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Introduction to Natural Language Processing

Last Updated : 21 Apr, 2023
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The essence of Natural Language Processing lies in making computers understand the natural language. That’s not an easy task though. Computers can understand the structured form of data like spreadsheets and the tables in the database, but human languages, texts, and voices form an unstructured category of data, and it gets difficult for the computer to understand it, and there arises the need for Natural Language Processing. There’s a lot of natural language data out there in various forms and it would get very easy if computers can understand and process that data. We can train the models in accordance with expected output in different ways. Humans have been writing for thousands of years, there are a lot of literature pieces available, and it would be great if we make computers understand that. But the task is never going to be easy. There are various challenges floating out there like understanding the correct meaning of the sentence, correct Named-Entity Recognition(NER), correct prediction of various parts of speech, coreference resolution(the most challenging thing in my opinion). Computers can’t truly understand the human language. If we feed enough data and train a model properly, it can distinguish and try categorizing various parts of speech(noun, verb, adjective, supporter, etc…) based on previously fed data and experiences. If it encounters a new word it tried making the nearest guess which can be embarrassingly wrong few times. It’s very difficult for a computer to extract the exact meaning from a sentence. For example – The boy radiated fire like vibes. The boy had a very motivating personality or he actually radiated fire? As you see over here, parsing English with a computer is going to be complicated. There are various stages involved in training a model. Solving a complex problem in Machine Learning means building a pipeline. In simple terms, it means breaking a complex problem into a number of small problems, making models for each of them and then integrating these models. A similar thing is done in NLP. We can break down the process of understanding English for a model into a number of small pieces. It would be really great if a computer could understand that San Pedro is an island in Belize district in Central America with a population of 16, 444 and it is the second largest town in Belize. But to make the computer understand this, we need to teach computer very basic concepts of written language. So let’s start by creating an NLP pipeline. It has various steps which will give us the desired output(maybe not in a few rare cases) at the end.

Natural Language Processing (NLP) is a subfield of computer science and artificial intelligence that deals with the interaction between computers and human languages. The primary goal of NLP is to enable computers to understand, interpret, and generate natural language, the way humans do.

NLP involves a variety of techniques, including computational linguistics, machine learning, and statistical modeling. These techniques are used to analyze, understand, and manipulate human language data, including text, speech, and other forms of communication.

Some of the main applications of NLP include language translation, speech recognition, sentiment analysis, text classification, and information retrieval. NLP is used in a wide range of industries, including finance, healthcare, education, and entertainment, to name a few.

Overall, NLP is a rapidly evolving field that is driving new advances in computer science and artificial intelligence, and has the potential to transform the way we interact with technology in our daily lives.

Natural Language Processing (NLP) is a field of Artificial Intelligence (AI) that deals with the interaction between computers and human languages. NLP is used to analyze, understand, and generate natural language text and speech. The goal of NLP is to enable computers to understand and interpret human language in a way that is similar to how humans process language.

  1. Natural Language Processing (NLP) is a field of computer science and artificial intelligence that focuses on the interaction between computers and humans using natural language. It involves analyzing, understanding, and generating human language data, such as text and speech.
  2. NLP has a wide range of applications, including sentiment analysis, machine translation, text summarization, chatbots, and more. Some common tasks in NLP include:
  3. Text Classification: Classifying text into different categories based on their content, such as spam filtering, sentiment analysis, and topic modeling.
  4. Named Entity Recognition (NER): Identifying and categorizing named entities in text, such as people, organizations, and locations.
  5. Part-of-Speech (POS) Tagging: Assigning a part of speech to each word in a sentence, such as noun, verb, adjective, and adverb.
  6. Sentiment Analysis: Analyzing the sentiment of a piece of text, such as positive, negative, or neutral.
  7. Machine Translation: Translating text from one language to another.

NLP involves the use of several techniques, such as machine learning, deep learning, and rule-based systems. Some popular tools and libraries used in NLP include NLTK (Natural Language Toolkit), spaCy, and Gensim.

Overall, NLP is a rapidly growing field with many practical applications, and it has the potential to revolutionize the way we interact with computers and machines using natural language.

NLP techniques are used in a wide range of applications, including:

  • Speech recognition and transcription: NLP techniques are used to convert speech to text, which is useful for tasks such as dictation and voice-controlled assistants.
  • Language translation: NLP techniques are used to translate text from one language to another, which is useful for tasks such as global communication and e-commerce.
  • Text summarization: NLP techniques are used to summarize long text documents into shorter versions, which is useful for tasks such as news summarization and document indexing.
  • Sentiment analysis: NLP techniques are used to determine the sentiment or emotion expressed in text, which is useful for tasks such as customer feedback analysis and social media monitoring.
    Question answering: NLP techniques are used to answer questions asked in natural language, which is useful for tasks such as chatbots and virtual assistants.
  • NLP is a rapidly growing field and it is being used in many industries such as healthcare, education, e-commerce, and customer service. NLP is also used to improve the performance of natural language-based systems like chatbot, virtual assistants, recommendation systems, and more. With the advancement in NLP, it has become possible for computers to understand and process human languages in a way that can be used for various applications such as speech recognition, language translation, question answering, and more.
  • Step #1: Sentence Segmentation Breaking the piece of text in various sentences.

