Natural language processing (NLP) is a form of artificial intelligence (AI) that enables computers to understand and interpret human language. It involves the use of linguistic intelligence and cognitive techniques to facilitate communication between humans and computers. NLP has a wide range of applications in various industries, including finance, healthcare, education, customer service, and more. Some specific uses of NLP include:
- Speech recognition: NLP can be used to transcribe spoken language into written text, which can be useful for tasks like voice-to-text transcription or automated call centers.
- Text-to-speech: NLP can be used to convert written text into speech, which can be useful for tasks like text-to-speech translation or automated customer service systems.
- Sentiment analysis: NLP can be used to analyze the sentiment expressed in written or spoken language, which can be useful for tasks like customer feedback analysis or social media monitoring.
- Text classification: NLP can be used to classify text into different categories based on its content, which can be useful for tasks like spam detection or topic identification.
Overall, NLP plays a crucial role in enabling computers to understand and interpret human language, which has opened up a wide range of possibilities for automating tasks and improving communication between humans and machines.
This blog is a guide to recruiting NLP engineers starting from understanding the field, the technologies NLP engineers work with, best areas to find NLP engineers and how to close them. Let's get started!
How does NLP work?
Anyone who has ever tried to learn a language knows how difficult this is. In theory, you have to master the syntax, grammar, and vocabulary - but we learn rather quickly that in practice this also involves tone of voice, which words we use concurrently, and the complex meaning of our interactions.
Computers and all machines only understand the language of 0’s and 1’s. Therefore, they require a set of rules to learn our language and understand its complexities.The communication between humans is stored in the form of “data” containing only 0’s and 1’s. NLP combines the field of linguistics and computer science to decipher language structure and guidelines and to make models which can comprehend, break down and separate significant details from text and speech.
The first step in this complex process is preprocessing consisting of the following steps:
- Removing Stop Words
- Parts of Speech Tagging
- Named Entity Tagging
Segmentation involves breaking the entire document down into its constituent sentences. It is accomplished by segmenting the article along with its punctuations like full stops and commas as shown below:
After Segmentation, comes the process of Tokenizing which helps the machines to understand these sentences. It involves breaking down the sentence into its constituent words and storing them. This is called tokenizing, and each world is called a token.
Removing Stop Words:
Now to make the learning process faster we have to get rid of non-essential words, which add little meaning to the sentences and are just there to make it sound more cohesive. Words such as was, in, is, and, the, are called stop words and can be removed.
It is the process of obtaining the Word Stem of a word. Word Stem gives new words upon adding affixes to them like:
Now comes the process of obtaining the Root Stem of a word. Root Stem gives the new base form of a word that is present in the dictionary and from which the word is derived. We can also identify the base words for different words based on the tense, mood, gender etc as shown below:
Part of Speech Tagging:
Now, we must explain the concept of nouns, verbs, articles, and other parts of speech to the machine by adding these tags to our words.
Named Entity Tagging:
Lastly, we introduce the machine to pop culture references and everyday names by flagging names of movies, important personalities or locations, etc that may occur in the data. We do this by classifying the words into subcategories. This helps the machine to find any keywords in a sentence. For eg, the subcategories can be person, location, monetary value, quantity, organization, movie.
After preprocessing of data the next steps in the NLP workflow are:
- Feature extraction: This involves extracting relevant features or characteristics from the text data that can be used to train machine learning models. This can include techniques such as term frequency-inverse document frequency (TF-IDF), which measures the importance of each word in a document relative to a collection of documents.
- Model training: This involves using the extracted features to train a machine learning model to perform a specific NLP task, such as language translation or text classification.
- Evaluation: This involves evaluating the performance of the model on a test dataset to determine how well it is able to perform the task.
- Deployment: Once the model has been trained and evaluated, it can be deployed in a real-world application or system to perform the NLP task.
Overall, the goal of NLP is to create systems that can effectively analyze and interpret the complex and often ambiguous nature of human language, and respond in a way that is appropriate and meaningful to the user.
Why is Natural Language Processing important?
The biggest benefit of NLP for businesses is the ability of technology to detect, and process massive volumes of text data across the digital world including; social media platforms, online reviews, news reports, and others.
