The term natural language processing refers to the technology in which computers are supported to recognize human languages. The supercomputers are only familiar with the binary formats (0s &1s). Hence natural language processing projects helps the devices to understand & respond to make communications. Natural language processing is often called NLP.

Artificial Intelligence (AI) is one of the irreplaceable technologies in the world. NLP is the subset of artificial intelligence. AI permits the devices, to handle the information received and allows investigating the same as humans do. At the end of this article, you could become masters in the areas that are to be presented for a natural language processing project. This could be possible by keenly observing the entire article. Let’s begin this article with an overview of natural language processing.

“In this article, you will find the necessary concepts that required for the natural language processing projects with crystal clear explanations”

What is Natural Language Processing?

• NLP is the process of meaning extraction from given raw logs of human languages
• It also investigates intentions behind the human languages from different perspectives
• Computers are the medium of natural language processing
• Computers are capable of extracting the exact meaning of the sentences
• E.g. Google voice assistances responds and retrieves the relevant data from web servers

This is an overview of natural language processing. Computerized assistants (Alexa) are responding to the human voices like human beings as you know very well. People are playing with them when they get bored.

 We are never aware of the hurdles behind effective NLP systems in the matter of fact it is very complex to build an effective system. In this regard, let us have the section on how to implement the NLP for your better understanding.

How to Implement NLP?

• Step 1
  • Acquire data/text information from web servers or datasets
• Step 2
  • Clean the text by applying lemmatization & stemming techniques
• Step 3
  • Enrich the data by applying feature engineering methodologies
• Step 4
  • Entrench word2vec techniques to the natural languages
• Step 5
  • Train the model  based on machine learning & neural network systems
• Step 6
  • Measure the performance of the model with several metrics
• Step 7
  • Alter the model according to the requirements
• Step 8
  • Finally install the natural languages processing model

The above listed are the major 8 steps involved in the natural language processing projects implementation. We hope that you understand the concepts as of now listed. NLP tasks are categorized into 3 main tasks named NLP processing, understanding & generation. Are you ready to know about the different tasks involved in NLP? We know that you are curious about them. Come lets we have them

Different Tasks in NLP

• Task 1: Natural Language Processing
  • Named Entity Recognition
  • PoS Tagging
  • Speech/Voice Recognition
  • Image-Text Mappings
  • Sentiment/Emotion Analysis
  • Question Answering Tasks
• Task 2: Natural Language Understanding
  • Multilingual Aspects Analyzing
  • Representing Mapped Inputs
•   Task 3: Natural Language Generation
  • Text Recognition
  • Text Formations
  • Sentence Arrangements

In the foregoing passage, we have detailed to you the tasks involved in the NLP. Our researcher’s crew of NLP is familiar with every concept presented in it. By offering various technical services, we do have sound knowledge of the technical edges. This is incredible results in delivering fruitful natural language processing projects and researches.

Now, we will move on to the article’s flow. As you know that, every technology is subject to some constraints and limitations. Similar to the other technologies NLP are also having several limitations to the users of that technology. Shall we have the next phase? Come let’s try to understand them.

Limitations of Natural Language Processing

• Lack of “Graphical User Interface”
  • It results in ineffective human interaction with the system
• Lack of “Simplified Query Languages”
  • It results in a low responding system utilizing handling the ambiguity in words
• Lack of “Dynamical Functions”
  • It results in poor adaptations with the newfangled domains & functional issues

The above listed are some of the complexities involved in the NLP systems. However, these limitations can be resolved by updating the technology. Our technical team is keenly observing and investigating the area to be improved. As well as we are getting the successful results in the same. Now we can have the section about the objectives of the natural language processing with clear hints.

