Named Entity Recognition (NER) is a task in NLP that involves identifying and classifying named entities in text into pre-defined categories such as person names, organizations, locations, and more. This technique finds application in various fields, including information extraction, question answering, and recommender systems. For example, in a news article, NER can help identify the names of politicians, companies, and places mentioned.
Topic modeling is a technique used to extract latent topics from a collection of documents. It helps in discovering the main themes or subjects present in a given dataset. One popular topic modeling algorithm is Latent Dirichlet Allocation (LDA). For instance, by applying topic modeling on a set of news articles, we can group them into different categories like business, politics, sports, etc.
Sequence-to-Sequence (Seq2Seq) models are widely used in tasks like machine translation, chatbot development, and text summarization. They consist of two components: an encoder that processes the input sequence and a decoder that generates the output sequence. For instance, a Seq2Seq model can be trained to translate English sentences into French sentences, capturing the context and generating accurate translations.
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