According to market trends, the NLP market size is projected to reach USD 49.4 billion by 2027 at a CAGR of 25.7% during the forecast period. Various factors such as developments in text analyzing computer programs, increase in investments across healthcare sectors, and growing demand for enterprise solutions to streamline business operations for better customer experience are expected to drive the adoption of NLP. Some of the daily life applications of NLP are listed below:
- Search autocomplete, predictive text - Every time we type a text on our smartphones, we see NLP in action. We often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more we text, the more accurate it becomes, often recognizing commonly used words and names faster than we can type them
- Text to Speech - The most common being Apple’s Siri and Amazon’s Alexa, virtual assistants use NLP machine learning technology to understand and automatically process voice requests. Natural language processing algorithms allow the assistants to be custom-trained by individual users with no additional input, to learn from previous interactions, recall related queries, and connect to other apps
- Chatbots - Customer service and experience are the most important thing for any company. It can help the companies improve their products, and also keep the customers satisfied. But interacting with every customer manually, and resolving the problems can be a tedious task. This is where Chatbots come into the picture. Chatbots help the companies in achieving the goal of smooth customer experience.
- Monitor brand sentiment on social media - Sentiment analysis is the automated process of classifying opinions in a text as positive, negative, or neutral. It’s often used to monitor sentiments on social media. You can track and analyze sentiment in comments about your overall brand, a product, particular feature, or compare your brand to your competition
- Survey Analysis - Surveys are an important way of evaluating a company’s performance. Companies conduct many surveys to get customer’s feedback on various products. This can be very useful in understanding the flaws and help companies improve their products
- Hiring and Recruitment - With the help of natural language processing, recruiters can find the right candidate with much ease. This simply means that the recruiter would not have to go through every resume and filter the right candidates manually. The technique, like information extraction with named entity recognition, can be used to extract information such as skills, name, location, and education. Then, these features can be used to represent the candidates in the feature space, and then they can be classified into the categories of fit or not-fit for a particular role. Or, they can also be recommended for a different role based on their resume.This allows the unbiased filtering of resumes and selection of the best possible candidates for a vacant position without requiring much human labor.
Why hire a Natural Processing Language Engineer?
NLP Engineers are responsible for the development and design of language understanding systems and for the effective use of text representation techniques. This can involve dealing with speech patterns, AI speech recognition, understanding of natural languages, and natural language generation.
There are several reasons why a company might hire a natural language processing (NLP) engineer:
- To build or improve NLP systems: NLP engineers are skilled in using techniques such as machine learning and data analysis to build and improve NLP systems that can understand and interpret human language. They can help a company develop custom NLP solutions to meet specific needs or goals, such as improving customer service, automating tasks, or analyzing customer sentiment.
- To research and develop new NLP technologies: NLP engineers can also be involved in researching and developing new NLP technologies, such as novel machine learning algorithms or methods for improving the performance of NLP systems.
- To optimize and maintain NLP systems: NLP engineers can help a company optimize and maintain existing NLP systems to ensure they are running smoothly and effectively. This can include tasks such as debugging, performance tuning, and implementing updates or new features.
Overall, hiring a NLP engineer can be a valuable investment for a company looking to leverage the power of NLP to improve operations, enhance the customer experience, or gain valuable insights from data.
Why hire a Machine Learning engineer versus using third party tools?
There are several reasons why a company might choose to hire a natural language processing (NLP) engineer rather than using third party tools:
- Customization: Hiring a NLP engineer allows a company to build custom NLP solutions that are tailored to their specific needs and goals. This can be particularly useful if the company has unique requirements or if the available third party tools do not meet their needs.
- Control: Hiring a NLP engineer gives a company more control over their NLP systems and applications, as they can make changes and updates as needed rather than being limited to the capabilities of third party tools.
- Expertise: NLP engineers have deep expertise in the field of NLP and can provide valuable insights and guidance on how to best use NLP to solve business problems. This can be particularly useful if a company is new to NLP and needs guidance on how to implement it effectively.
- Integration: A NLP engineer can help a company integrate their NLP systems with other technologies and systems, such as customer relationship management (CRM) systems or data storage and processing platforms.
To summarize, while third party tools can be useful for certain NLP tasks, hiring a NLP engineer can provide a company with more flexibility, control, and expertise in their NLP efforts.
