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What are the skills and competencies required for a successful career in NLP?

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Natural language processing (NLP) is a branch of artificial intelligence that deals with the interaction between computers and human languages. It enables applications such as chatbots, voice assistants, sentiment analysis, machine translation, and text summarization. If you are interested in pursuing a career in NLP, you need to develop a range of skills and competencies that will help you create, improve, and deploy NLP systems. Here are some of the key areas you should focus on.

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Ryan Younger, PhD

Research and Design
Binulal Narayanan

a student, in the pursuit of gradients...

1 Programming languages

You should be proficient in at least one of the popular programming languages for NLP, such as Python, Java, or C++. These languages offer a variety of libraries and frameworks that support NLP tasks, such as NLTK, spaCy, Gensim, TensorFlow, PyTorch, and Apache OpenNLP. You should also be familiar with the syntax, data structures, algorithms, and debugging tools of your chosen language.

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Binulal Narayanan

a student, in the pursuit of gradients...
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One must be competent in regular expressions for effective pre-processing. Choosing an efficient text pre-processing strategy is more art than science.

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2 Mathematics and statistics

You should have a solid foundation in mathematics and statistics, especially in areas such as linear algebra, calculus, probability, and optimization. These concepts are essential for understanding and implementing the underlying models and methods of NLP, such as vector representations, neural networks, natural language generation, and natural language understanding. You should also be able to apply statistical techniques to analyze and evaluate NLP data and results, such as descriptive statistics, hypothesis testing, and confidence intervals.

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3 Machine learning and deep learning

You should have a good grasp of the principles and practices of machine learning and deep learning, which are the core technologies of NLP. You should be able to use various supervised, unsupervised, and semi-supervised learning algorithms to train and test NLP models, such as classification, regression, clustering, and reinforcement learning. You should also be able to use different types of neural networks, such as recurrent neural networks, convolutional neural networks, and transformers, to handle complex NLP problems, such as sequence modeling, attention, and transfer learning.

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Ryan Younger, PhD

Research and Design
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Understanding fundamental principles is critical for anyone starting out in Natural Language Processing (NLP). Begin with tokenization, which divides text into meaningful units such as words or phrases. Learn how to use stemming and lemmatization to reduce words to their base forms and ensure consistency. Understand the notion of part-of-speech tagging, which categorizes words according to their grammatical roles. Use Named Entity Recognition (NER) to recognize entities such as names and locations. Understand how important it is to remove stop words in order to focus on significant material. Finally, use sentiment analysis to determine the emotional tone of text.

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4 Linguistics and language skills

You should have a basic knowledge of linguistics and language skills, such as grammar, syntax, semantics, pragmatics, and discourse. These skills will help you understand the structure and meaning of natural languages, as well as the challenges and limitations of NLP systems. You should also be able to work with different languages and dialects, depending on your target domain and audience. You should be aware of the cultural and social aspects of language use, such as tone, style, context, and nuance.

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5 Domain knowledge and problem-solving skills

You should have a clear understanding of the domain and the problem you are trying to solve with NLP. You should be able to identify the needs and expectations of your users and stakeholders, as well as the constraints and opportunities of your data and resources. You should also be able to formulate and refine your research questions and hypotheses, design and execute your experiments and evaluations, and communicate and present your findings and recommendations.

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6 Collaboration and communication skills

You should be able to collaborate and communicate effectively with other NLP professionals, as well as with people from other disciplines and backgrounds. You should be able to work in teams, share your ideas and feedback, and learn from others. You should also be able to communicate your NLP knowledge and skills to non-technical audiences, such as clients, managers, or end-users. You should be able to use appropriate tools and platforms, such as GitHub, Slack, or Jupyter Notebook, to document and showcase your work.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Elisa W.

Staff Machine Learning Scientist at Visa
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It's useful to do a literature search on NLP, understanding key methods that were used before transformers. For example, n-grams, tokenization and word embeddings, among others. From there, build up your knowledge by reading about modern methods. Those methods will be based on the older papers you've read about, which will give you a firm understanding of how certain approaches came to be and what the limitations of them are. Because a lot of NLP models nowadays are too complex for beginners to build from scratch, you can play with pre-trained models on Hugging Face to understand the ins and outs of implementation.

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What are the skills and competencies required for a successful career in NLP?

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1 Programming languages

2 Mathematics and statistics

3 Machine learning and deep learning

4 Linguistics and language skills

5 Domain knowledge and problem-solving skills

6 Collaboration and communication skills

7 Here’s what else to consider

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