Language Model Tutorials
This section lists some step-by-step tutorials for developing deep learning neural network language models.
1) How to Develop a Word-Level Neural Language Model and Use it to Generate Text.
2) How to Develop Word-Based Neural Language Models in Python with Keras.
3) How to Develop a Character-Based Neural Language Model in Keras
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Neural Language Models
Recently, the use of neural networks in the development of language models has become very popular, to the point that it may now be the preferred approach.
The use of neural networks in language modeling is often called Neural Language Modeling, or NLM for short.
Neural network approaches are achieving better results than classical methods both on .
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Overview
This post is divided into 3 parts; they are:.
1) Problem of Modeling Language 2.
Statistical Language Modeling.
3) Neural Language Models
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Problem of Modeling Language
Formal languages, like programming languages, can be fully specified.
All the reserved words can be defined and the valid ways that they can be used can be precisely defined.
We cannot do this with natural language.
Natural languages are not designed; they emerge, and therefore there is no formal specification.
There may be formal rules for parts o.
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What are NLP applications?
It refers to a technology that creates and implements ways of executing various tasks concerning natural language (such as:
- designing natural language based interfaces with databases
- machine translation
- etc
).
NLP applications belong to three main categories:1.
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What is statistical language modeling?
Statistical Language Modeling Statistical Language Modeling, or Language Modeling and LM for short, is the development of probabilistic models that are able to predict the next word in the sequence given the words that precede it.
Language modeling is the task of assigning a probability to sentences in a language.
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What is statistical NLP?
Statistical NLP has been the most widely used term to refer to nonsymbolic and nonlogical work on NLP over the past decade.
Statistical NLP comprises all quantitative approaches to automated language processing, including:
- probabilistic modeling
- information theory
- linear algebra (Manning and Schütze 1999 )
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What statistical methods are used in natural language processing?
Statistical methods based on Shannon’s Noisy-Channel model have become the method of choice within the speech community.
Many of the very same methods have been applied to problems in natural language processing by many of the very same researchers.
