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6 NLP Techniques Every Data Scientist Should Know


Natural language processing has already begun to transform to way humans interact with computers, and its advances are moving rapidly. The field is built on core methods that must first be understood, with which you can then launch your data science projects to a new level of sophistication and value.



By Sara Metwalli, Associate Editor at Towards Data Science.

Photo by Sai Kiran Anagani on Unsplash.

Natural language processing is perhaps the most talked-about subfield of data science. It’s interesting, it’s promising, and it can transform the way we see technology today. Not just technology, but it can also transform the way we perceive human languages.

Natural language processing has been gaining too much attention and traction from both research and industry because it is a combination between human languages and technology. Ever since computers were first created, people have dreamt about creating computer programs that can comprehend human languages.

The advances in machine learning and artificial intelligence fields have driven the appearance and continuous interest in natural language processing. This interest will only grow bigger, especially now that we can see how natural language processing could make our lives easier. This is prominent by technologies such as Alexa, Siri, and automatic translators.

The truth is, natural language processing is the reason I got into data science. I was always fascinated by languages and how they evolve based on human experience and time. I wanted to know how we can teach computers to comprehend our languages, not just that, but how can we make them capable of using them to communicate and understand us.

In this article, I will go through the 6 fundamental techniques of natural language processing that you should know if you are serious about getting into the field.

 

Lemmatization and stemming

 

Stemming and lemmatization are probably the first two steps to build an NLP project — you often use one of the two. They represent the field's core concepts and are often the first techniques you will implement on your journey to be an NLP master.

Often, beginners tend to confuse the two techniques. Although they have their similarities, they are quite different.

  • Stemming: Stemming is a collection of algorithms that work by clipping off the end of the beginning of the word to reach its infinitive form.These algorithms do that by considering the common prefixes and suffixes of the language being analyzed. Clipping off the words can lead to the correct infinitive form, but that’s not always the case. There are many algorithms to perform stemming; the most common one used in English is the Porter stemmer. This algorithm contains 5 phases that work sequentially to obtain the word’s root.
  • Lemmatization: To overcome the flaws of stemming, lemmatization algorithms were designed.In these types of algorithms, some linguistic and grammar knowledge needs to be fed to the algorithm to make better decisions when extracting a word’s infinitive form. For lemmatization algorithms to perform accurately, they need to extract the correct lemma of each word. So, they often require a dictionary of the language to be able to categorize each word correctly.

Image by the author, made using Canva.

Based on these definitions, you can imagine that building a lemmatizer is more complex and more time consuming than building a stemmer. However, it is more accurate and will cause less noise in the final analysis results.

 

Keywords extraction

 

Keyword extraction — sometimes called keyword detection or keyword analysis — is an NLP technique used for text analysis. This technique's main purpose is to automatically extract the most frequent words and expressions from the body of a text. It is often used as a first step to summarize the main ideas of a text and to deliver the key ideas presented in the text.

In the backend of keyword extraction algorithms lies the power of machine learning and artificial intelligence. They are used to extract and simplify a given text for it to be understandable by the computer. The algorithm can be adapted and applied to any type of context, from academic text to colloquial text used in social media posts.

Keywords extraction has many applications in today’s world, including social media monitoring, customer service/feedback, product analysis, and search engine optimization.

 

Named Entity Recognition (NER)

 

Like stemming and lemmatization, named entity recognition, or NER, NLP's basic and core techniques are. NER is a technique used to extract entities from a body of a text used to identify basic concepts within the text, such as people's names, places, dates, etc.

The NER algorithm has mainly two steps. First, it needs to detect an entity in the text and then categorize it into one set category. The performance of NER depends heavily on the training data used to develop the model. The more relevant the training data to the actual data, the more accurate the results will be.

Another factor contributing to the accuracy of a NER model is the linguistic knowledge used when building the model. That being said, there are open NER platforms that are pre-trained and ready to use.

NER can be used in a variety of fields, such as building recommendation systems, in health care to provide better service for patients, and in academia to help students get relevant materials to their study scopes.

 

Topic Modelling

 

You can use keyword extractions techniques to narrow down a large body of text to a handful of main keywords and ideas. From which, you can probably extract the main topic of the text.

Another, more advanced technique to identify a text's topic is topic modeling—a type of modeling built upon unsupervised machine learning that doesn’t require a labeled data for training.

Multiple algorithms can be used to model a topic of text, such as Correlated Topic Model, Latent Dirichlet Allocation, and Latent Sentiment Analysis. The most commonly used approach is the Latent Dirichlet. This approach analyzes the text, breaks it down into words and statements, and then extracts different topics from these words and statements. All you need to do is feed the algorithm a body of text, and it will take it from there.

Image by the author, made using Canva.

 

Summarization

 

One of the useful and promising applications of NLP is text summarization. That is reducing a large body of text into a smaller chuck containing the text's main message. This technique is often used in long news articles and to summarize research papers.

Text summarization is an advanced technique that used other techniques that we just mentioned to establish its goals, such as topic modeling and keyword extraction. The way this is established is via two steps, extract and then abstract.

In the extract phase, the algorithms create a summary by extracting the text's important parts based on their frequency. After that, the algorithm generates another summary, this time by creating a whole new text that conveys the same message as the original text. There are many text summarization algorithms, e.g., LexRank and TextRank.

In LexRank, the algorithm categorizes the sentences in the text using a ranking model. The ranks are based on the similarity between the sentences; the more similar a sentence is to the rest of the text, the higher it will be ranked.

 

Sentiment Analysis

 

The most famous, well-known, and used NLP technique is, without a doubt, sentiment analysis. This technique's core function is to extract the sentiment behind a body of text by analyzing the containing words.

The technique's most simple results lay on a scale with 3 areas, negative, positive, and neutral. The algorithm can be more complex and advanced; however, the results will be numeric in this case. If the result is a negative number, then the sentiment behind the text has a negative tone to it, and if it is positive, then some positivity in the text.

Sentiment analysis is one of the broad applications of machine learning techniques. It can be implemented using either supervised or unsupervised techniques. Perhaps the most common supervised technique to perform sentiment analysis is using the Naive Bayes algorithm. Other supervised ML algorithms that can be used are gradient boosting and random forest.

 

Takeaways

 

Humans' desire for computers to understand and communicate with them using spoken languages is an idea that is as old as computers themselves. Thanks to the rapid advances in technology and machine learning algorithms, this idea is no more just an idea. It is a reality that we can see and experience in our daily lives. This idea is the core diving power of natural language processing.

Natural language processing is one of today’s hot-topics and talent-attracting field. Companies and research institutes are in a race to create computer programs that fully understand and use human languages. Virtual agents and translators did improve rapidly since they first appeared in the 1960s.

Despite the different tasks that natural language processing can execute, to get in the field and start building your own projects, you need to be completely comfortable with the core 6 fundamental natural language processing techniques.

These techniques are the basic building blocks of most — if not all — natural language processing algorithms. So, if you understand these techniques and when to use them, then nothing can stop you.

Original. Reposted with permission.

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