Basics of Natural Language Processing

We don’t even notice all the ways Natural Language Processing is present in our daily lives. If you think about it, Natural Language Processing is present in:

  • Voice recognition in our smartphones
  • Translation of the pages in foreign languages
  • Customer Support Chatbots in eCommerce stores
  • Spam filters in our email inboxes
  • Report generation in our analytics tools

NLP is one of the integral elements of the business processes because it automates the interpretation of business intelligence and streamlines the operation.

In this introduction to Natural Language Processing, we will explain, what it is all about, how it works and its role in the modern world.

What is Natural Language Processing?

Natural Language Processing (aka NLP) is a field of computer science, Artificial Intelligence focused on the ability of the machines to comprehend language and interpret messages.

We can define NLP as a set of algorithms designed to explore, recognize, and utilize text-based information and identify insights for the benefit of the business operation.

As such, natural language processing and generation algorithms form a backbone for the majority of automated processes.

NLP gives the computer the skills to:

  • understand informally written queries;
  • extract the meaning out of it;
  • generate the responses of its own;
  • perform requested tasks.

The global task of NLP is streamlining the interaction between human operators and machines via more flexible conversational interfaces.

The NLP brings several value-added benefits to the table:

  • Insights into the content of the text (what is this text about?)
  • Exploration of the context of the message (Why, when, where, how it is about?)
  • Identification of the opportunities (facts, intents, sentiments) behind the message or “reading between the lines.”

The Origins of NLP technology

To continue our NLP introduction we should say about the roots of NLP technology, which go back into the times of the Cold War. The first practical application of Natural Language Processing was the translation of the messages from Russian to English to understand what the commies were at. The results were lackluster, but it was a step in the right direction. It took decades before the computers became powerful enough to handle NLP operations. You may check out current business applications of NLP in our article.

For a while, the major issue with NLP applications was flexibility. Long story short: early NLP software was stiff and not very practical. There was always something sore sticking out and breaking the game because language is complex and there is much going behind the words that were beyond the algorithm’s reach. Because of that, the algorithms required a lot of oversight and close attention to the details.

However, with the emergence of big data and machine learning algorithms, the task of fine-tuning and training Natural Language Processing models became less of an undertaking and more of a routine job.

How does natural language processing work?

In essence, Natural Language Processing is all about providing tools to enable the machine’s comprehension of language on a deeper level than straightforward commands.

This means the NLP models deal with a variety of different aspects of language, including:

  • Semantics – relations between words, sentences, paragraphs, and so on
  • Morphology – structure, and content of word forms
  • Phonology – a sound organization of words
  • Syntax – structural governance of the texts
  • Pragmatics – the way context contributes to meaning

The whole procedure involves the following steps:

  • The text is segmented into meaningful bits (topics, sentences, paragraphs, etc.)
  • Bag of words – counts words and their occurrences throughout the text.
  • After that, the words in the sentences are split apart. This process is called tokenization;
  • Then Parts of speech are tagged through the body of text
  • Term Frequency-Inverse Document Frequency (TF-IDF) – determines the importance of certain words in a corpus.
  • This process is continued with Named Entity Recognition which finds specific words that are names (people’s or company’s names, job titles, locations, product names, events, number figures, and others) or are related to them.
  • Next goes Stop Words Removal – this process removes the everyday language stuff like pronouns and prepositions. This process can be referred to as cleaning the text from irrelevant or noisy material. Stop words may also include anything deemed inconsequential for the particular use case.
  • The next step is Stemming – the process of separating the affixes from the words and extracting the root of the word. This includes prefixes (as in “biochemistry) and suffixes (as in “laughable”).
  • Then goes Lemmatization – the process of reducing the words to their base form and finding the variations of the word to form a distinct group. This includes the transformation of the words from one part of speech (as in the noun “walk” to the verb “walking”) to another or transformation from one time to another (from the present “write” to past “wrote”).
  • After that, the algorithm figures out how the words relate to each other. This process is called Dependency Parsing.
  • Topic modeling is applied to discover hidden structures or patterns in the text. The process involves text clustering into meaningful bits. The method also includes text chunking, which identifies the constituent parts of the sentence and the relations between the elements

That’s how an algorithm is capable of comprehending the text.

Because of the sheer volume of the information to be processed – NLP involves a combination of supervised and unsupervised machine learning algorithms. At first, the process involves clustering – exploring the texts and their content, then the procedure involves classification – sorting out the specific elements.

