Top NLP Interview Questions That You Should Know Before Your Next Interview

Pursuing a Career in Natural Language Processing

Career opportunities in NLP, as with machine learning and AI as a whole, are plentiful and growing by the day. Wherever there is a demand, high-paying jobs are sure to follow. Data scientists, machine learning engineers, computational linguists, and other AI and data engineering professionals who typically leverage NLP in their jobs can expect to earn salaries ranging from $82,000 to $175,000, according to recent data. It’s also a technology that’s expected to remain viable well into the future. 

If you haven’t already mastered it, Simplilearn offers a range of AI and machine learning courses that will get you up to speed on NLP (and so that you can answer those NLP interview questions!) including our focused Natural Language Processing Course. Simplilearn’s unique applied learning model combines the best of online, instructor-led coursework with self-guided videos and hands-on projects that will ensure you’re career-ready upon completion. Best of all, you can access world class skills training from the comfort of your own home at times that best fit your busy schedule.

Even if you’re confident in your skills and training, it’s important to review the types of NLP interview questions that are likely to come up during a job interview. We have compiled some of the most frequently asked NLP interview questions, below, to help you prepare for the next exciting chapter in your career.

1. What is Natural Language Processing?

While this may sound like a softball NLP interview question, the way you answer it will clue the interviewer into how well you grasp NLP as a whole.  

Natural language processing (NLP) is an automated way to understand or analyze the nuances and overall meaning of natural language, extracting key information from typed or spoken language by applying machine learning algorithms. Since meaning is largely derived from its context, NLP seeks to understand language beyond the literal and allow machines to learn through experience.

2. What is an NLP pipeline, and what does it consist of?

Generally, NLP problems can be solved by navigating the following steps (referred as a pipeline):

• Gathering text, whether it’s from web scraping or the use of available datasets
• Cleaning text (through the processes of stemming and lemmatization)
• Representation of the text (bag-of-words method)
• Word embedding and sentence representation (Word2Vec, SkipGram model)
• Training the model (via neural nets or regression techniques)
• Evaluating the model
• Adjusting the model, as needed
• Deploying the model

3. What does “parsing” mean in the world of NLP?

To “parse” a document, in the context of NLP, is to make sense of its grammatical structure. For example, an NLP application parses text by determining the relationship of words and phrases within the text (e.g., which words are the subject, or object, of a given verb?). Parsing will differ from one set of text to another, since its goal is to understand the grammar and what the writer or speaker is trying to convey.  

4. What is “named entity recognition”?

This will likely be one of the NLP interview questions you will get. Named entity recognition (NER) is an NLP process that separates out the components of a sentence to summarize it into its main components, similar to sentence diagramming in grade school. For example, the sentence “Bob moved to New York City in 1997” may be categorized as:

• Bob = name
• New York City = city/location
• 1997 = time

NER helps machines understand the context of the document by identifying data related to “who, what, when, and where.” It’s very useful for scanning documents and responding to chatbots in a customer service environment.

5. What is a “stop” word?

Articles such as “the” or “an,” and other filler words that bind sentences together (e.g., “how,” “why,” and “is”) but don’t offer much additional meaning are often referred to as “stop” words. In order to get to the root of a search and deliver the most relevant results, search engines routinely filter out stop words.

6. What is “feature extraction” and how is it accomplished using NLP?    

The process of feature extraction involves the identification of certain key words or phrases that put it into a particular category, often based on the author’s purported sentiment. For example, a product review by a customer that uses the word “great” or the phrase “good quality” could be summarized as a positive review. The feature extraction process in NLP could enable a given phrase or use of certain words to be “tokenized” into the positive review category.

