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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 […]


In this summary I like to provide a rough overview of Sequence-To-Sequence neural network architectures and what purposes these serve. Motivation A key observation when dealing with neural networks is that these can only handle objects of a fixed size. This means that the architecture has to be adopted if sequences like sentences should be process-able. The same problems with objects of variable length also appear on the dialog level, where a certain number of utterances and responses string together. Besides dialog modeling, speech recognition and machine translation demand for advanced neural networks. Ingredients Deep neural network, hidden layer, recurrent neural network, encoder, decoder, LSTM, back-propagation, word embedding, sentence embedding Steps As already stated standard neural networks can not deal with […]