A definition of natural language processing is to perform both training and testing of large-scale textual, video, image, or audio data to understand the original meaning by machines. For that, it makes the computer learn the natural human speaking language and enables it to generate sensible information. By the by, it primarily works on two main components as natural language generation as NLG and natural language understanding as NLU. Firstly, NLU is used to learn and map the input textual data for forming natural language. Secondly, NLG performs some key text processing tasks like text analysis, sentiment analysis, information extraction, information access, information retrieval, etc.

Are you looking for the best NLP Project Topics from trending research areas? Then, this page provides you with sufficient information to know the current research directions of the NLP field.

            Before diving deep into the NLP research field, it is necessary to know the NLP system architecture. Since it is the base for all the NLP application/system developments. Our developers are talented to design any sort of modern NLP model with the assurance of satisfying all your project requirements. Also, we know smart techniques to improve your system performance in the designing phase itself. Let’s see the basic system design and development of NLP systems. Further, it may include add-on components based on your project needs.

Architecture of NLP Natural Language Processing

• Training
  • Step 1 – Load input data
  • Step 2 – Normalize the data and find the synonyms
  • Step 3 – Filter the key features from text using radiological feature extraction
  • Step 4 – Construct the lexicon dictionary by means of synonyms and words
  • Step 5 – Select the optimal features in terms of word counts and lasso
  • Step 6 – Classify the text using any (e.g. machine learning) algorithms
    • For instances,
      • Logistic Regression
      • Decision Tree
      • Support Vector Machine
      • Random Forest
      • And many more
• Testing
  • Step 1 – Extract radiological features using NLP methods
  • Step 2 – Match with trained classifier (ML-based classification)
  • Step 3 – Display the respected outcomes.

What are the phases of NLP?

Fundamentally, NLP involves five major phases to succeed in their requirements. As well, they are parsing, lexical analysis, semantic analysis, pragmatic analysis, and discourse integration.

Our developers are best at selecting appropriate techniques to process all these phases. Due to the functional benefits of these phases, NLP is largely employed in several real-world application areas. For instance: machine translation, optical character recognition, chatbots, speech analysis, etc.

Although this field is extensively spreading in different real-time domains, NLP has ambiguity problems in dealing with large-scale data. This attracts the current research scholars’ attention to develop more suitable solutions. Further, it also has other problems that are not solved yet efficiently. Here, we have given you some important NLP limitations that are looking for the best research solutions. If you are curious to know apt techniques to solve these and other emerging research limitations, then communicate with us for NLP Projects for Final Year.

Limitations of NLP

Now, we can see about the important NLP project topics that instigate many scholars to do their research on natural language processing. In fact, we have given you the importance of each topic/area in the current NLP PhD / MS study. Moreover, we also guide active final year students until documentation submission.

• Lack of Accurate Preprocessing – Need to choose advanced preprocessing techniques for cleaning the input data. For instance: stop word removing
• Absence of Semantic-Word Meaning – Need to consider contextual data while acquiring the semantic meaning of the text. For instance: “Apple” is the name of both fruit and company
• Large Input Data-Vector Size – Need to focus on vector in the case of big-scale data to control computation time. For instance: organizational data

By the by, all these areas have a high degree of future scope which supports you in both current and future studies. Beyond this, we also extend our help to other emerging research areas. Further, we are ready to share advanced NLP project topics in your interesting research areas.

Top 7 (Natural Language Processing) NLP Project Topics

Language Modelling

The process of forecasting the next word based on the preceding word is known as Language modeling. In specific, it helps to learn the relationships among words and analyze the statistical consistency of the source word. As well, it is the sub-process of NLP in all applications. For instance: handwriting detection, optical character recognition, statistical machine translation, spell correction, speech recognition, etc.

Language models to create speech or text as output:

• New phrase, passages, and files creation
• Recommended sentence continuation creation
• New article headlines creation

Let’s see a few latest language model project ideas,

• Speech-to-Text Synthesis using Generative Model
• English Books, NEWS Articles and Files using Language Model
• Design and Development of Language Model over Wikipedia
• Efficient Language Model using Neural Probabilistic Theory
• Utilization of Recurrent Neural Networks (RNN) over Unreasonable Text

Machine Translation

The process of automated text transformation from the source language to another language is known as machine learning. As well, it turns out to be one of the most important NLP applications. In this process, the input data is composed of a series of symbols and fed to the computer. Then, the computer software transforms the series of symbols into some other requested language. As well, it uses a language model which produces destinated text (i.e., second language) as output. For instance: French to English Translation. Recently, deep learning techniques have majorly created a positive impact on this process.

