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CRFs are a class of statistical modeling methods often used in pattern recognition and machine learning for structured prediction. They are particularly useful in NLP for tasks like POS tagging and named entity recognition.

Support Vector Machines are supervised learning models that can be used for classification and regression challenges. In NLP, they are often used for text categorization tasks due to their effectiveness with high-dimensional data.

NMT is an approach to machine translation that utilizes deep neural networks, and in particular encoder-decoder architectures, to generate translations that are more fluent and accurate.

It assigns parts of speech to each word in a sentence, such as nouns, verbs, adjectives, etc. This helps in understanding grammatical structure and is essential for syntactic parsing.

n-gram models are a type of probabilistic model for predicting the next item in a sequence. In NLP, n-grams are contiguous sequences of n items from a given sample of text or speech. They are used in text mining and language modeling.

Decision trees are a method of machine learning where a model of decisions is made based on the data attributes. In NLP, they can be used for tasks like classification and feature extraction.

Topic modeling is a type of statistical model used to discover abstract topics within a collection of documents. Latent Dirichlet Allocation is the most common method used for topic modeling in NLP.

The Transformer is a deep learning model that uses self-attention mechanisms to weight the significance of different parts of the input data. It's very effective for NLP tasks like translation and text summarization.

RNNs are a class of neural networks where connections between nodes form a directed graph along a temporal sequence. This allows them to display temporal dynamic behavior, suitable for sequence prediction.

Sequence-to-sequence models use an encoder-decoder architecture designed to handle sequence-to-sequence tasks such as machine translation where the input and output are both sequences.

NER identifies and classifies named entities in text into predefined categories such as the names of persons, organizations, locations, etc. It's critical for information extraction and knowledge graph construction.

Stemming is an algorithm used to reduce words to their root form, often by chopping off affixes. It is commonly used in search engines and text analysis to generalize related words.

LSA is a technique to analyze relationships between a set of documents and the terms they contain. It uses singular value decomposition to reduce the number of rows while preserving the similarity structure among columns.

Tokenization is the process of breaking down text into units called tokens, which may be words, phrases, or symbols. It's a fundamental step in preprocessing for NLP tasks.

A simplistic representation of text where the text is represented as the bag of its words, disregarding grammar and word order but keeping multiplicity. Widely used in document classification and spam filtering.

Also known as opinion mining, it determines the emotional tone behind a body of text. This is widely used for brand monitoring, market research, and customer service.

Term Frequency-Inverse Document Frequency. It reflects how important a word is to a document in a collection or corpus. It's a statistical measure used for information retrieval and text mining.

Lemmatization involves reducing words to their lemma, or dictionary form. Unlike stemming, it takes into consideration the morphological analysis of words. Useful for text comprehension algorithms and search tools.

BERT is a Transformer-based model designed to understand the context of a word in search queries. By pre-training on a large corpus of text, BERT can be fine-tuned for a wide range of NLP tasks.