4 most asked NLP interview questions
NLP is a method for automated extraction of meaning or message from written or spoken natural language. NLP is an automated process undertaken by machine learning tools, trained using a lot of text or speech data. The meaning of a text or speech is usually determined by context and origin. Thus NLP seeks to dive deep and understand hidden or subliminal points to deduce a speech or text correctly and find out the intended meaning. NLP is an awesome technology, currently in use for multiple public and private sectors. And the deployment of NLP in several sectors is mostly successful and is known to yield desirable results. For instance, the success of NLP deployment in the traffic management sector is truly notable. Rouge vehicles and drivers can now be detected from a mile away and prosecuted with ease. However, the process is not limited to the deployment of NLP, also computer vision, and motion sensors. Thus deployable NLP tools are expected to work with allied tools with ease and finesse and a developer is mostly responsible for the same. Thus employers during an interview tend to find out the depth of knowledge possessed by a professional and evaluate the experience they claim to possess. This article will discuss some of the most asked NLP interview questions, simple, but comprehensive enough to make a real difference in the case of an interview.
1 . Explain named entity recognition
An NLP program is designed keeping in mind real-world applications. Thus the real-world information is expected to be coded in the program. However humongous the task might sound, it is necessary. Entity recognition is a process very similar to sentence indexing. For instance, “Rohit lives in Bangalore”, will end up classified into three sections following the needs.
Rohit – the name of an individual
Bangalore – the name of a city
Lives in – the lives now, thus the time is present
2. How feature extraction is performed?
Feature extraction is a process, by which a phrase can be categorized into a particular class. To categorize phrases based on the implications, a tool must be designed for extracting the sentiments of an author. Keywords in these cases are usually identified and utilized as determining factors. For instance, a review containing the words, good quality, satisfactory, etc can be categorized as a positive review. Given there is a clear deficiency of negative words. Similarly, negative phrases can be identified as well.
3. Two very prominent applications of NLP
Nlp is being used by many public and private sectors. And the examples are plenty, to say the least. But these two are the ones that most frequently cross paths with average internet users.
- Chatbots are a direct blessing of NLP. Chatbots are designed to figure out the texts written by users and make actions following the texts. These bots are usually trained with a lot of text data but with time they are expected to grow more efficient. Thanks to the emergence of chatbots, entire user communications sectors can be outsourced. Saving a lot of manpower and money in the process. And apart from the savings, the prospects of a more efficient service are also very much a reality.
- Translation tools are another blessing of NLP. The language gap has been plaguing the prospects of globalization for quite some time now. And NLP-powered translation tools are the most perfect solution to this problem.
4. how latent semantic indexing is performed?
Latent semantic indexing is a process widely deployed with search engines. This method concerns finding words of similar meaning under a single context. Through this mathematical venture, the NLP tool can be used to identify the depth of an implication. The process is purely mathematical and is basic. The goal of latent semantic indexing is to achieve a deeper understanding of the language in the light of a context.
Conclusion Bookish knowledge can not always suffice if a student is expecting to transform themselves into professionals. The responsibilities bestowed on an NLP expert are of paramount importance. Thus employers are usually reluctant to make risky hires, rookies are thus advised to try and get some hands-on experience before committing to a job search.