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NLP – Natural Language Processing, is a branch of artificial intelligence that deals with the study of natural language processing. It uses complex computational models to understand human language and extract structured information from it.
NLP is widely used in different application domains, such as machine translation, summarization, and speech recognition. Due to its vast applications, NLP is a popular skill sought after by employers in top companies.
Below are some of the most common NLP interview questions. Go through the questions and gather some knowledge regarding how you can answer them. We will make it a little easier if you are looking forward to securing a job in NLP.
- How can machines understand human language?
NLP procedure usually revolves around using stem and lemmatization methods. Using parts of speech tagging is common NLP practice. The way humans use language varies. Everything cannot be translated.
Stemming can identify the root of words. The process identifies the original words by removing plural and verb forms. For instance, “works” and “working are both denoting “work.” Therefore, all forms of “work” will be identified as the same word. Google started using the stemming method in 2003.
Machines also start understanding human language by removing stop words from sentences. The lemmatization process identifies the context in which a particular word is used. The method also revolves around scanning adjacent sentences to understand the meaning.
What Are Some Real-World Applications of NLP?
Spelling and Grammar Checking Apps
The websites and mobile applications that correct the entered text’s spelling and grammatical errors use NLP algorithms. The applications can also provide recommendations by analyzing the words typed by the user. It becomes possible only because specific NLP models are used in the backend.
Several websites are now offering customer support through virtual bots. Chatbots help filter issues that don’t require human interaction or intervention to get solved. The bots can chat with users and find solutions to their problems. It leaves time for the customer support team to focus on more pressing matters. Therefore, customer support teams of companies become more efficient.
NLP allows training machines on texts of different languages. Therefore, algorithms can produce the equivalent meaning of a text in another language. The technology also extends to traditional languages like Russian and Chinese, which are more challenging to translate.
The advancement of digital technology has made customers expect more personalization. NLP enables content producers to automate categorization and metadata information. They use tags to create a more personalized and engaging experience for readers. Media outlets offer content recommendations. It ensures that viewers watch content that’s the most relevant to them.
What Is Latent Semantic Indexing?
Latent semantic indexing refers to a mathematical technique. It focuses on extracting information from unstructured data. The concept is based on the belief words applied in the same context have the same meaning.
Also known as Latent Semantic Analysis, the technique analyzes a set of documents. It aims to find statistical coexistence among words appearing together. The method provides a detailed insight into the topics of documents.
The method is focused on identifying relevant concept components. It aims to categorize words into groups representing semantic fields or concepts. To do it, the process uses Singular Value Decomposition to the Term-Document matrix.
Latent semantic indexing relies on computation but provides an NLP model with contextual awareness. The matrix includes words as rows and documents as columns. It is close to NLU.
What Is NLU? How Is It Different from NLP?
NLU or Natural Language Understanding is a subcategory of NLP. NLU is often referred to as NLI or Natural Language Interpretation. The process is concerned with making a machine acquire reading comprehension skills. Some typical applications of NLU include text categorization, Newsgathering, and Machine Translation (MT).
NLP is a category of AI that designs programs for machines. The programs allow machines to analyze and process the language of humans. The purpose of NLP is to make machines imitate how humans use language to communicate.
The primary aim of NLP is to make computers better at understanding human language. The purpose gets fulfilled by converting unstructured data into a machine-readable format.
What are Text Summarization and its types?
The amount of textual data produced daily is increasing rapidly. Emails, messages, social media, and news articles generate massive amounts of data. With the increasing volume, the complexity of textual data is also increasing. It is pretty cumbersome to go through huge chunks of text.
Thanks to deep learning, text summarization is a thing. The process transforms long-format text into a summary. The text summary incorporates critical ideas of a long text. Readers find it easier to grasp the lengthy text.
The two primary types of text summarization include the following:
It revolves around extracting essential words and phrases from the input sentence. The idea is to create a summary with the most significant words from the input text.
The technique involves generating new phrases to deliver the meaning of the input sentence. The idea is to emphasize the form strongly. It requires advanced language modeling techniques.
How is Feature Extraction Conducted in NLP?
The features of a sentence are often used to perform document classification or sentiment analysis. For instance, an IMDb movie review contains words like “good” and “great.” Therefore, the movie review can be classified as positive.
A bag of words is also a popular model used for feature generation. A sentence is tokenized, and a group or category is formed from the individual words. The groups or classes can be further explored or exploited to identify specific characteristics. Some common factors that can be identified include the frequency of a particular word.
Knowing the answers to some popular NLP questions will increase your chances of getting hired. But before that, it’s better to earn a certification in a machine learning course from a reputed institution.