7. How do you test an NLP model? What metrics are used?

NLP models should be tested for accuracy, but also must consider the likelihood of false positives and false negatives due to the complexity and nuances of language. Therefore, while accuracy is important, you also want to test an NLP model using the following metrics:

• Recall. This is expressed by the following equation: 

True Positive / True Positive + False Negative = True Positive / Total Actual Positive

• Precision. This is expressed by the following equation:

True Positive / True Positive + False Positive = True Positive / Total Predicted Positive

• F1. This is a combination of recall and precision and is expressed by the following equation:

F1 = 2X Precision * Recall / Precision + Recall

8. What are two applications of NLP used today?

There are several real-world NLP applications in use today, including:

• Chatbots: Chatbots (powered by NLP) are often the starting point for customer service interactions, designed to resolve basic customer queries and funnel them to the right personnel if the chatbot is unable to provide resolution. These provide efficiency and cost savings for companies.
• Online translation: Services such as Google Translate use NLP to convert both written and spoken language into other languages and can also help with pronunciation. 

9. What is “term frequency-inverse document frequency?”  

Term frequency-inverse document frequency (TF-IDF) is an indicator of how important a given word is in a document, which helps identify key words and assist with the process of feature extraction for categorization purposes. While “TF” identifies how frequently a given word or phrase (“W”) is used, “IDF” measures its importance within the document. The formulas to answer this NLP interview question are as follows:

• TF(W) = Frequency of W in a document / Total number of terms in the document
• IDF(W) = log_e (Total number of documents / Number of documents having the term W)

Using these formulas, you can determine just how important a given word or phrase is within a document. If the TF-IDF is high, then the frequency of that term is lower; if the TF-IDF is low, then its frequency is higher. Search engines use this to help them rank sites.

10. What is “latent semantic indexing?”  

Undoubtedly, this will be one of the NLP interview questions that you will be asked: What is latent semantic indexing? Latent semantic indexing (LSI) is used to extract useful information from unstructured data by identifying different words and phrases that have the same or similar meanings within a given context. It’s a mathematical method for determining context and obtaining a deeper understanding of the language, widely used by search engines.

11. What is NLTK?

NLTK (Natural Language Toolkit) is a powerful open-source library for Python programming language that provides a comprehensive suite of libraries and tools for natural language processing (NLP) tasks. It is widely used for teaching, research, and development in NLP.

NLTK includes various modules for text processing, stemming, tokenization, parsing, tagging, machine learning, and semantic reasoning techniques. These modules can process raw text and extract meaningful insights and patterns. 

With NLTK, users can perform sentiment analysis, text classification, named entity recognition, part-of-speech tagging, and more. NLTK's extensive documentation and active community make it easy to learn and use. It is also compatible with popular Python libraries like Matplotlib, Scikit-learn, and Pandas, making it a versatile tool for data analysis and visualisation in NLP.

12. What is Syntactic Analysis?

Syntactic analysis is the process of analysing a sentence's syntax or grammatical structure. It involves identifying the different components of a sentence, such as nouns, verbs, adjectives, and adverbs, as well as the relationships between them.

The goal of syntactic analysis is to understand how words are combined in a sentence and how the grammatical rules of a language are used to create meaning. This involves identifying the grammatical function of each word and how it relates to the other words in the sentence.

Syntactic analysis is an integral part of natural language processing (NLP) and is used in a variety of applications such as machine translation, text-to-speech conversion, and chatbots. By understanding the structure of a sentence, computers can better understand the meaning of a text and generate more accurate and meaningful responses.

13. What is Semantic Analysis?

Semantic analysis, also known as natural language processing (NLP), is a branch of computer science that focuses on interpreting human language. It involves using algorithms and techniques to understand the meaning of a text's words, phrases, and sentences.

The semantic analysis aims to enable computers to understand human language in a way that is similar to how people understand it. This involves recognising the individual words in a sentence and understanding the relationships between them and the context in which they are used.

Semantic analysis is used in an array of applications, including sentiment analysis, machine translation, search engines, chatbots, and so on. It is also used in fields such as customer service, marketing, and social media analysis, where understanding the meaning behind human language is crucial for success.

