What is the difference between lemmatization and stemming in text processing?
Lemmatization and stemming are both techniques used in text processing to reduce words to their base or root form. While they serve a similar purpose, there are distinct differences between the two approaches. Stemming is a process of removing prefixes and suffixes from words to obtain their root form, known as the stem. This technique
How can NLTK library be used for tokenizing words in a sentence?
The Natural Language Toolkit (NLTK) is a popular library in the field of Natural Language Processing (NLP) that provides various tools and resources for processing human language data. One of the fundamental tasks in NLP is tokenization, which involves splitting a text into individual words or tokens. NLTK offers several methods and functionalities to tokenize
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What is the role of a lexicon in the bag-of-words model?
The role of a lexicon in the bag-of-words model is integral to the processing and analysis of textual data in the field of artificial intelligence, particularly in the realm of deep learning with TensorFlow. The bag-of-words model is a commonly used technique for representing text data in a numerical format, which is essential for machine
How does the bag-of-words model work in the context of processing textual data?
The bag-of-words model is a fundamental technique in natural language processing (NLP) that is widely used for processing textual data. It represents text as a collection of words, disregarding grammar and word order, and focuses solely on the frequency of occurrence of each word. This model has proven to be effective in various NLP tasks
What is the purpose of converting textual data into a numerical format in deep learning with TensorFlow?
Converting textual data into a numerical format is a important step in deep learning with TensorFlow. The purpose of this conversion is to enable the utilization of machine learning algorithms that operate on numerical data, as deep learning models are primarily designed to process numerical inputs. By transforming textual data into a numerical format, we