Objectives of Natural Language Processing

• To extract the connectivity between the corpus & documents
• To convert the data/text into arithmetical/numerical values
• To abstract the meaning of text groups & contents from the document corpus

These are the objectives of NLP technology in general. These objectives can be achieved by applying several techniques. Generally, our researchers in the institute are using these techniques to get the determined outcome in the predicted areas. Yes, guys, the next section is all about the techniques handled in natural language processing. Are you interested to know about that? If yes, let’s tune with this flow.

Techniques of Natural Language Processing

• Text Normalization
  • TFIDF
  • Count Vectors
  • N-Gramming
  • Tokenization
• Stop Word Removing
  • PoS Tagging
  • Phrase Modeling
  • Named Entity Extraction
  • Sentence Recognition
• Lemmatization & Stemming
  • Word Vectors
  • Topic Prototypes

Here, TF-IDF stands for Term Frequency Inverse Document Frequency & PoS stands for Parts of Speech. We hope that this article is offering you the relevant areas that you are surfing for. If you still need any assistance or need any clarifications in these areas you could approach our researchers at any time. In the subsequent passage, we have given you the popular methods that are used for natural language processing.

Popular Methods for Natural Language Processing

• “Statistical Inference” based NLP Methods
  • Statistical inference methods are used to build the reliable NLP models
  • Publicly available datasets are a good example of these methods
• “Machine Learning” based NLP Methods
  • Machine learning methods used to refine the manual errors presented in scripts
  • It automatically checks and corrects the very common terms

The aforementioned are the 2 major and popular methods used for the NLP technology. Programming languages are playing an important role in the script of the task as per requirements. Natural language processing is compatible with several programming languages. Yes, we are going to itemize the best languages that suit best for the NLP systems. Come let us try to understand them!

Which Language is best for Natural Language Processing?

• NLP’s very least unit is represented as sentences
• These sentences are consist of ontology attributes & capitalized named objects
• Ambiguity in the sentences are compiled by several high-resolution programming languages such as,
  • Python
  • Scilab
  • Octave
  • Matlab

The aforementioned are the best suitable languages for natural language processing in general. Here, we would like to give a detailed explanation about one of the best languages. Yes, we are going to enumerate the python programming language for the ease of your understanding. Python is the major language that is utmost compatible with every technological development. Come lets we’s get into the section.

Python

• Python has simplified structures & syntaxes which eases the users in implementing natural language processing projects.
• In addition, they progress the text in an effective manner
• Natural Language Toolkit (NLTK) is the python library
• NLTK deals with the human speech recognitions
• It permits to perform text parsing, tokenization, stemming, etc.
• It also facilitates segmenting the data/text & normalizes the lingual data
• Some of the NLTK libraries are mentioned below,
  • Trigram Tagger
  • Unigram Tagger
  • Bigram Tagger
  • Backoff Tagger
  • Regexp Tagger
  • Patterns & FreqDist
  • Wordnet & Treebank
  • Default Tagger
  • Sequential Backoff Tagger

As of now, we have discussed all the necessary concepts of NLP ranging from basics to programming languages. Our technical team is well proficient in dealing the every programming language to perform the NLP tasks. As this article is titled with the natural language processing projects, we are going to let you know some of the NLP project topics for your reference.

Interesting Research Natural Language Processing Projects Idea

• Automatic Spelling Checks & Corrections
• Grammatical Formations & Rephrasing
• Word Embedded & Dialectal Models
• Dynamic Dialog Mechanisms
• Phonological & Morphological Queries
• Knowledge Transfers & Enhancement
• Word Sensitivity Predictions
• Linguistic Data Study & Navigations
• Verbal Semantics Preprocessing
• Synthesis & Voice Recognition
• Data Recoveries & Extractions

The foregoing passage stated you about some of the major projects ideas in NLP. Apart from this, there are multiple innovative project ideas are in our pockets. If you do want any further details in these areas you are always welcome to have our suggestions.  In this regard, let us learn about the datasets used for the NLP systems in real-time for ease of your understanding. Are you ready to know about them? Come let us try to understand them.