14. List the Components of Natural Language Processing.

The components of Natural Language Processing (NLP) are:

• Text Preprocessing: This involves the cleaning and preparation of the raw text data for further analysis. This may include tokenization, stopword removal, stemming or lemmatization, and part-of-speech tagging.
• Morphological Analysis: This component studies words' internal structure and forms. This may include morpheme segmentation, inflectional morphology, and derivational morphology.
• Syntactic Analysis: This involves the study of the grammatical structure of sentences and the relationships between words. This may include tasks such as parsing, dependency analysis, and constituency analysis.
• Semantic Analysis: This component deals with the meaning of words and sentences. This may include named entity recognition, word sense disambiguation, and semantic role labelling.
• Discourse Analysis: This involves the study of larger units of language beyond the sentence level, such as paragraphs or entire documents. This may include tasks such as text coherence and cohesion analysis.
• Pragmatic Analysis: This component deals with studying the use of language in context and how it is affected by the speaker's intentions, beliefs, and assumptions. This may include speech act recognition, sentiment analysis, and emotion detection.

The abovementioned components work together to enable machines to process, understand, and generate human language.

15. What are Regular Expressions?

Regular expressions, often abbreviated as "regex," are patterns used to match and manipulate strings of text. They are a powerful tool for searching, replacing, and manipulating text data. Regular expressions can be used in a wide variety of programming languages, text editors, and other software tools to perform complex text operations with a single command.

Regular expressions are built from a combination of literal and special characters or metacharacters with specific meanings. For example, the period character "." matches any single character, while the asterisk character "*" matches zero or more occurrences of the preceding character or group.

Regular expressions can validate input, extract data, transform strings, and perform many other text-processing tasks. They are handy for working with large amounts of unstructured text data, such as log files or emails, where manual processing would be time-consuming or error-prone.

While regular expressions can be powerful, they can also be complex and difficult to understand or debug. As such, they require some learning and practice to use effectively.

16. What is Regular Grammar?

A regular grammar is a formal grammar that generates a regular language, a type of formal language that can be recognised by a finite-state automaton, such as a DFA (deterministic finite automaton) or an NFA (non-deterministic finite automaton).

Regular grammar consists of a set of production rules, each of which has one of the following forms:

• A → aB or A → a, where A and B are non-terminal symbols and a is a terminal symbol.
• A → ε, where A is a non-terminal symbol and ε represents the empty string.

In regular grammar, the right-hand side of each production rule consists of either a single terminal symbol or a non-terminal symbol followed by a single terminal symbol. This restriction means that regular grammars are less powerful than other types of grammar, such as context-free grammar. Still, they are simpler to analyse and more amenable to automatic parsing and generation.

Regular grammar is commonly used in programming languages, text editors, and other software tools to specify patterns for matching and manipulating strings of text, often using regular expressions. They are also crucial in theoretical computer science, where they form the basis for many important concepts and algorithms in automata theory, formal language theory, and computational complexity theory.

17. Explain Dependency Parsing in NLP.

Dependency parsing is a technique in natural language processing (NLP) that involves analysing the grammatical structure of a sentence to determine the relationships between words. Specifically, it involves identifying the dependency relationships between words in a sentence, where a dependency relationship represents the grammatical relationship between a headword and its dependencies.

In a dependency tree, each word in the sentence is represented by a node, and directed edges represent their dependency relationships. The head of a dependency relationship is the word that the dependent modifies or describes, while the dependent is the word modified or described by the head.

Several algorithms and techniques for performing dependency parsing, ranging from rule-based to statistical and machine-learning-based approaches. These techniques typically involve analysing the syntactic and semantic features of the sentence, such as part-of-speech tags, word embeddings, and contextual information, to predict the most likely dependency structure.

