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Top 10 NLP Techniques for Data Scientists in 2024
Published
22nd Dec, 2023
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Natural Language Processing is a popular processing technique that focuses on the interaction between human language and computers and aims to improve this communication drastically. Computer engineers use these techniques and NLP skills to analyze, understand, and generate natural languages like text or speech. Some commonly used NLP techniques include tokenization, POS tagging, NER, sentiment analysis, text classification, language modeling, and machine translation. Along with having NLP skills, going for Data Science certification programs will support you in absorbing the essential knowledge and transitioning to Data Science roles without hassles.
What is Natural Language Processing?
Natural Language Processing is a newly introduced field in recent years and can be considered a branch of data science. It combines artificial intelligence that focuses computer software engineers on enabling computers to understand, interpret, and generate human language. In simpler words, NLP is the technology that helps computers to come out of their coding languages and interact with humans using easily understandable natural language, such as text or speech.
This branch of data science combines various techniques and understanding from computer science, linguistics, mathematics, and psychology. Compared to the other segments, it is slightly complicated as it is new. The main goal of NLP techniques is to ensure that computers can efficiently process and analyze human language data. In recent years, the field of NLP has advanced significantly due to the sudden shift in the world and more focus on the digit world. Today, NLP is used in various applications, such as virtual assistants, chatbots, customer service systems, recommendation systems, and language translation tools.
Roles and Responsibilities of an NLP Engineer
The roles and responsibilities of an NLP (Natural Language Processing) engineer in data science can significantly vary. There are several factors due to which this can happen depending on the organization and specific job requirements. However, some typical roles and responsibilities of an NLP engineer include:
Developing and Implementing NLP Algorithms and Model Analysis
NLP engineers are responsible for developing and implementing algorithms. They must test models that can process natural language data, such as text or speech, and extract meaningful insights.
Data Preprocessing and Cleaning
NLP engineers are responsible for preprocessing and cleaning natural language data to make it usable for analysis. This can involve tasks such as tokenization, stemming, and stop-word removal.
Designing and Training NLP Models
NLP engineers are responsible for developing and training models. They commonly use machine learning techniques, which help the software or application perform tasks such as text classification, sentiment analysis, and machine translation.
Evaluating and Improving NLP Models
NLP engineers are responsible for assessing the performance of NLP models and continuously improving them based on the results.
Collaborating with Cross-functional Teams
They generally need to work closely with other teams of the computer service segment in the company, such as data scientists, software developers, and business analysts. This helps them to develop and implement NLP solutions that meet the organization's needs.
Staying up-to-date with Industry Trends and Advancements
This is one of the most important factors to be considered by NLP engineers. It helps them apply the recent advancements to improve the performance of NLP models and solutions.
Top Natural Language Processing Techniques
Several NLP techniques in data science are commonly used in machine learning and artificial intelligence. Combining these techniques has enabled the development of sophisticated NLP systems to process and analyze natural language data to extract valuable insights. Some of the top NLP techniques include:
1. Tokenization
This NLP technique involves breaking down a text into smaller units, such as words or sentences to enable further analysis. One of the most common examples of tokenization is breaking down credit card numbers into short values. This helps in easy and secure data transfer and offers the users privacy. In NLP, tokenization is used to break sentences into easily understandable fragments.
2. Named Entity Recognition (NER)
This identifies and categorizes named entities such as people, places, organizations, and dates in a text. NER is the most basic technique that is being used in the field of data science.
3. Part-of-Speech (POS) Tagging
This enables the software to segregate data easily by noting different parts of speech. In a way, it can be said that POS helps in labeling each part of the sentence into separate parts and then analyzing them individually for better understanding.
4. Sentiment Analysis
This NLP technique determines the sentiment or overall attitude expressed in a text, such as positive, negative, or neutral. This tool is highly beneficial in customer survey forms and data analysis during reviews.
5. Text Classification
This technique involves assigning a text to one or more predefined categories. It helps in processing data easily, based on its content, such as spam or not spam, news or opinion, and so on.
Important NLP Skills And Tools that Need to be Mastered by NLP Engineers
There are many other NLP skills that computer engineers can master in the field of data science to polish and add to the section of NLP skills resumes. These skills also help the recruiter understand your knowledge in the parallel streams of computer science, which helps in better understanding. There are several NLP training courses you can take to upgrade and enhance your resume. Below are some of the skills that you must consider:
Data Analysis Skills - NLP professionals should have strong data analysis skills and be able to work with large amounts of data. This includes skills in data preprocessing, data cleaning, and data visualization.
Linguistic Knowledge - NLP professionals should understand linguistics used in data science and be able to analyze the structure and syntax of natural language data.
Mathematical Skills - NLP professionals should have strong mathematical skills and be familiar with statistical models and algorithms used in machine learning.
Problem-solving Skills - NLP engineers must have a sharp eye and excellent problem-solving skills.
Domain Expertise - NLP professionals should know a specific domain, such as healthcare, finance, or e-commerce.
Communication Skills - NLP professionals should have excellent communication skills and be able to communicate their findings and recommendations to non-technical stakeholders.
Here are some of the tools that NLP engineers must master:
- NLTK - This is a popularly used tool in data science. Software engineers often use it for Python programming languages. Recently, it has been used by the NPL engineers to analyze human language data.
- SpaCy - Since this tool was recently incorporated about 7 years back, it uses the latest algorithms. Coming to its application, it offers the best performance and output.
- Stanford CoreNLP- This tool has been specifically developed by NLP engineers. One of the most distinguishing aspects of this tool is that it follows JAVA.
Ways to Boost Your NLP Skills
While applying for the role of an NLP engineer in data science, here are some things that you can do to update your skills:
- Take NLP Courses and Online Tutorials: Some popular options include the Natural Language Processing Specialization on Coursera, NLP Fundamentals course on Udacity, and the NLTK book and tutorials.
- Participate in NLP Challenges and Competitions: Participating in NLP challenges and competitions, such as the SemEval challenges, can help you gain practical experience and learn from other NLP professionals.
- Work on NLP Projects: Different NLP projects, such as sentiment analysis or text classification, can help you gain hands-on experience. It will also help you build a portfolio of NLP projects.
- Contribute to NLP Open-source Projects: Contributing to NLP open-source projects, such as spaCy or NLTK, will help you understand the real-world NLP codebases. Furthermore, it will help you know its importance and collaborate with other NLP professionals.
- Attending Events and Lectures: Attending NLP conferences and events can be a great add-on to your resume. It can also help you network with other NLP professionals and learn about the latest NLP research and industry trends.
- Practice and Experiment: Learning more and more about data science can help you to gain a deeper understanding of NLP techniques and concepts.
Key Takeaways
Data science, along with NLP, is helping to make the world faster, better and tech-savvy. Using machine learning and artificial intelligence techniques, NLP involves processing and analyzing natural language data. By continuously learning and experimenting with new data science techniques, you can become a more effective NLP professional and contribute to developing innovative NLP applications and systems. Learning more natural language processing skills can help you in the long run. The KnowledgeHut Data Science certification programs will boost your career and keep you updated on the latest industry trends.
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