In a number of natural language processing tasks, we face the problem of recovering a string of English words after it has been garbled by passage through a noisy channel. To tackle this problem successfully, we must be able to estimatethe probability with which any particular string of English...
The Brown clustering algorithm (Brown et al., 1992) is widely used in natural language processing (NLP) to derive lexical representations that are then used to improve performance on various NLP problems. The algorithm assumes an underlying model that is essentially an HMM, with the restriction ...
REFERENCES [1] CS224n: Natural Language Processing with Deep Learning.http://web.stanford.edu/class/cs224n/index.html#coursework. [2] Stanford CS224n追剧计划-Week4.https://
Inspired by n-gram models in natural language processing,\nVNN divides the view sequence into a set of visual n-grams, which involve\noverlapping ... S Bai,T Huang,X Bai,... 被引量: 0发表: 2019年 CONVERTING NEURAL NETWORK LANGUAGE MODELS INTO BACK-OFF LANGUAGE MODELS FOR EFFICIENT DECOD...
Statistical n-gram language modeling is a very important technique in Natural Language Processing (NLP) and Computational Linguistics used to assess the fluency of an utterance in any given language. It is widely employed in several important NLP applications such as Machine Translation and Automatic ...
Recently, tense has drawn attention in many natural language processing applications. However, most of current Statistical Machine Translation (SMT) systems mainly depend on translation model and language model. They never consider and...
Key Points In automatic speech recognition, n -grams are important to model some of the structural usage of natural language, i.e., the model uses word dependencies to assign a higher probability to "how are you today" than to "are how today you," although both phrases contain the exact...
Deep Neural Network Language Models Arisoy et al. A DNN acoustic model. Used in many natural language technologies. Represents a probability distribution over all possible word strings in a language. contribute Visual Question Answering & Dialog This subset of natural language processing models uses...
This model answers questions based on the context of the given input paragraph. GPT-2 Radford et al. A large transformer-based language model that given a sequence of words within some text, predicts the next word. Machine Translation This class of natural language processing models learns how...
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