Dependency parsing has a wide range of applications in NLP, including information extraction, question answering, sentiment analysis, and machine translation. It can also improve the performance of other NLP tasks, such as named entity recognition and text classification, by providing additional contextual information about the relationships between words in a sentence.

18. What is the Difference between NLP and NLU?

NLP and NLU are two closely related fields in artificial intelligence (AI) that deal with processing and understanding natural language.

NLP (Natural Language Processing) refers to using computational techniques to analyse and generate human language. It encompasses a broad range of tasks, such as text classification, language translation, sentiment analysis, and information retrieval.

On the other hand, NLU (Natural Language Understanding) is a subfield of NLP that focuses on understanding the meaning of human language. It involves comprehending the semantic relationships between words and extracting the underlying meaning from a piece of text. NLU aims to teach machines how to understand the nuances of human language by using techniques like entity recognition, semantic parsing, and sentiment analysis.

NLP deals with the broad processing of natural language, while NLU deals with the more specific task of understanding the meaning of natural language.

19. What is the Difference between NLP and CI?

NLP and CI are related to artificial intelligence but have different goals and applications.

NLP stands for Natural Language Processing, a field of AI focusing on the interaction between computers and human language. NLP aims to enable computers to understand, interpret, and generate natural language. Some of the applications of NLP include language translation, sentiment analysis, speech recognition, and chatbots.

On the other hand, CI stands for Computational Intelligence, a field of AI that focuses on developing algorithms and models that can learn from data and make decisions. CI aims to enable computers to perform tasks that usually require human intelligence, such as pattern recognition, optimisation, and prediction. Some of the applications of CI include data mining, machine learning, and evolutionary computation.

In summary, NLP is a subfield of AI that focuses on language-related tasks, while CI is a broader field that encompasses a wide range of AI techniques for solving complex problems.

20. What is Pragmatic Analysis?

Pragmatic analysis in natural language processing (NLP) studies how people use language in context to achieve specific communicative goals. Pragmatics deals with how people use language to convey meaning beyond the literal interpretation of words and phrases. It involves analysing the social, cultural, and situational factors influencing language use and interpretation.

Pragmatic analysis in NLP involves developing algorithms and models that can automatically analyse and understand the pragmatics of natural language. This can include tasks such as identifying speech acts (e.g. requests, commands, questions), detecting implicatures (meaning conveyed indirectly), and determining the intended meaning of an utterance based on context and background knowledge.

The pragmatic analysis is a vital area of NLP because it enables machines to understand language in a more human-like way, which can improve the accuracy and naturalness of language processing systems. For example, a virtual assistant capable of understanding the intended meaning behind a user's request can provide more accurate and helpful responses.

21. What is Pragmatic Ambiguity?

Pragmatic ambiguity in Natural Language Processing (NLP) refers to the situation where the meaning of a sentence or phrase is unclear or ambiguous because of the context in which it is used rather than due to any inherent ambiguity in the sentence structure or the meanings of individual words. In other words, ambiguity arises from how the sentence is used and interpreted in a particular context.

For example, consider the sentence, "I saw her duck". With additional context, whether the speaker saw the woman's duck (verb) or a duck belonging to her (noun) is clear. The sentence's meaning can only be determined from the context in which it is used. If the preceding sentence was "She has a pet duck", then the meaning is likely to be the latter interpretation. However, if the preceding sentence was "She performed a dive in the pool, " the meaning is likely to be the former interpretation.

Pragmatic ambiguity can be challenging for NLP systems to handle because it requires a deep understanding of the context in which the sentence is used, as well as the ability to reason about the multiple possible interpretations of the sentence.

22. What are Unigrams, Bigrams, Trigrams, and N-grams in NLP?

In Natural Language Processing (NLP), unigrams, bigrams, trigrams, and N-grams are different types of text tokens used to represent the frequency of words or sequences of words in a given corpus of text.