What is the future of NLP?
The future of natural language processing (NLP) looks bright, as the technology continues to advance and become more widely adopted across a variety of industries. Some of the key trends and developments in the field that are likely to shape the future of NLP include:
- Increased use of deep learning: Deep learning is a type of machine learning that involves training artificial neural networks on large datasets. It has already been shown to be effective in a variety of NLP tasks, and its use is likely to continue to grow in the future.
- Improved language understanding: NLP systems are likely to continue to improve their ability to understand and interpret human language, with a particular focus on handling more complex and nuanced language. This could involve techniques such as multi-task learning, which allows NLP models to perform multiple tasks simultaneously.
- Increased automation: NLP is likely to play a key role in the automation of various tasks and processes, including customer service, document classification, and language translation. This could lead to increased efficiency and cost savings for businesses.
- Greater use of real-time language processing: NLP systems are likely to be used more frequently to process language in real-time, such as in virtual assistants or chatbots. This will require NLP systems to be able to respond quickly and accurately to user input.
- Greater integration with other technologies: NLP is likely to be increasingly integrated with other technologies, such as virtual and augmented reality, to create more immersive and interactive experiences.
What technologies do Natural Processing Language engineers know?
Some of the technologies that NLP engineers might be familiar with include:
- Programming languages: NLP engineers often have expertise in programming languages such as Python, Java, and C++, which are commonly used in NLP applications.
- Machine learning frameworks: NLP engineers often use machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn to build and train NLP models.
- NLP libraries and frameworks: NLP engineers might also be familiar with specific libraries and frameworks that are designed specifically for NLP tasks, such as Natural Language Toolkit (NLTK), SpaCy, and CoreNLP.
- Data storage and processing technologies: NLP engineers often work with large datasets, and might be familiar with technologies such as Hadoop, Spark, and NoSQL databases to store and process this data.
- Cloud computing platforms: NLP engineers might also be familiar with cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform, which can be used to deploy and scale NLP systems and applications.
Why is Python so popular in the AI industry?
Python’s popularity in artificial intelligence programming is due in part to its rich library ecosystem. Python’s libraries and frameworks streamline coding, provide supervised and unsupervised machine learning algorithms, support the construction and training of artificial neural networks, and much more. These open-source tools also optimize development while reducing overhead costs.
Because Python is highly portable and extensible, it can be integrated with AI systems that are coded in other languages. Python can even execute cross-language tasks, and this flexibility makes it a top choice for artificial intelligence programming. Python’s flexibility also extends to the variety of programming styles the language supports. Python allows programmers to code in an imperative, functional, object-oriented, or procedural style—meaning you use the programming approach that best supports your AI, Machine Learning or Natural Language Processing solution.
Who are the top companies hiring Machine Learning (ML) engineers?
There are many companies that are actively hiring NLP engineers, as natural language processing (NLP) is a key technology in a wide range of industries. Some examples of top companies that hire NLP engineers include:
- Tech giants: Companies such as Google, Microsoft, and Amazon are all known for their advanced NLP capabilities, and are likely to be hiring NLP engineers to help develop and maintain their systems and applications.
- Healthcare companies: NLP has many applications in the healthcare industry, and companies such as Epic Systems and Cerner are known for their use of NLP in electronic health record systems and other healthcare applications.
- Financial institutions: Financial institutions such as JPMorgan Chase and Goldman Sachs are known for their use of NLP to automate tasks and improve efficiency, and are likely to be hiring NLP engineers to help develop and maintain their systems.
- Retail and e-commerce companies: Retail and e-commerce companies such as Walmart and Amazon use NLP to analyze customer data and improve the customer experience, and are likely to be hiring NLP engineers to help develop and maintain these systems.
- Startups: There are also many startups focused on NLP that are likely to be hiring NLP engineers, such as Keboola and Linguamatics.
Overall, there are many opportunities for NLP engineers in a variety of industries, and top companies in these industries are likely to be hiring NLP engineers to help develop and maintain their NLP systems and applications.
How to find Natural Processing Language Engineers?
There are only a few sources for hiring Natural Language Processing (NLP) engineers - LinkedIn, Github or StackOverflow. Most of the mid tier job boards such as Indeed or Monster won't have access to this top level talent and is likely to be a waste of time. Niche job boards like NLP Job Board, Clojure, AI Job Hunt, AI & ML Jobs, Kaggle, Remoteok.io (Remote) can also be very helpful.
