Feature extraction is the process of converting raw text into numerical representations that machines can analyze and interpret. This involves transforming text into structured data by using NLP techniques like Bag of Words and TF-IDF, which quantify the presence and importance of words in a document...
TF-IDF (Term Frequency-Inverse Document Frequency)was another early attempt in the 70s to capture texts as numbers. This approach calculated the weight of each word not only by its frequency in a specific document but also by its commonness across all documents, assigning higher values to less ...
The earliest NLP applications were simple if-then decision trees, requiring preprogrammed rules. They are only able to provide answers in response to specific prompts, such as the original version of Moviefone, which had rudimentary natural language generation (NLG) capabilities. Because there is no ...
NLP relies on various techniques and algorithms, such as bag-of-words, TF-IDF, word embeddings, and recurrent neural networks (RNNs). Computer Vision Computer visionis a field of artificial intelligence that focuses on enabling machines to interpret visual information from their physical surroundings...
Let’s start with basic embedding techniques like one-hot encoding and frequency-based methods such as TF-IDF (Term Frequency-Inverse Document Frequency) and count vectors. In one-hot encoding, each word in the vocabulary is represented as a unique vector in a high-dimensional space, the size...
TF-IDF(Term Frequency-Inverse Document Frequency) andBM25are two classic related algorithms. They're simple and computationally efficient. However, they can struggle with synonyms and don't always capture semantic similarities. If you’re interested in going deeper, refer to our article onSparse Vec...
Representation of the text is done through word frequency rather than word order. Term Frequency-Inverse Document Frequency (TF-IDF) takes into account the importance of every word in the dataset. Frequently occurring words are given more value. Word embeddings capture semantic relationships between ...
Keyword search typically relies on statistical matching using BM25 or TF-IDF and related techniques for ranking results based on the query terms that appear in each document. For example, TF-IDF looks at the inverse frequency of a word in a document (IDF) versus the term frequency of a word...
a classic algorithm known as TF-IDF would look at the number of times keywords appeared in the respective documents (Term Frequency) and at the number of times keywords appeared in all other documents in the repository (Inverse Document Frequency). The latter analysis helps to filter out common...
, textual data has to be put into word vectors, which are vectors of numbers representing the value for each word. Input text can be encoded into word vectors using counting techniques such as Bag of Words (BoW) , bag-of-ngrams, or Term Frequency/Inverse Document Frequency (TF-IDF)....