• Unigrams: A unigram is a word in a text corpus. For example, in the sentence "I love to play football", the unigrams are "I", "love", "to", "play", and "football".
• Bigrams: A bigram is a sequence of two adjacent words that occur together in a text corpus. For example, in the same sentence, "I love to play football", the bigrams are "I love", "love to", "to play", and "play football".
• Trigrams: A trigram is a sequence of three adjacent words that occur together in a text corpus. For example, in the same sentence, "I love to play football", the trigrams are "I love to", "love to play", and "to play football".
• N-grams: An N-gram is a sequence of N adjacent words that occur together in a text corpus. The value of N can be any positive integer, although unigrams, bigrams, and trigrams are the most commonly used. For example, a 4-gram in the sentence "I love to play football" would be "I love to play".

N-grams are often used in language modelling and text classification tasks in NLP. By counting the frequency of occurrence of different N-grams in a corpus of text, we can gain insights into the language patterns and structures in the text, which can then be used to develop models for various NLP tasks.

23. What are the Steps Involved in Solving an NLP Problem?

Solving an NLP (Natural Language Processing) problem involves several steps. Here is a general overview of the steps involved:

• Define the problem: The first step is to define the NLP problem you want to solve. This could be anything from sentiment analysis to text classification, text generation, named entity recognition, and more.
• Gather data: The second step is to gather data for your problem. This may involve scraping websites, collecting data from databases, or using pre-existing datasets.
• Preprocess data: The third step is to preprocess the data. This includes cleaning and transforming the data, such as removing stop words, stemming, lemmatization, and converting text to lowercase.
• Feature extraction: The fourth step is to extract relevant features from the preprocessed data. This may involve using techniques such as bag-of-words, TF-IDF, word embeddings, and more.
• Model training: The fifth step is to train a machine learning model on the extracted features. This may involve using algorithms such as Naive Bayes, SVM, Random Forest, or deep learning models like LSTM, Transformer, etc.
• Model evaluation: The sixth step is to evaluate the model's performance on the test dataset. This may involve using metrics such as accuracy, precision, recall, F1-score, or other relevant evaluation measures.
• Model tuning: The seventh step is to tune the model parameters to improve its performance. This may involve using cross-validation, grid search, random search, or Bayesian optimisation techniques.
• Deployment: The final step is to deploy the model in a real-world application. This may involve integrating the model into a web application, mobile app, or other systems.

24. What are Precision and Recall?

Precision and recall are two common evaluation metrics used in machine learning, particularly in information retrieval and classification. They assess the quality of a model's predictions and are particularly useful in evaluating models for NLP problems.

Precision refers to the proportion of true positives (correctly classified positive examples) among all predicted positives (examples classified as positive by the model).

In other words, precision measures how often the model correctly identifies a positive example out of all the examples it predicted as positive. A high precision score indicates that the model makes few false positive errors.

In contrast, recall refers to the proportion of true positives among all actual positives in the dataset. In other words, recall measures how often the model correctly identifies a positive example out of all the positive examples in the dataset. A high recall score indicates that the model makes a few false negative errors.

In summary, Recall and precision are measures of a model's accuracy, but they focus on different aspects of the model's performance. High precision indicates that a model makes few false positive errors, while high recall indicates that a model makes few false negative errors. In practice, the choice of which metric to prioritise depends on the specific problem and the trade-offs between precision and recall that are acceptable in that context.

25. What is F1 Score in NLP?

In Natural Language Processing (NLP), the F1 score is a commonly used metric for evaluating the performance of a classification model. It measures the accuracy and completeness of a model's predictions, considering both precision and recall.

Precision measures the proportion of true positive predictions (i.e., correctly classified samples) out of all positive predictions made by the model. Recall, on the other hand, measures the proportion of true positive predictions out of all actual positive samples in the data.

The F1 score is the harmonic mean of precision and recall and is calculated as follows:

F1 = 2 * (precision * recall) / (precision + recall)

An F1 score of 1 indicates perfect precision and recall, while a score of 0 indicates that the model makes no correct predictions. In general, higher F1 scores indicate better model performance.

