Wall Street Journal корпусы

Improved acoustic modeling can significantly decrease the error rate in large-vocabulary speech recognition. Our approach to the problem is twofold. We first propose a scheme that optimizes the degree of …...

This paperintroduces a simple mixtare language model that attempts to capture long distance conslraints in a sentence or paragraph. The model is an m-component mixture of Irigram models. The models …...

This paper reports on our experience hand tagging the senses of 25 of the most fre- quent verbs in 12,925 sentences of the Wall Street Journal Treebank corpus (Marcus et …...

In this paper) we propose a method for text categoriza- Lion task using term weight learning. In our approach, learning is to learn true keywords from the error of clus- …...

We propose a novel Co-Training method for statistical parsing. The algorithm takes as input a small corpus (9695 sentences) annotated with parse trees, a dictionary of possible lexicalized structures for …...

Memory-based learning (MBL) has enjoyed considerable success corpus-based natural language process-in ing (NLP) tasks and is thus a reliable method of getting a high-level of per- formance when building corpus-based …...

We present a comparative evaluation of two data-driven models used in translation selec- tion of English-Korean machine translation. La- tent semantic analysis(LSA) and probabilistic latent semantic analysis (PLSA) are applied …...

An unsupervised method for word sense disam- biguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each lan- …...

Two methods using comparable corpora to se- lect translation equivalents appropriate to a do- main were devised and evaluated. The first method ranks translation equivalents of a target word according …...

We combine lexical, syntactic, and discourse features to produce a highly predictive model of human readers’ judgments of text readabil- ity. This is the first study to take into ac- …...

This paper describes a new method for un- supervised grammar induction based on the automatic extraction of certain pat- terns in the texts. Our starting hypoth- esis is that there …...

AbstractThis paper investigates whether high-quality annotations for tasks involving semantic disambiguation can be obtained without a major investment in time or expense. We examine the use of untrained human volunteers …...

Recently, various unsupervised representation learning approaches have been investigated to produce augmenting features for natural language processing systems in the open-domain learning scenarios. In this paper, we propose a dynamic …...

We propose a language modeling (LM) approach incorporating interpolated distanced n-grams in a Dirichlet class language model (DCLM) (Chien and Chueh, 2011) for speech recognition. The DCLM relaxes the bag-of-words …...

AbstractCoherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not …...