LinkedIn remains the best place to look for Natural Processing Language Engineers or Data Scientists, at least in the United States and Canada. Their Recruiter search has several fields you can use to search candidates, including title, location, current organization, years of experience etc. and by creating a boolean the search can be narrowed down. To know how to create a boolean LinkedIn checkout their resources such as: Boolean Help
In addition to searching yourself on LinkedIn, here are some other ways to find and engage with NLP Engineers:
- Staffing companies - you can utilize staffing companies like Rocket, RobertHalf, Randstad and others to help you find a proficient NLP Engineer.
- AI sourcing - you can use tools like Hireflow or Fetcher to source NLP Engineers for you to engage.
Competing with top companies for Natural Processing Language (NLP) talent
Competing with companies for hiring Natural Language Processing (NLP) talent can be challenging, but there are several strategies that can help.
First, offering competitive salaries and benefits packages can attract top NLP talent. This includes offering flexible work arrangements, such as remote work options, and providing opportunities for professional development and career advancement. Another strategy is to focus on building a positive company culture and reputation. This can include promoting a collaborative and inclusive environment, as well as highlighting the company’s commitment to diversity, equity, and inclusion.
Additionally, highlighting the company’s involvement in cutting-edge NLP research and development can be attractive to potential NLP hires. This can include showcasing the company’s work on advanced NLP techniques and technologies, as well as any partnerships or collaborations with leading academic institutions.
Finally, networking and building relationships with potential NLP hires can be effective in recruiting top talent. This can include attending industry conferences and events, and connecting with NLP professionals on LinkedIn and other professional networking platforms.
Overall, competing with companies for hiring NLP talent requires a strategic approach that focuses on offering competitive salaries and benefits, promoting a positive company culture, highlighting cutting-edge NLP work, and building relationships with potential hires.
Sample Interview questions for NLP Engineers
Typical technical interviews for NLP Engineers divide the questions broadly based on Hard & Soft Skills of the candidate.
Logic & Algorithms
- What is tokenization in NLP?
- What is the difference between stemming and lemmatization?
- List the differences between NLP and NLU
- What do you know about Latent Semantic Indexing (LSI)?
- List a few methods for extracting features from a corpus for NLP
- What is perplexity in NLP?
- What are homographs, homophones, and homonyms?
- What is a hapax/hapax legomenon?
- What is an embedding matrix? How does it work?
- What does TF*IDF stand for? Explain its significance
- List a few real-world applications of the n-gram model
- What is the Naive Bayes algorithm?
- What do you know about the Masked Language Model?
- What is the Markov assumption for the bigram model?
- What are the stages in the lifecycle of a natural language processing (NLP) project?
- What are some of the common NLP tasks?
- How do Conversational Agents work?
- What are unigrams, bigrams, trigrams, and n-grams in NLP?
- What is meant by data augmentation? What are some of the ways in which data augmentation can be done in NLP projects?
- What are the steps involved in preprocessing data for NLP?
- What are the steps to follow when building a text classification system?
Programming Languages & Tools
- How will you use Python’s concordance command in NLTK for a text that does not belong to the package?
- Write the code to count the number of distinct tokens in a text?
- What are some metrics on which NLP models are evaluated?
- How is POS tagging achieved?
- What are some of the measures used to assess NLP models?
- What exactly is NLTK? What distinguishes it from Spacy?
- What are the most effective NLP tools?
- What is F1 score in NLP?
- What does a Python code to implement the Cosine Similarity function look like?
- Explain Named Entity Recognition by implementing it.
Behavioral / Soft Skills
- What are some real-life applications / real-world examples of Natural Language Processing (NLP)?
- List any two real-life applications of Natural Language Processing and explain their working?
- Will AI open source softwares like ChatGPT replace programmers and document writers for good?
- What are the current hot topics surrounding NLP?
- How would you approach a problem in NLP which is very easily solved for English (where you have abundant resources like Wordnet, Dictionaries, Sense tagged and parallel corpora) for other resource deprived languages
Hope you found our guide to recruiting NLP engineers useful!
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