1) What is the full form of NLP? / What is Natural Language Processing?
NLP stands for “Natural Language Processing”. NLP is a field of computer science that deals with communication between computer systems and humans. This technique uses Artificial Intelligence and Machine Learning to create automated software that helps understand the human spoken languages and extract useful information from the data gathered from the audio.
The techniques used in NLP allow computer systems to process and interpret data in the form of natural languages. It designs algorithms that can extract meaning from large datasets in audio or text format by applying machine learning algorithms. In other words, we can say that NLP is software that uses artificial intelligence and machine learning algorithms to understand natural languages or the way human beings read and write in a language and extracts required information from such data.
2) What are the most used NLP (Natural Language Processing) Terminologies?
Following is the list of most used NLP (Natural Language Processing) Terminologies:
Preprocessing: This is a method used to remove unwanted text or noise from the given text and make it “clean.” It is the first step of any NLP task. s
Documents: Documents are the body of text and are collectively used to form a corpus.
Corpus, or Corpora (Plural): It is a collection of text of similar type, for example, movie reviews, social media posts, etc.
Vocabulary: It is a group of terms used in a text or speech.
Out of Vocabulary: It specifies the terms not included in the vocabulary. We put the terms created during the model’s training in this category.
Tokenization: It is used in NLP to break down large sets of text into small parts for easy readability and understanding. Here, the small parts are referred to as ‘text’ and provide a piece of meaningful information.
N-grams: It specifies the continuous sequence (similar to the power set in number theory) of n-tokens of a given text.
Parts of Speech (POS): It specifies the word’s functions, such as a noun, verb, etc.
Parts of Speech Tagging: It is the process of tagging words in the sentences into different parts of speech.
3) What are some real-life applications / real-world examples of Natural Language Processing (NLP)?
Some real-life applications of NLP or Natural Language Processing are as follows:
Spelling/Grammar Checking Apps: Spelling and grammar checking applications are real-life examples of Natural Language Processing. These apps are mainly used in mobile applications and websites that facilitate users to correct grammar mistakes in the entered text rely on NLP algorithms. They also recommend the best possible substitutes that the users might type. This is possible because of specific NLP models being used in the backend.
Google Translate: Google Translate is the most famous application of Natural Language Processing. Using this, you can convert your written or spoken sentences into any language. You can also get the correct pronunciation and meaning of a word by using Google Translate. The Google Translate application uses some advanced techniques of Natural Language Processing to provide translation of sentences into various languages.
Chatbots apps: Chatbots applications provide a better customer support service. Many websites and companies use this to offer customer support through these virtual bots that chat with the user and resolve their problems. Many companies use chatbots for 24/7 service to resolve the basic queries of customers. Generally, it filters the basic issues that do not require an interaction with the companies’ customer executives. It makes the customers feel that the customer support team quickly attends them. If a chatbot cannot resolve any user’s query, it forwards it to the support team while still engaging the customer. Chatbots also make companies capable of building cordial relations with customers. These all are only possible because of Natural Language Processing.
4) What are the most used NLP (Natural Language Processing) Terminologies?
Following is the list of most used NLP (Natural Language Processing) Terminologies:
Embeddings (Word): This process is used to embed each token as a vector and then pass it into a machine learning model. We can apply embeddings also on phrases and characters, apart from words.
Stop Words: These are used to remove the unwanted text from further text processing, for example, a, to, can, etc.
Transformers: Transformers are deep learning architectures that can parallelize computations. They are used to learn long-term dependencies.
Normalization: This is a process of mapping similar terms to a canonical form, i.e., a single entity.
Lemmatization: Lemmatization is a type of normalization used to group similar terms to their base form according to their parts of speech. For example, talking and talking can be mapped to a single term, talk.
Stemming: Stemming is also a type of normalization similar to lemmatization. But, it is different in the term that it segregates the words without the parts of speech tags. It is faster than lemmatization and also be more precise in some cases.
5) What are some of the major components of Natural Language Processing?
Following is a list of some of the major components of Natural Language Processing:
Entity extraction: It is used for segmenting a sentence to identify and extract entities, such as a person (real or fictional), organization, geographies, events, etc. 85
Pragmatic analysis: Pragmatic analysis extracts information from the input text. It is part of the process of data extraction.
Syntactic analysis: Syntactic analysis is used for the proper ordering of words.
6) What do you understand by Dependency Parsing in NLP or Natural Language Processing?
