Deep Contextualized Word Representations - SNLP2018
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1) The document introduces ELMo (Embeddings from Language Models), a new type of deep contextualized word representation that models both the complex characteristics of word use and how these uses vary across linguistic contexts. 2) ELMo representations are learned as functions of the internal states of a pre-trained deep bidirectional language model, which is pre-trained with an unsupervised language modeling objective on a large text corpus. 3) Unlike previous approaches, ELMo representations are deep in that they are a function of all the internal layers of the bidirectional language model.
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sequence on which the model will be tested is likely to be different from all the word sequences seen
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for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern
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Processing (NLP) is to resolve the sense ambiguity of
characters or words on text, such as polyphones, homonymy,
and homograph. The paper addresses the ambiguity issue of
Chinese character polyphones and disambiguity approach for
such issues. Three methods, dictionary matching, language
models and voting scheme, are used to disambiguate the
prediction of polyphones. Compared with the well-known MS
Word 2007 and language models (LMs), our approach is
superior to these two methods for the issue. The final precision
rate is enhanced up to 92.75%. Based on the proposed
approaches, we have constructed the e-learning system in
which several related functions of Chinese transliteration are
integrated.
OPTIMIZE THE LEARNING RATE OF NEURAL ARCHITECTURE IN MYANMAR STEMMER
Morphological stemming becomes a critical step toward natural language processing. The process of
stemming is to reduce alternative forms to a common morphological root. Word segmentation for
Myanmar Language, like for most Asian Languages, is an important task and extensively-studied
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OPTIMIZE THE LEARNING RATE OF NEURAL ARCHITECTURE IN MYANMAR STEMMER
The document describes a deep BiGRU-CNN-CRF neural network model that jointly performs word segmentation, stemming, and named entity recognition for the Myanmar language. The model uses character-level and syllable-level representations as input. It was trained on a manually annotated Myanmar corpus. Evaluation results showed the neural sequence labeling architecture achieved state-of-the-art performance for the joint tasks of word segmentation, stemming, and named entity recognition in Myanmar text.
nlp (1).pptx
Natural Language Processing (NLP) involves developing computational techniques to analyze and understand human languages. Key techniques in NLP include sentiment analysis to classify emotions in text, text classification to categorize text, and tokenization to break text into discrete units like words. NLP is used to teach machines how to read and understand human language by identifying relationships between words and other linguistic elements.
New word analogy corpus
The document introduces a new word analogy corpus for the Czech language to evaluate word embedding models. It contains over 22,000 semantic and syntactic analogy questions across various categories. The authors experiment with Word2Vec (CBOW and Skip-gram) and GloVe models on the Czech Wikipedia corpus. Their results show that CBOW generally performs best, with accuracy improving as vector dimension and training epochs increase. Performance is better on syntactic tasks than semantic ones. The new Czech analogy corpus allows further exploration of how word embeddings represent semantics and syntax for highly inflected languages.
[Emnlp] what is glo ve part ii - towards data science
GloVe is a new model for learning word embeddings from co-occurrence matrices that combines elements of global matrix factorization and local context window methods. It trains on the nonzero elements in a word-word co-occurrence matrix rather than the entire sparse matrix or individual context windows. This allows it to efficiently leverage statistical information from the corpus. The model produces a vector space with meaningful structure, as shown by its performance of 75% on a word analogy task. It outperforms related models on similarity tasks and named entity recognition. The full paper describes GloVe's global log-bilinear regression model and how it addresses drawbacks of previous models to encode linear directions of meaning in the vector space.
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The document discusses different branches and concepts of semantics according to various scholars. It covers the overlap between semantics and pragmatics in determining sentence meaning versus speaker meaning. It also discusses levels of meaning including reference, denotation, sense, functional and content meaning. Contextual meaning is influenced by the context. Metaphor and metonymy are discussed as ways of extending meaning through resemblance or contiguity between concepts.
Nlp (1)
Natural Language Processing (NLP) involves using computational techniques to analyze and understand human languages. Key techniques in NLP include sentiment analysis to classify emotions in text, text classification to categorize text into predefined tags or categories, and tokenization which breaks text into discrete words and punctuation. NLP is used to teach machines how to read and understand human languages by identifying relationships between words and entities. Other areas of NLP include parts of speech tagging, constituent structure analysis, and analysis of pronunciation, morphology, syntax, semantics, and pragmatics.
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