Datasets for Natural Language Processing

• Text Classification Datasets
  • Movie Lens Dataset
    • It has information of 33k movies’ 22 million reviews
    • In addition, they have 5,80,000 massive tags
    • The processes involved with the regression, clustering & classification
  • Teaching Assistant Evaluation Dataset
    • It is the dataset that consists of online teaching reviews
    • For instance, online learning platforms are Byjus, Udame, and so on
    • It has 151+ reviews and performs the classification processes
  • Skytrax-user Reviews Dataset
    • This dataset is all about airline service reviews
    • For example availability of the seats & waiting rooms
    • It has 41396 reviews & performs the regression & classification processes
  • YouTube Comedy Slam Preference Dataset
    • This dataset represents the reviews & voting on the funnier video clips
    • It has 1,138,562 voting & reviews  from YouTube users
    • It performs the classification function
  • Car Evaluation Dataset
    • It reveals the information about the car property ratings
    • It has 1728+ ratings and reviews about car trading
  • Yahoo Music User Rating Dataset
    • It represents the 10 million ratings of every music artists
    • It performs both regression & clustering processes
  • OpinRank Reviews Dataset
    • It has reviews of hotels & cars from TripAdvisor & Edmunds.com
    • Reviews ranging from 259,000 to 42,230
    • It performs the clustering & emotion analyzing processes
  • Amazon Reviews Dataset
    • It the reviews from Amzon.com about the US-based out products
    • It has 82 million reviews & performs clustering & emotion analyzing processes
  • Movie Review Data
    • It offers the subjective (*) & sentiment-based rating (+ or -)
    • This is the website of movie review labeled documents
    • Sentences are labeled according to the polarity/subjective ranks
  • Twitter Sentiment Analysis
    • It has 1,578,627 categorized tweets with ratings
    • 0 represents the negative (-) emotion & 1 represents the (+) positive emotion
    • Nick sanders & Kaggle competition analysis determines the data here
  • Spam & Non-Spam
    • It has the 1324 number of spam and non-spam messages
    • For instance, Gmail segments the spam mails separately
• Other Natural Language Processing Datasets
  • Wordnet Tools & Databases
  • Wikipedia Links Data
  • Wikipedia Databases
  • UseNet Postings Corpus of 2005-2011
  • SMS Spam Collection in English
  • Machine Translation of European Languages
  • Hansards Text Chunks Of Canadian Parliament
  • Gutenberg E-books List
  • Google Web 5-gram 1TB 2006
  • Google Books Ngrams 2.2TB
  • DBpedia 4.58M Things with 583M Facts
  • Clueweb12 FACC
  • Clueweb09 FACC

The foregoing passage has revealed to you the most commonly used datasets in every field presented across the world. The features of every instance are getting used to several processes named clustering, regression and classification, and so on.  The above-listed datasets are converting the raw logs into the text formats to progress them. On the other hand, it is important to measure the performance of the NLP model. They are measured by major metrics named recall, precision, and F1 score.

Generally, prediction proportion and determined results from the NLP models’ accuracy. Accuracy is also a metric used to resolute the performance of the NLP model. Let’s have further explanations in the following passage for your better understanding. Are you ready to know about that? As this is one of the important sections of the article, you are advised to pay your attention here.

Performance Metrics for NLP

• Recall
  • It represents the proportion of true positive values of real positive instances
• Precision
  • It represents the proportion of true positive values of predicted positive instances
• F1 score
  • It computes the weighted average/mean of the precision & recall metrics
  • It deals with false positive & false negative instances to compute the NLP model
  • It is subject to the proper class distribution compared to accuracy metrics

These are the 3 major metrics that are used to evaluate the model in real-time. We hope that you would have understood the concepts needed for natural language processing projects. So far, we have come up with technical facts ranging from basic to advance level. This is one of the emerging new generation technologies, exploring these areas would incredibly magnify the core job opportunities. You will love every area of NLP concepts because it has so many interesting fields in it. 

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