In Natural Language Processing, Dependency Parsing is a process of assigning syntactic structure to a sentence and identifying its dependency parses. This is an important process to understand the correlations between the “head” words in the syntactic structure. That’s why it is also known as syntactic parsing.
The process of dependency parsing becomes a little complex if there are more sentences that have more than one dependency parses. Multiple parse trees are known as ambiguities. The main task of dependency parsing is to resolve these ambiguities to assign a syntactic structure to a sentence effectively. It is also used in semantic analysis apart from syntactic structuring.
7) What are some most common areas of usage of Natural Language Processing?
Following is a list of some most common areas of usage of Natural Language Processing:
Semantic Analysis
Text classification
Automatic summarization
Questioning Answering
Some real-life examples of Natural Language Processing are chatbots, IOS Siri, Google Assistant, Amazon echo, Spelling, grammar checking apps, and Google translate.
8) What do you understand by NLTK in Natural Language Processing?
In Natural Language Processing, NLTK stands for Natural Language Toolkit. It is a Python library used to process data in human spoken languages. NLTK facilitates developers to apply parsing, tokenization, lemmatization, stemming techniques, and more to understand natural languages. It is also used for categorizing text, parsing linguistic structure, analyzing documents, etc.
Following is the list of some libraries of the NLTK package that are often used in NLP:
DefaultTagger
UnigramTagger
RegexpTagger
backoff_tagger
SequentialBackoffTagger
UnigramTagger
BigramTagger
TrigramTagger
treebank
wordnet
FreqDist
Patterns etc.
9) What is the use of TF-IDF? Why is it used in Natural language Processing?
In Natural language Processing, tf-idf, TF-IDF, or TFIDF stands for Term Frequency-Inverse Document Frequency. It is a numerical statistic used to specify how important a word is to a document in a collection or the collection of a set.
10) What is the difference between formal and natural languages?
The main difference between a formal language and a natural language is that a formal language is a collection of strings. Each string contains symbols from a finite set called alphabets. On the other hand, a natural language is a language that humans use to speak. This is completely different from a formal language as it contains fragments of words and pause words like uh, um, etc.
11) What are the tools used for training NLP models?
The most common tools used for training NLP models are NLTK, spaCY, PyTorch-NLP, openNLP, etc.
12) What do you understand by information extraction? What are the various models of information extraction?
In Natural Language Processing, information extraction is a technique of automatically extracting structured information from unstructured sources to get useful information. It extracts information such as attributes of entities, the relationship between different entities, and more.
Following is a list of various models of information extraction in Natural Language Processing:
Fact Extraction Module
Entity Extraction Module
Sentiment Analysis Module
Tagger Module
Relation Extraction Module
Network Graph Module
Document Classification and Language Modeling Module
13) What are the stop words in Natural Language Processing?
In Natural Language Processing, stop words are regarded as useless data for a search engine. It includes the words like articles, prepositions, was, were, is, am, the, a, an, how, why, and many more. The algorithm used in Natural Language Processing eliminates the stop words to understand and analyze the meaning of the sentences. Eliminating the stop words is one of the most important tasks for search engines to process data.
Software developers design the algorithms of search engines so that they ignore the use of stop words and only show the relevant search result for a query.
14) What is Bag of Words in Natural Language Processing?
Bag of Words is a commonly used model in Natural Language Processing that depends on word frequencies or occurrences to train a classifier. This model creates an occurrence matrix for documents or sentences without depending on their grammatical structure or word order.
15) What do you understand by semantic analysis? What are the techniques used for semantic analysis?
Semantic analysis is a process that makes a machine understand the meaning of a text. It uses several algorithms to interpret the words in sentences. It is also used to understand the structure of a sentence.
Following are the techniques used for semantic analysis:
Named entity recognition: This technique is used to specify the process of information retrieval that helps identify the entities like the name of a person, organization, place, time, emotion, etc.
Natural language generation: This technique specifies a process used by the software to convert the structured data into human spoken languages. By using natural language generation, organizations can automate content for custom reports.
Word sense disambiguation: It technique is used to identify the sense of a word used in different sentences.
16) What is pragmatic ambiguity in NLP?
Pragmatic ambiguity is used to specify words with more than one meaning, and they can be used in any sentence depending on the context. In pragmatic ambiguity, words have multiple interpretations.
Pragmatic ambiguity occurs when the meaning of the words is not specific. For example, if a word gives different meanings. Due to pragmatic ambiguity, a sentence can have multiple interpretations. Sometimes, we come across sentences that have words with multiple meanings, making the sentence open to interpretation.
