N-Gram Model

Named entities form the major components in a document. When we catch the fundamental entities, we can understand a document to some degree. This paper employs different types of information …...

Ngram models are simple in language modeling and have been successfully used in speech recognition and other tasks. However, they can only capture the short distance context dependency within an …...

Statistical language models using n-gram approach have been under the criticism of neglecting large-span syntactic-semantic in- formation that influences the choice of the next word in a language. One of …...

In the paper, an effective technique, based on the non-uniform redistribution probability for novel events (the unknown events), to improve the smoothing method in language models is proposed. Basically, there …...

We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce …...

AbstractIn this paper, we propose an estimation method of user satisfaction for a spoken dialog system using an N-gram-based dialog history model. We have collected a large amount of spoken …...

We propose a novel way of incorporating de- pendency parse and word co-occurrence in- formation into a state-of-the-art web-scale n- gram model for spelling correction. The syn- tactic and distributional …...

Approximate search algorithms, such as cube pruning in syntactic machine translation, rely on the language model to estimate probabili- ties of sentence fragments. We contribute two changes that trade between …...

AbstractIn this paper, we present the first incremental parser for Tree Substitution Grammar (TSG). A TSG allows arbitrarily large syntactic fragments to be combined into complete trees; we show how …...

PTT (批踢踢) is one of the largest web forums in Taiwan. In the last few years, its importance has been growing rapidly because it has been widely mentioned by most …...

Despite the overwhelming use of statistical language models in speech recognition, machine translation, and several other domains, few high probability guarantees exist on their generaliza- tion error. In this paper, …...

Class-based language modeling (LM) is a long-studied and effective approach to overcome data sparsity in the context of n-gram model training. In statistical machine translation (SMT), differ- ent forms of …...

We present an approach to speech recogni- tion that uses only a neural network to map acoustic input to characters, a character-level language model, and a beam search decoding procedure. …...

AbstractWe present a probabilistic model of phonotactics, the set of well-formed phoneme sequences in a language. Unlike most computational models of phonotactics (Hayes and Wilson, 2008; Goldsmith and Riggle, 2012), …...

AbstractWith the advent of powerful neural language models over the last few years, research attention has increasingly focused on what aspects of language they represent that make them so successful. …...