The deep learning (DL) technique is designed to work on the basis of artificial intelligence (AI) to enhance the performance of expert systems. Also, it is the subset of machine learning where the architecture is based on artificial neural networks. Overall, it creates any sort of mainstream applications in artificial neural networks.
This page is about to prescribe innovations of NLP Deep Learning Projects with its Research Ideas and Future Directions!!!
Generally, the deep learning context used in semantic analytics and NLP utilizes thin AI and DL techniques. This combined technique helps you to interpret the text documents. The document can be in any format such as medical reports, social media comments, survey questions, online reviews, etc. Further, this combined technique helps you to enhance the text analytics functions and acquire useful information from unstructured raw data!!!
How does NLP Works?
Now, let’s see how the NLP works through one sentence as an example. Consider the sentence as “Manu throws the ball over the house”. Here, the sentence will be returned in three different formats as follows,
- Syntax Data
- Subject
- Action – Direct object – Indirect object
- Context Data
- Background
- The statement means a kid playing with a ball
- Semantic Data
- Person
- Throw an object over other – sphere-shaped playful thing – location people living
As a matter of fact, our experts are proficient to model, develop and interpret all sorts of neural network systems. Since we have years of experience in developing NLP deep learning projects. We are here to support you from the beginning of project topic selection to the end of project documentation delivery. Similarly, we also give end-to-end support to our handhold scholars in phases of research. So, let’s jointly create modernization in the NLP-based deep learning field. For your awareness, here we have mentioned the reason that why deep learning is best for NLP?
Is deep learning best for NLP?
Nowadays, deep learning techniques are found to be more efficient over several natural language processing (NLP Deep Learning Projects) operations. Since it uses only one end-to-end neural network in the absence of conventional task-based feature engineering. For better understanding, here we have given you the workflow of sentiment analysis that works on the principle of NLP-based deep learning technique.
Sentiment Analysis
- Step 1 – Collect input data as Themes, Named entities, Short Brief, Intentions, and Topics
- Step 2 – Implement Machine learning algorithms and other Strategies
- Step 3 – Perform the following processes to analyze sentiments
- Sentence Limitations
- Tokenization (Chunking)
- POS Tagging
- Syntax Inquiry
- Sentence Chaining
- Step 4 – Attain Syntax and Semantic Data (Syntax and Concept Matrix) as output
Next, we can see the important research ideas of natural language processing on employing deep learning. These ideas are categorized by highlighting the key processes of NLP. Our developers have sufficient knowledge in handling every aspect of NLP. So, we are ready to develop any sort of application regardless of complexity.
Once, you make contact with us, we share more interesting research ideas in other evolving research areas of NLP with the DL field. And, we also encourage and support our clients to come up with their creative notions.
NLP Research Topics
- Tokenization
- It means splitting the whole text document into multiple chunks like words for machine understanding
- Part of Speech (PoS) Tagging
- It means recognizing the PoS of each token like adjective, noun, article, adverb, etc.
- It performs tagging process of PoS based on NLP operations
- Named Entity Identification
- It addresses the named entities as things, person and place sometimes it also includes emails, mobile contacts, hashtags, etc.
- Sentiment / Emotion Analysis
- It signifies the emotions of the text like neutral, positive, and negative
- It sets weightage score to every theme, entity, category, and topic-based on emotion
- It is a complicated process to execute practically which fully varies from context
Next, we can see about widely used deep learning techniques in NLP. All these techniques have their own purposes and characterization. So, one should be more precise in selecting the appropriate techniques for the proposed research issues. In order to choose the appropriate techniques than the existing approaches, we have to refer to different currently released related research papers.
Then, need to analyze the pros and cons of the existing techniques. At last, choose the optimal one which overcomes the flaws of the exiting approach. When you create a bond with us for NLP Deep Learning Projects, we perform all these processes precisely in your place under the guidance of our field experts.
Deep Learning Algorithms for NLP
- Association Rule Learning
- Eclat and Apriori Algorithm
- Decision Tree Techniques
- Random Forest
- Iterative Dlchotomiser
- Gradient Boosting Machines
- C4.5
- Classification and Regression Tree
- Kernel Techniques
- Linear Discriminate Analysis
- Support Vector Machine
- Radial Basis Function
- Ensemble Techniques
- Random Forest
- AdaBoost and Boosting
- Stacked Generalization
- Bootstrapped Aggregation
- Gradient Boosting Machines
- Regression Techniques
- Logistics Regression
- Locally Estimated Scatterplot Smoothing
- Ordinary Least Squares
- Multivariate Adaptive Regression Splines
- Artificial Neural Network Techniques
- Hopified Network
- Perception
- Back-propagation
- Regularization Techniques
- LASSO
- Elastic Net
- Ridge Regression
- Bayesian Techniques
- Bayesian Belief Network
- Naïve Bayes
- Bayesian Belief Network
- Averaged One-Dependence Estimators
- Dimensionality Reduction Techniques
- Sammon Mapping
- Projection Pursuit
- Principal Component Analysis
- Multidimensional Scaling
- Partial Least Square Regression
- Instance-based Techniques
- Self-Organizing Map
- K-Nearest Neighbour
- Learning Vector Quantization
- Deep Learning Techniques
- Deep Belief Networks
- Restricted Boltzmann Machine
- Stacked Auto-encoders
- Convolutional Network
Additionally, we have also given you some libraries that are majorly used for NLP Deep Learning projects. By the by, it is essential to include appropriate libraries on your project for easy development and for achieving the expected result. In our experience, we found the following libraries have a key player role in project implementation. Further, we also support you in other deep learning libraries that give sure to give the best outcome. We assure you that we smartly suggest to you the best-fitting libraries/packages/modules based on your project requirements.
