Can technology comprehend human language? This question is relevant in this age of digital transformation where technology is disrupting every aspect of our lives. From smartphones, computers, tablets and smartwatches, technology is connecting us to the digital world.

The understanding of human language is critical for this relationship to last and technologies such as NLP¹ are changing this space. Voice assistants use natural language processing to understand human language and these devices are becoming smart assistants for our lives.

The explosion of natural language processing is just beginning and this technology could become bigger in the coming years. A decade ago, no one would have imagined having smart assistants on our phones and external devices.

In 2021, the reality is here as more consumers shift their demand to voice services such as Siri from Apple. Translation of languages is increasing and NLP technology is making this possible.

Data scientists² have vast opportunities in natural language processing and learning NLP techniques will help them get ahead of the game. NLP developers approach their work differently and with many #NLP methods out there, let us explore common NLP methods that data scientists should use in 2021.

NLP Methods for Data Scientists

1. Lemmatization and Stemming

Every NLP student must learn lemmatization and stemming to build the foundation of natural language processing. Lemmatization and stemming involves understanding the root of words and clipping them to reach their infinitive form.

NLP developers use lemmatization and stemming because of automatic removal of word sections as they train language models. #Algorithms make this possible by creating the right word order after extraction. Suffixes create problems for NLP developers when training models³. Stemming eliminates suffixes and this makes translation accurate. Errors in word order can lead to inaccurate results and NLP algorithms do all the work by fixing word translation problems.

2. Sentiment Analysis

Sentiment analysis is the second popular NLP method that data scientists should use because of accurate translations in numerical form. Text analysis is the basis of natural language processing and sentiment analysis makes this possible by reflecting positive, negative or neutral states. The numeric figures provided by sentiment analysis enables NLP developers⁴ to understand the nature of the text.

For instance, a neutral number in the text means that the language is conservative and does not lie in negative or positive sides. NLP developers use sentiment analysis because of the accuracy of searching text and giving fast results. Sentiment analysis utilizes many algorithms⁵ depending on translation needs with the most common such as Naïve Bayes algorithm and Random Forest.

3. Topic Modelling

The topic modelling NLP technique makes it easy to lift words and topics from large text materials. By separating keywords, this technique enables understanding of the main themes of written materials. Algorithms for topic modelling such as Correlated Topic Model bring quick results because of highlighting statements that give clues about what the text entails.

Going through a large text material is tedious and with more algorithms such as Latent Dirichlet Allocation in use, text analysis has never been easier. #Datascientists can use algorithms favorable to them and what matters is inputting the correct text for algorithm search.

4. Extraction of Keywords

Keyword extraction has become commonplace in this data driven age we live in with companies using this technique to understand their customers and markets. NLP engineers extract words by checking common phrases that appear within a text and building concepts and ideas from them. Keyword extraction saves time as algorithms powered by #AI support the extraction process.

An accurate review of a text means that each word and phrase is captured by the algorithm and this makes keyword extraction⁶, a great NLP technique that those in the data field can use for their analysis of information. The understanding of a text is what matters and keywords extraction breaks down large information sets into smaller digestible bits that facilitate understanding of the main topic.

5. Named Entity Recognition

The Named Entity Recognition⁷ model works in similar fashion to most the other NLP techniques discussed here but focuses more on entities compared to the rest. In this case, algorithms identify entities within a text and then categorize the text for further analysis. You use these entities to narrow down information written in the text.

NER applications are becoming commonplace across different industries with health care a good example where they use NER technology to identify patient information and use the analysis to offer personalized services. Recommendation systems run on #NER technology where the entities provide feedback based on the information fed into the system.

Level Up Your NLP Skills

Data scientists work in a diverse environment and levelling up their skills in areas such as NLP is a good idea to continue becoming better as you advance. As human and machine interaction⁸ builds up, natural language processing will expand and this will mean more innovation.

Data scientists are different and prefer using different NLP methods based on their circumstances. For instance, some prefer NER while others prefer keyword search and all that matters is using the approaches in the right ways.

Learning these NLP techniques is essential for every data scientist looking to become flexible and apply ideas outside their scope to improve their work. In addition, with more algorithms for #machinelearning and NLP popping up, levelling up your skills in these NLP methods is crucial.

Works Cited

¹NLP, ²Data scientists, ³Training Models, ⁴NLP Developers, ⁵Algorithms, ⁶Keyword Extraction, ⁷Named Entity Recognition, ⁸Machine Interaction