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Syntactic Parse Trees for sentence representation

Table of Contents 1. Syntactic Parse Trees for sentence representation   syntax parse tree 1.1. goal 1.2. motivation 1.3. ingredients 1.4. steps 1.5. outlook 1.6. resources Syntactic Parse Trees for sentence representation :syntax:parse:tree: goal Today’s summary deals with the question of modeling additional information present in natural language and how models could take advantage of this hierarchical syntactic structure. motivation Besides semantics, being about meaning and logic in language, syntax determines the structure of sentences. Basic linguistic information in the form of parse trees, characterizing relationships among words in a sentence, might in principle lead to better word and sentence representations which should enhance natural language processing. ingredients parse tree, constituency, dependency, syntax, semantics, recursive neural network, recurrent neural network, reduce shift algorithm, shift […]

Word Embedding

Thoughts about character-based word embedding and vocabularies in NLP :character:word:embedding:vocabulary: Goal In this summary we compare the two standard methods of single character embedding and full word embedding. Motivation In order to teach a computer to understand words in order to perform natural language tasks, we have to map characters or words to a vector space the computer naturally acts on. Ingredients vocabulary, convolutional layers, highway layers, vector space, out of vocabulary words, semantics, syntax Steps The mapping of character, words or even complete sentences into a vector space is usually called embedding. Given some text, there are two distinct methods to compute word embeddings manageable by a computer. Children learning to read to start by recognizing individual characters before […]