allow no fewer than seven and no more than 25 per intent.
After you've completed your first performance experiment, make any necessary adjustments based on the precision of each intent. Over-trained intentions are more likely to produce false positives, whereas under-trained intents are more likely to produce false negatives.
Start Small, Iterate, and Incorporate
A solution that does five things well will typically provide more excellent business value than a solution that does 100 things poorly. Begin with small. Iterate on your ideas. Expect a few reshuffles in intent categorization. Dialogue nodes may need to be altered as a result of this. As a result, before you start constructing further sophisticated rules to manage your inputs, focus on putting your essential ground truth in a solid performance condition.
Once AI solutions are exposed to real-world encounters, they can only improve. Make a plan to go over your logs. Incorporate sample examples and other significant learnings about how your users engage with the product into your next enhancement cycle.
Natural Language Toolkit (NLTK): It supports almost every component of NLP, including categorization, tokenization, stemming, tagging, parsing, and semantic reasoning. There are often multiple implementations for each, allowing to pick the particular algorithm that needs to be utilized. It also supports a wide range of languages. However, it displays all data as strings.
SpaCy: In most cases, SpaCy is faster than NLTK, but it has only a single implementation for each NLP component. It represents data as an object, simply the interface to build applications. It can also integrate with several other tools and frameworks. It has a simple interface. But ut does not support as many languages as SpaCy can.
TextBlob: It is great for smaller projects, and if someone has just started with the technology, this is a perfect tool. It is an extension of NLTK, so many of NLTK’s functionalities can be accessed in this.
PyTorch-NLP:PyTorch-NLP has just been around for a little over a year, yet it already has a tremendous community. It's a fantastic tool for quick prototyping. It is primarily designed for researchers but may also be utilized for prototypes and early production workloads.
Compromise: Compromise isn't the most advanced tool in the toolbox. It does not have the most powerful algorithms or the most comprehensive system. But it is a fast tool with a lot of functionality and the ability to work on the client side. Overall, the developers compromised on functionality and accuracy by focusing on a tiny package with more particular functionality that benefits from the user understanding more of the context around the usage.
Nlp.js: Nlp.js is built on numerous other NLP tools, such as Franc and Brain.js. Many aspects of NLP, including classification, sentiment analysis, stemming, named entity identification, and natural language creation, are accessible through it. It also supports a variety of languages. It is a tool with a simple UI that connects to a number of other excellent programs.
OpenNLP: It is hosted by the Apache Foundation and also can integrate with other Apache projects, like Apache Flink, Apache NiFi, and Apache Spark, easily. It covers all the common processing components of NLP. It allows users to use it as a command line or within the application as a library. It supports multiple languages. It is ready for production workloads using Java.
Automatic text summarization is the process of shortening long texts or paragraphs and making a summary that conveys the intended message. There are 2 main types of summarization that can be done:
Extractive summary: In this method, summarization will be done on the basis of the combination of meaningful sentences which will be extracted directly from the original text.
Abstractive summary: This method is more advanced than the extractive as the output will be a new text. The aim here is to understand the general meaning of sentences, interpret the context, and generate new sentences based on the overall meaning.
To do text summarization the following are the steps that one needs to follow:
Cleaning the text from filling words
Tokenization: Making the sentences shorter
A similarity matrix is created that represents relations between different tokens
Calculating sentence ranks based on semantic similarity
Sentences with top ranks are selected (abstractive or extractive) for the generation of summary.
The Sentiment Analysis are of various types few of therm are below described:
Sentiment Analysis in Social Media
NLP allows to uncover hidden data patterns and insights from the data of social media channels. Sentiment analysis can analyze language or text used in social media posts, reviews, responses and it can extract the emotions and attitudes of users that could be used for several purposes. One of them is for companies to deliver things according to the likes of users.
Sentiment Analysis of product reviews
It is a natural language processing method that identifies the emotional tone behind the text and suggests a wide range of analyzes to identify positive or negative emotions in a sentence, customer review sentiments, textual judgment or voice analysis, and other similar activities. Many product reviews may be collected from various sources such as review sites, forums, app stores, and eCommerce stores to collect user experience data.
Machine learning algorithms follow the following step:
Convert data into structured form.
The next step is to feed the data to the ML model.
And then sentiment analysis is done on it.
With the increasing market competition, sentiment analysis has become the need of time. Even established brands actively use this process to improve consumer knowledge. Whether you have a new or popular brand, you should use sentiment analysis in the ways mentioned above to constantly improve user experience and stay ahead of competitors.
To translate text from one language to another. It is more than just replacing the words. For effective translation, translators need to capture the meaning and tone of the input language accurately and then translate it to text with the same meaning and desired impact in the output language.
NLP models can be used for text editing to detect spam-related words, sentences, and emotions in emails, text messages, and social media messaging applications.
Following are the steps that should be followed for doing spam detection using NLP :
Data preprocessing: This includes removing stopwords, punctuation, converting text into lowercase, etc.
Tokenization: Sampling text into smaller sentences and paragraphs.
Part-of-speech (PoS) tagging: We can tag a word to its corresponding part of a speech based on its context.
Then this processed data will be fed to a classification algorithm (e.g. random forest, KNN, decision tree) to classify the data into spam or ham.
The working of NLP revolves around the text(language) and speech that refers to words in its raw form without considering the medium of communication. In the current process of NLP, there are some challenges, but the effect of some of those challenges can be reduced with NLP best practices and reaming challenges will surely be addressed in the near future.