Input : San Pedro is a town on the southern part of the island of Ambergris Caye in the Belize District of the nation of Belize, in Central America. According to 2015 mid-year estimates, the town has a population of about 16, 444. It is the second-largest town in the Belize District and largest in the Belize Rural South constituency. Output : San Pedro is a town on the southern part of the island of Ambergris Caye in the 2.Belize District of the nation of Belize, in Central America. According to 2015 mid-year estimates, the town has a population of about 16, 444. It is the second-largest town in the Belize District and largest in the Belize Rural South constituency. For coding a sentence segmentation model, we can consider splitting a sentence when it encounters any punctuation mark. But modern NLP pipelines have techniques to split even if the document isn’t formatted properly.

  • Step #2: Word Tokenization Breaking the sentence into individual words called as tokens. We can tokenize them whenever we encounter a space, we can train a model in that way. Even punctuations are considered as individual tokens as they have some meaning.

Input : San Pedro is a town on the southern part of the island of Ambergris Caye in the Belize District of the nation of Belize, in Central America. According to 2015 mid-year estimates, the town has a population of about 16, 444. It is the second-largest town in the Belize District and largest in the Belize Rural South constituency. Output : ‘San Pedro’, ’ is’, ’a’, ’town’ and so.

  • Step #3: Predicting Parts of Speech for each token Predicting whether the word is a noun, verb, adjective, adverb, pronoun, etc. This will help to understand what the sentence is talking about. This can be achieved by feeding the tokens( and the words around it) to a pre-trained part-of-speech classification model. This model was fed a lot of English words with various parts of speech tagged to them so that it classifies the similar words it encounters in future in various parts of speech. Again, the models don’t really understand the ‘sense’ of the words, it just classifies them on the basis of its previous experience. It’s pure statistics. The process will look like this:
Input : Part of speech classification model 
Output : Town - common noun
         Is - verb 
         The - determiner
  • And similarly, it will classify various tokens.
  • Step #4: Lemmatization Feeding the model with the root word. 

For example –

There’s a Buffalo grazing in the field. 
There are Buffaloes grazing in the field. 
  • Here, both Buffalo and Buffaloes mean the same. But, the computer can confuse it as two different terms as it doesn’t know anything. So we have to teach the computer that both terms mean the same. We have to tell a computer that both sentences are talking about the same concept. So we need to find out the most basic form or root form or lemma of the word and feed it to the model accordingly. In a similar fashion, we can use it for verbs too. ‘Play’ and ‘Playing’ should be considered as same.
  • Step #5: Identifying stop words There are various words in the English language that are used very frequently like ‘a’, ‘and’, ‘the’ etc. These words make a lot of noise while doing statistical analysis. We can take these words out. Some NLP pipelines will categorize these words as stop words, they will be filtered out while doing some statistical analysis. Definitely, they are needed to understand the dependency between various tokens to get the exact sense of the sentence. The list of stop words varies and depends on what kind of output are you expecting.
  • Step 6.1: Dependency Parsing This means finding out the relationship between the words in the sentence and how they are related to each other. We create a parse tree in dependency parsing, with root as the main verb in the sentence. If we talk about the first sentence in our example, then ‘is’ is the main verb and it will be the root of the parse tree. We can construct a parse tree of every sentence with one root word(main verb) associated with it. We can also identify the kind of relationship that exists between the two words. In our example, ‘San Pedro’ is the subject and ‘island’ is the attribute. Thus, the relationship between ‘San Pedro’ and ‘is’, and ‘island’ and ‘is’ can be established. Just like we trained a Machine Learning model to identify various parts of speech, we can train a model to identify the dependency between words by feeding many words. It’s a complex task though. In 2016, Google released a new dependency parser Parsey McParseface which used a deep learning approach.
  • Step 6.2: Finding Noun Phrases We can group the words that represent the same idea. For example – It is the second-largest town in the Belize District and largest in the Belize Rural South constituency. Here, tokens ‘second’, ‘largest’ and ‘town’ can be grouped together as they together represent the same thing ‘Belize’. We can use the output of dependency parsing to combine such words. Whether to do this step or not completely depends on the end goal, but it’s always quick to do this if we don’t want much information about which words are adjective, rather focus on other important details.
  • Step #7: Named Entity Recognition(NER) San Pedro is a town on the southern part of the island of Ambergris Caye in the 2. Belize District of the nation of Belize, in Central America. Here, the NER maps the words with the real world places. The places that actually exist in the physical world. We can automatically extract the real world places present in the document using NLP. If the above sentence is the input, NER will map it like this way:
San Pedro - Geographic Entity
Ambergris Caye - Geographic Entity
Belize - Geographic Entity
Central America - Geographic Entity
  • NER systems look for how a word is placed in a sentence and make use of other statistical models to identify what kind of word actually it is. For example – ‘Washington’ can be a geographical location as well as the last name of any person. A good NER system can identify this. Kinds of objects that a typical NER system can tag:
People’s names. 
Company names. 
Geographical locations
Product names. 
Date and time. 
Amount of money. 
Events.
  • Step #8: Coreference Resolution: San Pedro is a town on the southern part of the island of Ambergris Caye in the Belize District of the nation of Belize, in Central America. According to 2015 mid-year estimates, the town has a population of about 16, 444. It is the second-largest town in the Belize District and largest in the Belize Rural South constituency. Here, we know that ‘it’ in the sentence 6 stands for San Pedro, but for a computer, it isn’t possible to understand that both the tokens are same because it treats both the sentences as two different things while it’s processing them. Pronouns are used with a high frequency in English literature and it becomes difficult for a computer to understand that both things are same.

