A word n-gram language model is a purely statistical model of language. It has been superseded by recurrent neural network–based models, which have been...

words, n-grams may also be called shingles. In the context of natural language processing (NLP), the use of n-grams allows bag-of-words models to capture...

neural network-based models, which had previously superseded the purely statistical models, such as word n-gram language model. Noam Chomsky did pioneering...

models pioneered word alignment techniques for machine translation, laying the groundwork for corpus-based language modeling. A smoothed n-gram model...

Standard (non-cache) N-gram language models will assign a very low probability to the word "elephant" because it is a very rare word in English. If the...

observed language, word embeddings or semantic feature space models have been used as a knowledge representation for some time. Such models aim to quantify...

continuous skip-gram architecture, the model uses the current word to predict the surrounding window of context words. The skip-gram architecture weighs...

factored language model (FLM) is an extension of a conventional language model introduced by Jeff Bilmes and Katrin Kirchoff in 2003. In an FLM, each word is...

back-off is a generative n-gram language model that estimates the conditional probability of a word given its history in the n-gram. It accomplishes this...

algorithm used has a low enough time complexity to be practical. 2003: word n-gram model, at the time the best statistical algorithm, is outperformed by a...

the context of language modeling. The intuition behind the method is that a class-based language model (also called cluster n-gram model), i.e. one where...

Oren Melamud, Omer Levy, and Ido Dagan uses the skip-gram model to find a vector for each word and its synonyms. Then, it calculates the cosine distance...

generative models that contribute to the ongoing AI boom. In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings...

language models. The addition of the term for lower order n-grams adds more weight to the overall probability when the count for the higher order n-grams...

Discourse representation Lexical analysis: Word and text tokenizer n-gram and collocations Part-of-speech tagger Tree model and Text chunker for capturing Named-entity...

a dependency grammar). Bigrams, along with other n-grams, are used in most successful language models for speech recognition. Bigram frequency attacks...

any integer n ≥ 1 {\displaystyle n\geq 1} , we define the set of its n-grams to be G n ( y ) = { y 1 ⋯ y n , y 2 ⋯ y n + 1 , ⋯ , y K − n + 1 ⋯ y K } {\displaystyle...

LEPOR (Length Penalty, Precision, n-gram Position difference Penalty and Recall) is an automatic language independent machine translation evaluation metric...

settings, such as in-context learning, masked language tasks, stripped down transformers, bigram statistics, N-gram statistics, pairwise convolutions, and arithmetic...

Interjections are another word class, but these are not described here as they do not form part of the clause and sentence structure of the language. Linguists generally...

sentence. Language models are typically approximated by smoothed n-gram models, and similar approaches have been applied to translation models, but this...

together with other researchers, Holger Schwenk replaced the usual n-gram language model with a neural one and estimated phrase translation probabilities...

grammar (TAG) – Natural language – n-gram – sequence of n number of tokens, where a "token" is a character, syllable, or word. The n is replaced by a number...

available. ROUGE-N: Overlap of n-grams between the system and reference summaries. ROUGE-1 refers to the overlap of unigrams (each word) between the system...

ones (sentence-length penalty and n-gram based word order penalty). The experiments were tested on eight language pairs from ACL-WMT2011 including English-to-other...

outperforms other algorithms, including WordNet semantic similarity measures and skip-gram Neural Network Language Model (Word2vec). ESA is used in commercial...

the n-gram language model. 1987 – The back-off model allowed language models to use multiple length n-grams, and CSELT used HMM to recognize languages (both...

input word to produce the normalized (root) form. Some stemming techniques use the n-gram context of a word to choose the correct stem for a word. Hybrid...

English Fiction. The program can search for a word or a phrase, including misspellings or gibberish. The n-grams are matched with the text within the selected...

that corresponds to the number of atoms in 12 grams of 12C, which made the molar mass of a compound in grams per mole, numerically equal to the average molecular...