The field of computational linguistics constantly presents new challenges and topics for research. Whether it be analyzing word usage changes over time or identifying relationships between pairs of seemingly unrelated words. To this point, we identify Anagrams and Antigrams as words possessing such unique properties. The presented work is an exploration into generating anagrams from a given word and determining whether there exists antigram (semantically opposite anagrams) relationships between the pairs of generated anagrams using GloVe embeddings. We propose a rudimentary, yet interpretable, rule-based algorithm for detecting antigrams. On a small dataset of just 12 antigrams, our approach yielded an accuracy of 39\% which shows that there is much work left to be done in this space.

The lemmatization summary statistics of the Example 3 sentence are also provided in Table 1. In this case, the percentage of words that were normalized for the example reached 100%, which means that all content words (excluding stop words and special characters) are lemmatized. This may be due to the fact that this is a short document, a sentence of 8 words. Unlike the lemmatization statistics of this example, a proportion of words in any typical text document (i.e., longer than a sentence) will normally remain unresolved - words that the algorithm fails to lemmatize in both stages. Overall and as part of evaluating the proposed method, we have tested the algorithm on 120 documents of various lengths including general news articles, and social media posts. For the news articles, we have used extracts (i.e., title and first 1-2 paragraphs) as well as the full articles to see the effect of document length. The results we found for these different document categories are summarized in Table 2. The notations #Docs, Avg Doc Len, and Avg Acc. in the table respectively represent the number of documents, average document length in words, and average lemmatization accuracy. As shown, the results demonstrate that the algorithm achieves a relatively good accuracy of 57% for moderately long documents (e.g.

Traditionally, NLP performance improvement has been focused on improving models and increasing the number of model parameters. NLP vocabulary construction has remained focused on maximizing the number of words represented through subword regularization. We present a novel tokenizer that uses semantics to drive vocabulary construction. The tokenizer includes a trainer that uses stemming to enhance subword formation. Further optimizations and adaptations are implemented to minimize the number of words that cannot be encoded. The encoder is updated to integrate with the trainer. The tokenizer is implemented as a drop-in replacement for the SentencePiece tokenizer. The new tokenizer more than doubles the number of wordforms represented in the vocabulary. The enhanced vocabulary significantly improves NLP model convergence, and improves quality of word and sentence embeddings. Our experimental results show top performance on two Glue tasks using BERT-base, improving on models more than 50X in size.

Example: 'I want to read a book' In the above example there are 6 tokens which are- ('I', 'want, 'to', 'read', 'a' and'book') A type is the class of all tokens containing the same character sequence. In the above example, there are only 5 types which are - 'can, 'you', 'a, 'as' and'canner' as'can', 'as' and'a' are being repeated. In the above example, by deleting period and hyphens between the characters and words we are normalizing the type by making it a term. So the term in the above example is: 'USA' and'antiinflammatory' Example: "Hello everyone.Welcome to the course." The tokens for the given sentence will be -- ['Hello','everyone', 'Welcome', 'to', 'the', 'course'] Welcome to the Natural Language Processing course.

Natural Language Processing (NLP) helps us to communicate or talk with a computer just like we talk to a human. NLP can also be defined as the intersection of Artificial Intelligence (AI), Linguistics and Computer Science, that helps the machine or computer to understand, interpret and manipulate human language. There are two main parts to NLP: 1. Data Preprocessing 2. Algorithm development Here, in this blog we'll be only looking about the first and most important process, "data preprocessing". Data preprocessing is the most essential step for any Machine Learning model. It plays a major role in deciding the performance of the model.

All of the topics will be explained using codes of python and popular deep learning and machine learning frameworks, such as sci-kit learn, Keras, and TensorFlow. Natural Language Processing is a part of computer science that allows computers to understand language naturally, as a person does. This means the laptop will comprehend sentiments, speech, answer questions, text summarization, etc. We will not be much talking about its history and evolution. If you are interested, prefer this link.

In my previous article I discussed about'Stemming' a process where a given word is chopped off to its root word. If you haven't red my previous article on'Stemming' I insist you to read it before moving any further on this article. Unlike stemming which chop off the given word to its root word'Lemmatization' is a almost similar but it always return you the chopped word which has some dictionary meaning. But lemmatization do care if the word it is returning has meaning or no. A word that is returned by lemmatization can also be called a'lemma'.

According to Taweh Beysolow, "Natural Language Processing (NLP) is a subfield of computer science that is focused on allowing computers to understand language in a'natural' way, as humans do." NLP has evolved so rapidly gaining traction in its applications inn artificial intelligence (AI). In this project, we will explore one of the most exciting NLP applications i.e. We will build a machine learning model that can categorize tweets as positive (pro-vaccine), negative (anti-vaccine) or neutral. Stay tuned and let's jump into the project.

"Language is the road map of a culture. It tells you where its people come from and where they are going" -- Rita Mae Brown I would like to share my real-life experience. Back in 2016, I got myself admitted into a renowned engineering university of Bangladesh aiming to be a computer science graduate. At the very onset of my 4th semester, I came to know about the buzzword machine learning. And immediately I got involved in learning Machine Learning and felt longing to learn about the techniques. I started to study from the very basics of ML algorithms.

There is a lot of text present around us. We see it in books, articles, comments, and newspapers. It would be really wise to use this text and convert it into a form that could be easily understood by machine learning and deep learning algorithms. As a result, they would take the processed text and give predictions for different use cases. Natural language processing (NLP) refers to converting natural text into a form that could be used for machine learning purposes.