26. How to Tokenize a Sentence Using the NLTL Package?

NLTK (Natural Language Toolkit) is a popular Python package for natural language processing tasks. Tokenization is the procedure of breaking a sentence or a document into individual words or tokens. Here's how you can tokenize a sentence using the NLTK package:

First, you must install NLTK using the command !pip install nltk if you still need to install it.

Next, you can import the nltk module and download the necessary data by running the following code:

import nltk

nltk.download('punkt')

Now that you have the nltk module and the necessary data, you can tokenize a sentence using the word_tokenize() function, which is part of the nltk.tokenize module. Here's an example:

from nltk.tokenize import word_tokenize

sentence = "Tokenization is breaking a sentence or a document into individual words or tokens."

tokens = word_tokenize(sentence)

print(tokens)

Output:

['Tokenization', 'is', 'the', 'process', 'of', 'breaking', 'a', 'sentence', 'or', 'a', 'document', 'into', 'individual', 'words', 'or', 'tokens', '.']

As you can see, the word_tokenize() function has broken down the sentence into individual words or tokens, including punctuation.

27. Explain Stemming with the Help of an Example.

Stemming is the procedure of reducing inflected words to their root or base form. This is often done to normalise text for natural language processing tasks such as text classification or information retrieval. For example, the words "running", "runner", and "runs" all have the same root word, "run".

Here's an example of how stemming works using the Porter stemming algorithm, one of the most commonly used stemming algorithms:

Original words: run, runs, running, runner

Stemmed words: run, run, run, runner

As you can see, all words have been reduced to their base form, "run". This can be useful for applications that need to identify a word's root without considering its specific form, such as when searching a database of documents for all occurrences of a particular word regardless of its tense or other variations.

28. Explain Lemmatization with the Help of an Example.

Stemming is reducing words to their base or root form, allowing word variations to be treated as the same word. For example, the words "jumping," "jumps," and "jumped" are all variations of the base word "jump." We can identify these words as the same word by stemming them to their root form.

Let's take the example sentence: "The cat is jumping over the fence."

Using stemming, we can reduce the words to their root form as follows:

• "The" remains as it is since it is a stop word.
• "cat" remains as it is since it is already in its base form.
• "is" remains as it is since it is a stop word.
• "jumping" is stemmed to "jump"
• "over" remains as it is since it is a stop word.
• "the" remains as it is since it is a stop word.

"fence" remains as it is since it is already in its base form.

So the stemmed version of the sentence becomes: "The cat jump over the fence."

As you can see, stemming allows us to reduce the words to their base form and treat variations of the same word as one. This is useful in natural language processing tasks such as information retrieval, text mining, and sentiment analysis.

29. What is Parts-of-Speech Tagging?

Parts-of-speech tagging, or POS tagging, is analysing a text and assigning each word a part of speech based on its role in the sentence. Parts of speech refer to the grammatical categories that words are classified into, such as nouns, verbs, adjectives, adverbs, pronouns, prepositions, conjunctions, and interjections.

POS tagging is fundamental in natural language processing (NLP) tasks, such as text classification, sentiment analysis, machine translation, and information retrieval. The tagging process is typically done using a computational model, such as a statistical model or a neural network, trained on a large corpus of text data.

POS tagging is vital because it helps computers understand the meaning of a text by identifying the grammatical structure of sentences. This allows machines to perform more sophisticated analyses, such as identifying the subject and object of a sentence, or recognising patterns in using certain parts of speech.

30. How to Check Word Similarity Using the Spacy Package?

Spacy is a popular Python package for Natural Language Processing (NLP) tasks. It provides an efficient way to perform word similarity using its built-in models.