17) What is Latent Semantic Indexing (LSI)? What is the use of this technique?
LSI or Latent Semantic Indexing is a mathematical technique used in Natural Language Processing. This technique is used to improve the accuracy of the information retrieval process. The LSI algorithm is designed to allow machines to detect the latent correlation between semantics.
The machines generate various concepts to enhance information understanding. The technique used for information understanding is called singular value decomposition. It is mainly used to handle static and unstructured data. This is one of the best-suited models to identify components and group them according to their types.
Latent Semantic Indexing or LSI is based on a principle that specifies that words carry a similar meaning when used in a similar context. The computational LSI models are slow compared to other models, but they can improve a text or document’s analysis and understanding.
18) What do you understand by MLM in Natural Language Processing?
In Natural Language Processing, MLM is a term that stands for Masked Language Model. It helps learners understand deep representations in downstream tasks by taking the output from the corrupt input.
This model is mainly used to predict the words used in a sentence.
19) What are the most commonly used models to reduce data dimensionality in NLP?
The most commonly used models to reduce the dimensionality of data in NLP are TF-IDF, Word2vec/Glove, LSI, Topic Modelling, Elmo Embeddings, etc.
20) What is Lemmatization in Natural Language Processing?
Lemmatization is a process of doing things properly using a vocabulary and morphological analysis of words. It is mainly used to remove the inflectional endings only and return the base or dictionary form of a word, known as the lemma. It is just like cutting down your beard or shaving to get the original shape of your face.
For example: girl’s = girl, bikes= bike, leaders= leader etc.
So, the main task of Lemmatization is to identify and return the root or original words of the sentence to explore various additional information.
21) What is Stemming in Natural Language Processing?
Stemming is a process of extracting the base form of a word by removing the affixes from them. It is just like cutting down the branches of a tree to its stems.
For example: After stemming, the words go, goes, and going would be ‘go’.
Search engines use stemming for indexing the words. It facilitates them to store only the stems rather than storing all forms of a word. By using stemming, the search engines reduce the size of the index and increase the retrieval accuracy.
22) What is the difference between Stemming and Lemmatization in NLP?
Stemming and Lemmatization are both the text normalization techniques used in Natural language Processing. Both are used to prepare text, words, and documents for further processing. They seem very similar techniques, but there are quite differences between them. Let’s see the main differences between them:
Stemming
Lemmatization
Stemming is the process of extracting the base form of a word by removing the affixes from them. It produces the morphological variants of a root/base word. Stemming programs are commonly known as stemming algorithms or stemmers.
Lemmatization is a more advanced process and looks beyond word reduction, just like stemming. It considers a full vocabulary of a language and applies a morphological analysis to words.
For example, the lemma of ‘went’ is ‘go’, and the lemma of ‘mice’ is ‘mouse’.
Stemming is not as much informative as Lemmatization. It is a somewhat crude method for cataloging related words. It essentially cuts letters from the end until the stem is reached.
Lemmatization is much more informative than simple Stemming; that is why Spacy has opted to only have Lemmatization available instead of Stemming.
Stemming is not as efficient as Lemmatization. This method works fairly well in most cases, but unfortunately, English has many exceptions requiring a more sophisticated process.
Lemmatization is more efficient than Stemming as it works well in exceptional words.
Following are some examples of Stemming:
run: run
runner: runner
running: run
ran: ran
runs: run
easily: easili
fairly: fair etc.
Following are some examples of Lemmatization:
run: run
runner: run
running: run
ran: run
runs: run
goes: go
go: go
went: go
saw: see
mice: mouse
23) What is tokenization in Natural Language Processing?
In Natural Language Processing, tokenization is a method of dividing the text into various tokens. These tokens are the form of the words, just like a word forms into a sentence. In NLP, the program computers process large amounts of natural language data. These large amounts of natural language data have to be cut into shorter forms. So, tokenization is an important step in NLP that cuts the text into minimal units for further processing.
23) Which NLP techniques use a lexical knowledge base to obtain the correct base form of the words?
The NLP techniques that use a lexical knowledge base to obtain the correct base form of the words are Lemmatization and stemming.
25) What are some open-source libraries used in NLP?
Some popular open-source libraries used in NLP are NLTK (Natural Language ToolKit), SciKit Learn, Textblob, CoreNLP, spaCY, Gensim, etc.
26) What are the key differences between NLP and NLU?
Following is the list of key differences between NLP and NLU:
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