5 NLP Topics And Projects You Should Know About! | by Bharath K | Towards Data Science

Natural Language Processing (NLP) is one of the most intriguing and fascinating aspects of Artificial Intelligence. With the continuous evolution and development of NLP in recent years, it is essential to know about the most advanced and high-quality topics that every individual Data Science enthusiast or aspirant must focus on to achieve a higher rate of success in the field.
The interactions between the software and humans is becoming significantly easier, thanks to the advancements in the field of Natural Language Processing. The AI programs tend to computes, process, and analyze large amounts of natural language data to provide the user with a decent semantic and accurate reply to the users.
Despite the numerous challenges faced in the field of NLP, like making the AI understand the true semantic meaning of the sentences, we have made tremendous progress and have come a long way in the field of Natural Language Processing.
If you are curious about more awesome projects with Python and Data Science, feel free to check out the link below, where fifteen such best projects are covered for 2021 and beyond. In this article, we will focus on the five NLP topics and projects every enthusiast of the subject should know about and aim to achieve perfection!
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One of the most essential Natural Language Processing (NLP) tools for solving many types of problems is the NLTK library. The natural language toolkit (NLTK) offers numerous utility for solving a multitude of Natural Language Processing problems. The NLTK library is very well suited for linguistic-based tasks. It offers a wide range of options for tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning.
The best part about utilizing this library with machine learning and deep learning is you can create numerous high-quality projects. The features of the NLTK library module are broad. There is so much you can do with this library and then use the methods of the bag of words, Term frequency-inverse document frequency (TF-IDF), word to vectors, and other similar methods to approach these tasks and problems.
Below is an example sample code that shows how you can create datasets and essay vectors for large datasets and then make use of hyperparameter tuning along with NLP techniques and machine learning algorithms like Naïve Bayes, Decision trees, and other similar machine learning approaches to solve these complex and complicated problems quite easily.
Sample Code:
vectorizer = CountVectorizer(min_df=10,ngram_range=(1,4), max_features=50000)vectorizer.fit(X_train['essay'].values) # fit has to happen only on train data# we use the fitted CountVectorizer to convert the text to vectorX_train_essay_bow = vectorizer.transform(X_train['essay'].values)X_cv_essay_bow = vectorizer.transform(X_cv['essay'].values)X_test_essay_bow = vectorizer.transform(X_test['essay'].values)
To learn more about how you can simplify your Natural Language Processing projects with Regular Expressions, I would highly recommend all of you to check out the link provided below. It covers how you can utilizer the four basic regular expression operations for a majority of the pre-processing on essays and text datasets for your projects.
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One of the most significant tasks that are accomplished with the help of Artificial Intelligence is the prediction of the next words or sentences that are to occur in the following line or lines. This task is one of the more basic and useful functionalities of Natural Language Processing (NLP) in machine learning and deep learning.
To solve the following task of predicting the concurrent or closest words in machine learning, the concept of similarity can be utilized to achieve the desired results. The concurrent word vector with smaller distances is interlinked. Machine learning algorithms like support vector machines (SVMs), decision trees, and other similar methods can be utilized for solving tasks like next word predictions and other such indistinguishable tasks.
The more popular approach to solving these complex problems is to ensure that we effectively use the concepts of deep learning to solve them. The methods of building neural network architectures using Recurrent Neural Networks is one such common method to solve the task of the next-word predictions. However, due to the issues of exploding and vanishing gradients, other alternatives to RNNs like the long-short term memory (LSTM) are used as an amazing alternative method to approach these tasks.
A unique way of solving these tasks includes the utilization of 1-Dimensional convolution neural networks to create a linking to the word vectors. I would recommend the viewers to check out one of my following projects on the prediction of the next word, where I have implemented the following procedure with the help of a couple of stacked LSTMs.
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One of the most popular applications of Natural Language Processing is the use of chatbots. Chatbots are employed by most major tech-giants, large companies, and even smaller startups on websites to greet people, introduce the fundamental aspects of the company to the visitors, viewers, or audiences, and also answer some of the common questions that the first time site visitors may have.
They are also useful for providing clarifications to some issues that the users might encounter during the browsing of their website. Chatbots can also be deployed for more generic use cases for the majority of the public audiences. The most popular virtual assistants like Google Assistant, Siri, Alexa, etc., among many others, also have the ability to act as chatbots.
The conversations of the chatbots can either be carried out in a traditional method of in-line texting or a more modern approach of speech translation. The use cases of chatbots in the current generation are increasing rapidly. More people and companies are trying to implement them as well. In the field of NLP, the rise of chatbots is an extremely important scenario and something that every enthusiast of the subject must look forward to implementing.
I would highly encourage checking out the numerous methods of working on these chatbots. There are several deep learning algorithms and methods to obtain desirable results on these chatbots. One such unique method is by the construction of these chatbots by making use of the 1-Dimensional Convolutional Neural Networks. Check out the article link provided below to gain a more intuitive understanding of the following.
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Transformers are one of the most significant architectures of the current deep learning era. They aim to solve the sequence to sequence tasks with greater ease. They have the ability to retain long chains of data. And hence, they have a high range of dependability while handling long-range sequences. They utilize the concepts of self-attention to solve complex tasks without the use of sequence-aligned RNNs or convolution.
Transformers are an innovative development in the field of Natural Language Processing. They have the ability to solve complex tasks such as machine translation with greater ease. The topic and project concept of machine translation will be discussed in further detail in the next section of this article.
These transformers also find their utility in numerous tasks such as information retrieval, text classification, document summarization, image captioning, and genome analysis. I would highly recommend doing in-depth research and learning more on the topic of transformers to gain further intuition and understanding of this modern evolution of transformers.
When you trying to talk to a person from another country and you both don’t know a common language, the use of a translator is often required to communicate and agree to terms on a particular contract or deal. Whenever you want to communicate in a foreign language, you can make use of the Google translate function to convert the sentence from one language to another.
Upon typing a particular sentence in English and asking Google translate to convert it into the language of German, the translator usually does a decent job of converting the sentence in English to a sentence in German without changing the actual semantic meaning of a sentence. This task is referred to as Machine Translation.
Machine translation is one of the most useful and significant tasks of natural language processing. Every enthusiast must work on accomplishing the task of machine translation with the help of either the TensorFlow library or the Pytorch library. By using these libraries, you must try to construct a Sequence To Sequence model that can achieve the task of solving the problem of machine translation while achieving the highest possible accuracy. There are a lot of amazing modern methods that are being developed to approach these tasks.
Natural Language Processing is one of the best subjects and sub-topics to learn in Artificial Intelligence. There are so many research papers and articles that are being published continuously. Rapid developments and extensive research are consistently taking place on a daily basis. In the upcoming years, a lot more amazing discoveries to be made in the following field.
In this article, we have discussed five Natural Language Processing (NLP) concepts and project topics that every enthusiast should know about and explore. They constitute the most crucial and vital aspects of these modern-day NLP applications. The demand and significance of these moderately advanced fields are rapidly increasing every day. Hence, this time is one of the most effective periods for aspirants to invest in and learn more.
In my opinion, all of the viewers who are interested and passionate about the field of Natural Language Processing should research more on these topics and try to learn more about the significant aspects of these concepts. After gaining a decent amount of theoretical knowledge, I would highly encourage the viewers to take a dive into the practical world and start implementing these projects on their own.
If you have any queries related to the various points stated in this article, then feel free to let me know in the comments below. I will try to get back to you with a response as soon as possible.
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Thank you all for sticking on till the end. I hope all of you enjoyed reading the article. Wish you all a wonderful day!