Probabilistic Models of Short and Long Distance Word Dependencies in Running Text

This article describes two complementary models that represent dependencies between words in loca/ and non-local contexts. The type of local dependencies considered are sequences of part of speech categories for words. The non-local context of word dependency considered here is that of word recurrence, which is typical in a text. Both are models of phenomena that are to a reasonable extent domain independent, and thus are useful for doing prediction in systems using large vocabularies. Modeling Part of Speech Sequences A common method for modeling local word dependencies is by means of second order Markov models (also known as trigram models). In such a model the context for predicting word wi at position i in a text consists of the two words wi_l, wi-2 that precede it. The model is built from conditional probabilities: P(wi I wi_l, wi-2). The parameters of a part of speech (POS) model are of the form: P(wi [ Ci) x P(Ci [ Ci-1, Ci-2)

Character-Aware Neural Language Models

We describe a simple neural language model that relies only on character-level inputs. Predictions are still made at the word-level. Our model employs a convolutional neural network (CNN) and a highway network over characters, whose output is given to a long short-term memory (LSTM) recurrent neural network language model (RNN-LM). On the English Penn Treebank the model is on par with the existing state-of-the-art despite having 60% fewer parameters. On languages with rich morphology (Arabic, Czech, French, German, Spanish, Russian), the model outperforms word-level/morpheme-level LSTM baselines, again with fewer parameters. The results suggest that on many languages, character inputs are sufficient for language modeling. Analysis of word representations obtained from the character composition part of the model reveals that the model is able to encode, from characters only, both semantic and orthographic information.Comment: AAAI 201