The models are trained on datasets (known as corpora) that include a lot of different examples of language use related to the use case requirements. The analysis of the text creates something of a map with the general layout, which, in turn, serves as a matrix through which the input text is understood.

For example, the translation algorithm is trained on a corpus of text and its counterpart in another language. Then the whole thing is augmented on each side with the accompanying vocabulary layout, which includes synonyms, semantics, and other supplementary material.

Overall, Natural Language Processing consists of two basic divisions:

  • Natural Language Understanding
  • Natural Language Generation

Natural Language Understanding

Natural Language Understanding is the analytical branch of Natural Language Processing. It is all about analyzing the contents of the text and understanding its insights.

Comprehension is the key element of NLP. The thing is – language is an ambiguous and multi-pronged beast. The meaning of the message depends on the context it is expressed in and other factors that address the purpose of the message.

To take these factors into the equation make the algorithm capable of getting the true meaning of the message – different techniques are used to deconstruct and analyze the text.

That’s what Natural Language Understanding (AKA Natural Language Interpretation) deals with. It lays the foundation for further proceedings.

NLU is a subdivision of data mining (you can read about it right here) that deals with textual content. As such, it is used prominently in the majority of data science operations. It is everywhere – from the spam filters to the grammar checks.

Natural Language Understanding involves

  • processing the text (i.e., structuring a piece of unstructured data)
  • analyzing its content to extract insights of relevance (for example, names mentioned in the article or figures related to market growth)
  • subsequently preparing it for some utilization (for example, to generate custom responses).

NLU is applied in the text classification.

Natural Language Generation

Natural Language Generation is the operational branch of NLP. In strict terms, NLG can be described as:

  • creation of the custom messages
  • with the information that is relevant to the query (telling the time when asked “what time is it?”)
  • in a form appropriate to the context of the query (answer to the question, summarization of the text, and so on).

Natural Language Generation is built on the foundation of Natural Language Understanding. In broad terms, the effectiveness of the generative model depends on the quality and precision of the applied analysis. This means – it is not a good idea to use NLP Model trained on Shakespeare’s sonnets to generate medical bills.

The generative procedure involves the following steps:

  • An algorithm determines what information must be generated into text.
  • This includes determining the solid and fluid elements, i.e., parts of the text that must be included unchanged (relevant terms, names, figures, etc.) and pieces of the book that can be transformed depending on the context).
  • Then the message is organized into the appropriate structure. The structure can be casual (plain narrative sentence), or it can be formalized (for example, as a list)
  • In the case of voice synthesis – uses a prosody model, which determines breaks, duration, and pitch. Then, using a speech database (recordings from a voice actor), the engine puts together all the recorded phonemes to form one coherent string of speech.

Natural Language Processing Challenges

Two basic challenges occur during the development of NLP models. Both of them are directly related to the preeminent features of the natural language. These are:

  1. Natural Language is irregular and ambiguous. There are many different words with numerous alternative uses.
  2. Natural Language structures are mutable and therefore complicated. Various phrase types can be formed out of the same bag of words.

This creates a problem for NLP as it is unable to comprehend the real meaning of the text. Misinterpretations pile up, and this manifests itself in incorrect, unusable results.

The solution for these challenges lies in more in-depth and more thorough corpus analysis.

  • The parsing mechanism must be able to explore various syntactic arrangements for phrases and be able to backtrack and rearrange them whenever necessary
  • Grammars must contain large libraries of relevant expressions to improve the precision of the checking. This way, the anomalies are easier to detect.
  • Grammatical rules must be tuned to detect inconsistencies in the structure and word use.

Why NLP is important?

Large volumes of textual data

Natural language processing helps computers communicate with humans in their language and scales other language-related tasks. For example, NLP makes it possible for computers to read the text, hear the speech, interpret it, measure sentiment, and determine which parts are essential.

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to analyze text and speech data efficiently thoroughly.

Structuring a highly unstructured data source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each style is a unique set of grammar and syntax rules, terms, and slang. When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter, and borrow terms from other languages.

While supervised and unsupervised learning, and specifically deep learning, are now widely used for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches. NLP is important because it helps to resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

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In Conclusion

The main currency of the modern world is information. The most valuable elements of information are insights and understanding of the context they are in. Semantics is the key to understanding the meaning and extracting valuable insight out of available data. This is what the majority of human activity is about – in one way or another.

However, there is way too much data to comprehend and far too many tasks to accomplish to get the big picture manually. That’s why computers are integral parts of any business operation.