To check the similarity between two words using the Spacy package, you can follow these steps:

• Install the Spacy package using pip:

pip install spacy

• Download the Spacy model you want to use. For example, you can download the default English model using the following command:

python -m spacy download en_core_web_sm

• Load the Spacy model using the 'load' method:

word1 = nlp("apple")

word2 = nlp("banana")

similarity_score = word1.similarity(word2)

print(similarity_score)

import spacy

nlp = spacy.load('en_core_web_sm')

• Use the 'similarity' method to get the similarity score between two words:

word1 = nlp("apple")

word2 = nlp("banana")

similarity_score = word1.similarity(word2)

print(similarity_score)

The 'similarity' method returns a float value between 0 and 1, where 0 means the words are entirely dissimilar, and 1 means the words are identical in meaning.

31. What is the Naive Bayes algorithm, When can we use this algorithm in NLP?

The Naive Bayes algorithm is a supervised machine learning technique based on the Bayes theorem. It is a probabilistic classifier frequently used in NLP applications like sentiment analysis, which pinpoints a text corpus's sentimental or emotional undertone. When previous knowledge is available, the Bayes theorem is used to calculate the probability of a hypothesis. Conditional probabilities have a role.

32. What is Text Summarization?

The challenge of writing a succinct, precise, and fluid summary of lengthier text content is known as text summarization. To better assist in discovering relevant information and consuming relevant information more quickly, approaches for automatic text summarizing are urgently needed. We require it for the reasons listed below:

• Summaries shorten reading sessions.
• Summaries facilitate the selection of documents for study.
• Indexing performance is improved via automatic summarization.
• Compared to human summarizers, automatic systems are less prejudiced.
• Because they offer individualized information, personalized summaries are helpful in question-answering systems.
• Commercial abstract services can handle more texts using automatic or semi-automatic summarizing techniques.

33. What is information extraction?

Information extraction involves taking data from unstructured text sources to locate entities, classify them, and store them in a database. These items are combined with their semantic descriptions and linkages from a knowledge network using semantically enhanced information extraction, also known as a semantic annotation. This solution addresses numerous issues in business content management and knowledge discovery by adding metadata to the extracted ideas. 

The technique of removing particular (pre-specified) information from text sources is known as information extraction. One of the most elementary instances is when your email extracts the information you need to put into your calendar. Legal documents, medical records, social media interactions and streams, and other freely flowing textual sources are additional sources from which structured information can be extracted.

34. What is a Bag of Words?

The bag-of-words model is one way to encode text data when analyzing text using machine learning methods. The bag-of-words method has been used to tackle problems like language modeling and document classification since it is simple to understand and use.

A bag of words is a textual illustration that shows where words appear in a manuscript. There are two components:

• A collection of well-known terms.
• A metric for the number of well-known words.

It is referred to as a "bag" of words since any details on the arrangement or structure of the words inside the document are ignored. The model doesn't care where in the document recognized terms appear; it is simply interested in whether they do.

35. What are the best NLP Tools?

• MonkeyLearn 
• Aylien 
• IBM Watson
• Google Cloud 
• Amazon Comprehend
• NLTK 
• Stanford Core NLP 
• TextBlob

36. What is NER?

Named entity recognition (NER), also known as entity chunking, extraction, or identification, is the process of locating and classifying significant pieces of data (entities) in text. Any word or group of words that constantly refers to the same item is considered an entity. Each recognized object is put into a specific category. NER machine learning (ML) models, for instance, may identify the word "super.AI" in a text and categorize it as a "Company."

Natural language processing (NLP), a branch of artificial intelligence, includes NER. NLP is concerned with the processing and analysis of natural language by computers, which refers to any language that has emerged organically instead of artificially, like coding languages.

37. What are the possible features of a text corpus in NLP?

• Word count in a document.
• The presence of a word in a document is a boolean characteristic.
• Word vector notation
• Tag: part of speech
• Dependency grammar basics.
• As a feature, the entire document.

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