ADVANTAGES OR DISADVANTAGES:

Advantages of Natural Language Processing:

  1. Improves human-computer interaction: NLP enables computers to understand and respond to human languages, which improves the overall user experience and makes it easier for people to interact with computers.
  2. Automates repetitive tasks: NLP techniques can be used to automate repetitive tasks, such as text summarization, sentiment analysis, and language translation, which can save time and increase efficiency.
  3. Enables new applications: NLP enables the development of new applications, such as virtual assistants, chatbots, and question answering systems, that can improve customer service, provide information, and more.
  4. Improves decision-making: NLP techniques can be used to extract insights from large amounts of unstructured data, such as social media posts and customer feedback, which can improve decision-making in various industries.
  5. Improves accessibility: NLP can be used to make technology more accessible, such as by providing text-to-speech and speech-to-text capabilities for people with disabilities.
  6. Facilitates multilingual communication: NLP techniques can be used to translate and analyze text in different languages, which can facilitate communication between people who speak different languages.
  7. Improves information retrieval: NLP can be used to extract information from large amounts of data, such as search engine results, to improve information retrieval and provide more relevant results.
  8. Enables sentiment analysis: NLP techniques can be used to analyze the sentiment of text, such as social media posts and customer reviews, which can help businesses understand how customers feel about their products and services.
  9. Improves content creation: NLP can be used to generate content, such as automated article writing, which can save time and resources for businesses and content creators.
  10. Supports data analytics: NLP can be used to extract insights from text data, which can support data analytics and improve decision-making in various industries.
  11. Enhances natural language understanding: NLP research and development can lead to improved natural language understanding, which can benefit various industries and applications.

Disadvantages of Natural Language Processing:

  1. Limited understanding of context: NLP systems have a limited understanding of context, which can lead to misinterpretations or errors in the output.
  2. Requires large amounts of data: NLP systems require large amounts of data to train and improve their performance, which can be expensive and time-consuming to collect.
  3. Limited ability to understand idioms and sarcasm: NLP systems have a limited ability to understand idioms, sarcasm, and other forms of figurative language, which can lead to misinterpretations or errors in the output.
  4. Limited ability to understand emotions: NLP systems have a limited ability to understand emotions and tone of voice, which can lead to misinterpretations or errors in the output.
  5. Difficulty with multi-lingual processing: NLP systems may struggle to accurately process multiple languages, especially if they are vastly different in grammar or structure.
  6. Dependency on language resources: NLP systems heavily rely on language resources, such as dictionaries and corpora, which may not always be available or accurate for certain languages or domains.
  7. Difficulty with rare or ambiguous words: NLP systems may struggle to accurately process rare or ambiguous words, which can lead to errors in the output.
  8. Lack of creativity: NLP systems are limited to processing and generating output based on patterns and rules, and may lack the creativity and spontaneity of human language use.
  9. Ethical considerations: NLP systems may perpetuate biases and stereotypes, and there are ethical concerns around the use of NLP in areas such as surveillance and automated decision-making.

Sure, here are some additional important points and recommended reference books for NLP:

Important points:

  1. Preprocessing: Before applying NLP techniques, it is essential to preprocess the text data by cleaning, tokenizing, and normalizing it.
  2. Feature Extraction: Feature extraction is the process of representing the text data as a set of features that can be used in machine learning models.
  3. Word Embeddings: Word embeddings are a type of feature representation that captures the semantic meaning of words in a high-dimensional space.
  4. Neural Networks: Deep learning models, such as neural networks, have shown promising results in NLP tasks, such as language modeling, sentiment analysis, and machine translation.
  5. Evaluation Metrics: It is important to use appropriate evaluation metrics for NLP tasks, such as accuracy, precision, recall, F1 score, and perplexity.

Here are some important points to keep in mind when it comes to Natural Language Processing:

  1. NLP is a subfield of computer science and artificial intelligence that deals with the interaction between computers and human languages.
  2. The primary goal of NLP is to enable computers to understand, interpret, and generate natural language, the way humans do.
  3. NLP involves a variety of techniques, including computational linguistics, machine learning, and statistical modeling.
  4. NLP is used in a wide range of industries, including finance, healthcare, education, and entertainment.
  5. Some of the main applications of NLP include language translation, speech recognition, sentiment analysis, text classification, and information retrieval.
  6. NLP is a rapidly evolving field that is driving new advances in computer science and artificial intelligence.
  7. NLP has the potential to transform the way we interact with technology in our daily lives.


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