The critical element in interpreting data and the meaning behind it is the natural language processing algorithms.


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Natural Language Processing Tools and Libraries

Natural language processing helps us to understand the text receive valuable insights. NLP tools give us a better understanding of how the language may work in specific situations. Moreover, people also use it for different business purposes. Such proposes might include data analytics, user interface optimization, and value proposition. But, it was not always this way.

The absence of natural language processing tools impeded the development of technologies. In the late 90s, things had changed. Various custom text analytics and generative NLP software began to show their potential.

Now the market is flooded with different natural language processing tools.

Still, with such variety, it is difficult to choose the open-source NLP tool for your future project.

In this article, we will look at the most popular NLP processing tools, their features, and use cases.

Let’s start

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8 Best NLP tools and libraries

natural language processing tools examples NLTK NLP Tool

1. NLTK – entry-level open-source NLP Tool

Natural Language Toolkit (AKA NLTK) is an open-source software powered with Python NLP. From this point, the NLTK library is a standard NLP tool developed for research and education.

NLTK provides users with a basic set of tools for text-related operations. It is a good starting point for beginners in Natural Language Processing.

Natural Language Toolkit features include:

  • Text classification
  • Part-of-speech tagging
  • Entity extraction
  • Tokenization
  • Parsing
  • Stemming
  • Semantic reasoning

NLTK interface includes text corpora and lexical resources.

They include:

  • Penn Treebank Corpus
  • Open Multilingual Wordnet
  • Problem Report Corpus
  • and Lin’s Dependency Thesaurus

Such technology allows extracting many insights, including customer activities, opinions, and feedback.

Natural Language Toolkit is useful for simple text analysis. But, if you need to work on a massive amount of data, try something else. Why? Because in this case, Natural Language Toolkit requires significant resources.

Do you want to know more about the NLTK application?

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Stanford Core NLP Library

2. Stanford Core NLP – Data Analysis, Sentiment Analysis, Conversational UI

We can say that the Stanford NLP library is a multi-purpose tool for text analysis. Like NLTK, Stanford CoreNLP provides many different natural language processing software. But if you need more, you can use custom modules.

The main advantage of Stanford NLP tools is scalability. Unlike NLTK, Stanford Core NLP is a perfect choice for processing large amounts of data and performing complex operations.

With its high scalability, Stanford CoreNLP is an excellent choice for:

  • information scraping from open sources (social media, user-generated reviews)
  • sentiment analysis (social media, customer support)
  • conversational interfaces(chatbots)
  • text processing, and generation(customer support, e-commerce)

This tool can extract all sorts of information. It has smooth named-entity recognition and easy mark up of terms and phrases.

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Apache OpenNLP - Data Analysis and Sentiment Analysis

3. Apache OpenNLP – Data Analysis and Sentiment Analysis

Accessibility is essential when you need a tool for long-term use, which is challenging in the realm of Natural Language Processing open-source tools. Because while being powered with the right features, it could be too complex to use.

Apache OpenNLP is an open-source library for those who prefer practicality and accessibility. Like Stanford CoreNLP, it uses Java NLP libraries with Python decorators.

While NLTK and Stanford CoreNLP are state-of-the-art libraries with tons of additions, OpenNLP is a simple yet useful tool. Besides, you can configure OpenNLP in the way you need and get rid of unnecessary features.

Apache OpenLP is the right choice for:

  • Named Entity Recognition
  • Sentence Detection
  • POS tagging
  • Tokenization

You can use OpenNLP for all sorts of text data analysis and sentiment analysis operations. It is also perfect in preparing text corpora for generators and conversational interfaces.

SpaCy - NLP Library

4. SpaCy – Data Extraction, Data Analysis, Sentiment Analysis, Text Summarization

SpaCy is the next step of the NLTK evolution. NLTK is clumsy and slow when it comes to more complex business applications. At the same time, SpaCy provides users with a smoother, faster, and efficient experience.

SpaCy, an open-source NLP library, is a perfect match for comparing customer profiles, product profiles, or text documents.

SpaCy is good at syntactic analysis, which is handy for aspect-based sentiment analysis and conversational user interface optimization. SpaCy is also an excellent choice for named-entity recognition. You can use SpaCy for business insights and market research.

Another SpaCy advantage is word vector usage. Unlike OpenNLP and CoreNLP, SpaCy works with word2vec and doc2vec.