Deep Learning Libraries for NLP
- TextBlob
- Used to work with the NLTK interface instantly
- CoreNLP
- Used to perform key NLP operations
- SpaCy
- Used to apply NLP operations in industrial applications
- Gensim
- Used to analyze similarities among document
- NLTK
- Used to perform python-based NLP operations
NLTK
For illustration purposes, here we have taken the NLTK as an example from the above list. First of all, NLTK stands for Natural Language Toolkit. It is nothing but libraries/modules that are used for statistical/symbolic NLP operations. Particularly, it is launched for providing a platform for academics and researchers to smoothly work with NLP operations. This toolkit not only supports NLP but also other significant areas such as cognitive science, deep learning, artificial intelligence, and linguistics. Here, we have given you some key features of the NLTK that make NLTK more popular among scholars.
NLTK Features
- It is widely spreading in multiple fields such as engineering studies, the industrial sector, research developments, etc.
- It is furnished with 50+ lexical and corpora resources for a friendly interface
- It is more beneficial for linguistic processing using python
- It is supported in various platforms such as Linux, Mac OS X, and Windows
Best NLP Project using Deep Learning Algorithm
Now, we can see the workflow of NLP deep learning projects through sample demonstration. It is intended to address the major operations such as raw data creation/collection, preprocessing, feature extraction, analysis, classification, and evaluation. Overall, it labels the unlabeled data by undertaking train and test actions. All these are achieved through NLP-based deep learning models using the Keras library. Here, we have given you the general steps for implementation.
- Firstly, employ the K-means clustering technique and label the dataset
- Secondly, train the developed DL models to forecast the classification of tweets
- Thirdly, assess the trained models and detect the possible enhancements
Let’s see the above specified general steps in detail. Basically, the whole process is classified into two main operations such as training and testing. Before executing the testing process, a training process is needed to be formed. In the training process, we train the collected dataset through certain techniques/algorithms. Then the output of the training process is related to testing outcomes for identifying similarities and differences. Here, we have given the training steps followed by testing steps.
Training steps
- At first, collect dataset and perform Tokenizing, Padding, and Sequencing
- Then, stabilize dataset through SMOTE
- Next, divide the dataset into train and test sets
- After that, perform training through LSTM and SimpleRNN models
- At last, assess models
Here, we have explained the above-specified operations in NLP deep learning projects through twitter examples.
Step 1: Data Collection with Requirements
- Requirements – Prior to collecting data from Twitter, make sure that you have the following information
- Registered Twitter Account and API consumer keys
- Access token key and secret key
- Consumer key and secret key
- Jupyter notebook to install Tweepy package
- Collection – For instance: collect US election tweets that have keywords like Trump, Biden, US election, etc.
- Count – 400,000 tweets
- 1 chunk – 20,000 tweets (needed 20 chunks)
Step 2: Data Preprocessing and Feature Extraction
- Preprocessing – Remove unnecessary data for effective visualization
- 1 dataset – 440,000 rows
- Use “pandas” library for splitting data into data frames through chunk size attribute
- Chunk size represent interesting columns
- Use “Pyspark” based cluster as an alternative
- Feature Extraction – Fine-tune the data visualization by small adjustments using NLP-based deep learning approaches
- Divide tweets into tokens/keywords
- Filter tweets only in English
- Take away punctuations and stopwords
- Remove duplicates and missing values
- Eliminate repeated terms “Twitter”, acronyms, and URLs
- Change the words into lower-case characters
Step 3: Data Analysis / Visualization
- Analysis – Analyze and interpret the characteristics of the data by followings
- Bigram
- Unigram
- Wordcloud
- Trigram
- Usual word length in one tweet
- Total word counts in every tweet
Step 4: Data Classification
- Classification – Execute classification process over tweets
- Training models
- LSTM and RNN
Step 5: Data / Model Evaluation
- Evaluation – Assess the performance of the model by certain evaluation metrics. Plot the graph for followings over 10 epochs
- SimpleLSTM: Time Vs Accuracy
- SimpleRNN: Time Vs Loss
- SimpleLSTM: Time Vs Loss
- SimpleRNN: Time Vs Accuracy
Last but not least, now we can see about the future scope of the NLP project using python. Since selecting the topic with no future scope is completely not useful to do research. On the contrary, selecting the topic with future scope will make you do further study on the future and allow your followers to follow in your footsteps. So, we always suggest topics that have a high degree of future scope. Currently, we have found that the following areas have extended future scope. Most importantly, future technological advancements of the NLP-deep learning field are based on the following directions.
NLP using Python
- Cross-Layer Design and Validation
- New Learning Techniques for Labeling Dataset
- Improved Hyperparameter Settings in RNN Model
- Enhanced Solutions for Multi-class issue in Information
- Design of Various Recurrent Neural Network Architectures
- Label Dataset using Ground Truth and Amazon Mechanical Turk
On the whole, we ensure you that we support every aspect of exploration and practical execution of NLP deep learning projects. In specific, we provide services on area identification, topic selection, and problem-solving solution identification, code development in the appropriate tool, performance evaluation, and manuscript writing. And also, we extend our service in other related fields such as artificial intelligence, machine learning, web-semantic analytics, web ontologies, cybersecurity, etc. So, communicate with us to create the best project in your research profession.