Language models to create text or speech as output:

• Spanish-Audio to German-Text Translation
• English-Text to Italian-Audio Translation
• English to French Translation

Let’s see a few latest neural machine translation project ideas:

• Jointly Learning for Neural Machine Translation (Translate and Align)
• Wikipedia Content for Neural Machine Translation

Let’s see a few latest deep learning and neural network-based machine translation project ideas:

• English-to-Tamil Translation
• English-to-Korean Translation
• English-to-Spanish Text Translation
• RNN-based Translation Modeling for Joint Language
• Neural Network-based Sequence-to-Sequence Learning

Speech Recognition

The process of identifying and understanding human voice input is speech recognition. The main function of this process is to map acoustic signals which comprise natural language (spoken by a human) with respective sentences (planned by the speaker). It enables the design of a model to generate human-readable text based on audio data.  This automated process of human speech is referred to as Automated Speech Recognition (ASR). Language models to create text as output which constrained on audio input:

• TV shows / Movie Caption Creation
• Radio Commands Generation and Distribution in Driving
• Speech Transcription

Let’s see a few latest speech recognitions over Wikipedia project ideas:

• Deep Recurrent Neural Network (Deep-RNN) for Speech Recognition and Analysis
• Optimized Convolutional Neural Network (CNN) for Speech Synthesis and Investigation
• Recurrent Neural Network (RNN) for Sequence Data Labelling and Classification

Text Classification

The process of predicting given input data to classify the theme/topic of the document is called text classification. Moreover, the classification is performed on the basis of pre-defined classes/labels. The best example of text classification is the categorization of emotion by sentiment analysis. Here, it classifies the emotional tones in terms of “negative” and “positive” aspects.

Let’s see a few latest text classification project ideas,

• Classification of Text over Scholarpedia
• Classification of Document over Wikipedia
• Categorization of Question Type using Sentiment Analysis
• Classification of Email as Spam or Not using Spam Filter
• Classification of Text over Word Order Utilization using CNN
• Classification of Source Language using Language Recognition
• Prediction of Review over Rotten Movie using Sentiment Analysis
• Classification of Sentence into Objective / Subjective Type using CNN
• Classification of Topic (NEWS Articles) over Product Reviews
• Categorization of Fictional Story Genre using Genre Classification
• Classification of Text using Deep Unordered Composition Rivals Syntactic Techniques

Question Answering

The process of finding suitable answers for given questions is known as Question answering. For this purpose, it chooses some key subject areas like text documents to generate possible questions. Then, answer the questions from the specific subject area using any learning approaches. Since it is necessary to learn the question well to answer correctly.

Let’s see a few latest deep learning-based question and answering project ideas:

• Specific Document Questions and Answers
• NEWS Articles-based Answering for Subjective Questions
• Freebase Articles for General Knowledge Q&A

Caption Generation

The process of producing image content as the description is known as Caption generation. For this purpose, it takes an image of any format as input and produces textual description as output (i.e., image content). The supportive image formats are jpg, png, bmp, gif, etc. Further, it also uses a language model to create image captions. Language models to create caption as output which constrained on image input,

• Video Description
• Scene Content Definition
• Photograph Caption Creation

Let’s see a few latest caption generations (for video/image input like web search) project ideas:

• Visual Attention-based Neural Image Content Creation
• Sequence-to-Sequence Caption Creation for Video to Text
• Caption Generation for Streaming Video

Document Summarization

The process of creating a brief overview of a document is known as document summarization. For this purpose, it uses a language model which produces summary/description as output. By the by, this summary is composed of primary elements of the overall document.

Language models to create speech or text as output:

• Document abstract generation
• Document heading generation

Let’s see a few latest document summarization project ideas:

• NEWS Articles for Sentences Summarization
• Wikipedia-based Automated Summary Creation

Last but not least, now we can see about the important developing tools and technologies. When you have chosen your topic, select the appropriate implementation tools and libraries along with it. Certainly, there are several implementation technologies to support the development of problem-solving techniques for NLP research issues. From our experience, we are able to choose the best-fitting technologies by doing a comparative study. In some cases, libraries have only partial common functions. However it may look tricky task, our developers have sufficient knowledge on handpicking the optimal one for your project.

Tools and Libraries for NLP

• NLP ToolKit (NLPTK)
  • Advantages
    • Made up of wide-range of ML algorithms
    • Support intuitive classes and documentation
    • Comprises function to perform bag-of-words to capture features for text classification
• Pattern Recognition
  • Advantages
    • Presence of DOM parser, web crawler, etc.
    • Permit to perform wordnet, SVM, PoS tagging, sentiment analysis, vector space modeling, etc.
• SpaCy
  • Advantages
    • Provide active support of NLP development through in-built word vectors
    • Popularly known as a rapid working framework
    • Simple to use due to individual optimized tool
    • Utilizes neural network for training models
    • Executes OOPs concepts-based comparison for libraries
• Polyglot
  • Advantages
    • Capable to deal with multiple languages
• Gensim
  • Advantages
    • Support latent Dirichlet assignment, word2vec, semantic analysis, document2vec, TF-IDF vectorization, etc.
    • Use deep learning to handle large-scale data and data streams
• Natural Language Toolkit
  • Advantages
    • Include extensions of several third-parties
    • Collection of large-scale NLP libraries
    • Able to perform rapid tokenization on sentences
    • Allow more languages than other libraries
    • Comprises numerous techniques for every NLP task

Further, if you need to know more 150+ NLP Project Topics from your required areas then create a bond with us. Overall, we are here to support you in all the phases of your research starting from project topic selection to thesis/dissertation submission. We believe that you make use of this opportunity to grab NLP research success by holding your hands with us.

 To the great extent, we also support you in other NLP-related research areas such as artificial intelligence, deep learning, machine learning, human-to-machine communication, augmented reality, expert system, etc.