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Still, the main advantage of SpaCy over the other NLP tools is its API. Unlike Stanford CoreNLP and Apache OpenNLP, SpaCy got all functions combined at once, so you don’t need to select modules on your own. You create your frameworks from ready building blocks.

SpaCy is also useful in deep text analytics and sentiment analysis.

AllenNLP - Text Analysis, Sentiment Analysis

5. AllenNLP – Text Analysis, Sentiment Analysis

Built on PyTorch tools & libraries, AllenNLP is perfect for data research and business applications. It evolves into a full-fledged tool for all sorts of text analysis. This way, it is one of the more advanced Natural Language Processing tools on this list.

AllenNLP uses SpaCy open-source library for data preprocessing while handling the rest processes on its own. The main feature of AllenNLP is that it is simple to use. Unlike other NLP tools that have many modules, AllenNLP makes the natural language process simple. So you never feel lost in the output results. It is an excellent tool for inexperienced users.

The machine comprehension model provides you with resources to make an advanced conversational interface. You can use it for customer support as well as lead generation via website chat.

So, the textual entailment model guarantees smooth and comprehensible text generation. You can use it for both multi-source text summarization and simple user-bot interaction.

The most exciting model of AllenNLP is Event2Mind. With this tool, you can explore user intent and reaction, which are essential for product or service promotion.

Omit, AllenNLP is suitable for both simple and complex tasks. AllenNLP performs specific duties with predicted results and enough space for experiments.

GenSim NLP Library

6. GenSim – Document Analysis, Semantic Search, Data Exploration

Sometimes you need to extract particular information to discover business insights. GenSim is the perfect tool for such things. It is an open-source NLP library designed for document exploration and topic modeling. It would help you to navigate the various databases and documents.

The key GenSim feature is word vectors. It sees the content of the documents as sequences of vectors and clusters. And then, GenSim classifies them.

GenSim is also resource-saving when it comes to dealing with a large amount of data.

The main GenSim use cases are:

  • Data analysis
  • Semantic search applications
  • Text generation applications (chatbot, service customization, text summarization, etc.)
TextBlob Library - Conversational UI, Sentiment Analysis

7. TextBlob Library – Conversational UI, Sentiment Analysis

TextBlob is the fastest natural language processing tool. TextBlob is an open-source NLP tool powered by NLTK. It could be enhanced with extra features for more in-depth text analysis.

You can use TextBlob sentiment analysis for customer engagement via conversational interfaces. Besides, you can build a model with the verbal skills of a broker from Wall Street.

Another TextBlob notable feature is machine translation. Content localization has become trendy and useful. For that, it would be great to have your website/application localized in an automated manner. Using TextBlob, you can optimize the automatic translation using its language text corpora.

TextBlob also provides tools for sentiment analysis, event extraction, and intent analysis features. TextBlob has different flexible models for sentiment analysis. Thus, you can build entire timelines of sentiments and look at things in progress.


Intel NLP Architect - Data Exploration, Conversational UI3

8. Intel NLP Architect – Data Exploration, Conversational UI3

Intel NLP Architect is the newer application in this list. Intel NLP Architect uses Python library for deep learning using recurrent neural networks. You can use it for:

  • text generation and summarization
  • aspect-based sentiment analysis
  • and conversational interfaces such as chatbots

One of its most exciting features is Machine Reading Comprehension. NLP Architect applies a multi-layered approach by using many permutations and generated text transfigurations. In other words, it makes the output capable of adapting the style and presentation to the appropriate text state based on the input data. You can use it for more personalized services.

The other great feature of Architect NLP is Term Set Expansion. This set of NLP tools fills in the gap of data based on its semantic features. Let’s look at an example.

When making research on virtual assistants, your initial input would be “Siri” or “Cortana.” Term Set Expansion (TSE) adds the other relevant options as “Amazon Echo.” In more complex cases, TSE is capable of scraping bits and pieces of information based on longer queries.

NLP Architect is the most advanced tool being one step further, getting deeper into the sets of text data for more business insights.

You might also like Guide to machine learning applications: 7 major fields.

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Choosing a Particular NLP Library

Natural Language Processing tools are all about analyzing text data and receiving useful business insights out of it.

But it is hard to find the best NLP library for your future project. This way, to make the right decision, you should be aware of the alternatives. Also, you should choose your next NLP tool according to its use case. There is no reason to take a state-of-the-art library when you need to wrangle the text corpus and clean it from all data noise.

If you want to receive a consultation on Natural Language Processing, fill in the contact form, and we will get in touch.


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