Welcome to Answerclub.org
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
Sign Up to our social questions and Answers Engine to ask questions, answer people’s questions, and connect with other people.
Login to our social questions & Answers Engine to ask questions answer people’s questions & connect with other people.
Lost your password? Please enter your email address. You will receive a link and will create a new password via email.
Please briefly explain why you feel this question should be reported.
Please briefly explain why you feel this answer should be reported.
Please briefly explain why you feel this user should be reported.
Questions | Answers | Discussions | Knowledge sharing | Communities & more.
What is topic modeling in NLP?
Topic modeling is a statistical modeling technique used to identify topics or themes present in a collection of text documents, often based on probabilistic models such as Latent Dirichlet Allocation (LDA).
Topic modeling is a statistical modeling technique used to identify topics or themes present in a collection of text documents, often based on probabilistic models such as Latent Dirichlet Allocation (LDA).
See lessWhat is word embedding in NLP?
Word embedding is a technique used to represent words as dense, low-dimensional vectors in a continuous vector space, capturing semantic relationships and contextual information between words.
Word embedding is a technique used to represent words as dense, low-dimensional vectors in a continuous vector space, capturing semantic relationships and contextual information between words.
See lessWhat is attention mechanism in NLP?
Attention mechanism is a mechanism used in sequence-to-sequence models to selectively focus on relevant parts of the input sequence when generating each element of the output sequence, improving the model's performance.
Attention mechanism is a mechanism used in sequence-to-sequence models to selectively focus on relevant parts of the input sequence when generating each element of the output sequence, improving the model’s performance.
See lessWhat is sequence-to-sequence modeling in NLP?
Sequence-to-sequence modeling is an approach in NLP where both the input and output are variable-length sequences, commonly used for tasks such as machine translation, text summarization, and chatbot responses.
Sequence-to-sequence modeling is an approach in NLP where both the input and output are variable-length sequences, commonly used for tasks such as machine translation, text summarization, and chatbot responses.
See lessWhat is machine translation in NLP?
Machine translation is the task of automatically translating text from one language to another, using algorithms and statistical models trained on parallel corpora of translated sentences.
Machine translation is the task of automatically translating text from one language to another, using algorithms and statistical models trained on parallel corpora of translated sentences.
See lessWhat is text summarization in NLP?
Text summarization is the process of generating a concise and coherent summary of a longer text document while retaining the most important information and key points.
Text summarization is the process of generating a concise and coherent summary of a longer text document while retaining the most important information and key points.
See lessWhat is sentiment analysis in NLP?
Sentiment analysis is the process of analyzing text data to determine the sentiment or opinion expressed, such as positive, negative, or neutral, often used for tasks such as sentiment classification or sentiment polarity detection.
Sentiment analysis is the process of analyzing text data to determine the sentiment or opinion expressed, such as positive, negative, or neutral, often used for tasks such as sentiment classification or sentiment polarity detection.
See lessWhat is named entity recognition (NER) in NLP?
Named entity recognition is the process of identifying and classifying named entities (such as persons, organizations, locations, etc.) in text documents.
Named entity recognition is the process of identifying and classifying named entities (such as persons, organizations, locations, etc.) in text documents.
See lessWhat is part-of-speech tagging (POS tagging) in NLP?
Part-of-speech tagging is the process of assigning grammatical categories (such as noun, verb, adjective, etc.) to words in a sentence, based on their syntactic context.
Part-of-speech tagging is the process of assigning grammatical categories (such as noun, verb, adjective, etc.) to words in a sentence, based on their syntactic context.
See lessWhat is lemmatization in NLP?
Lemmatization is the process of reducing words to their base or dictionary form (lemma) by considering their meaning and context, often resulting in more accurate normalization compared to stemming.
Lemmatization is the process of reducing words to their base or dictionary form (lemma) by considering their meaning and context, often resulting in more accurate normalization compared to stemming.
See less