Марковтық кездейсоқ өріс

Probabilistic models have been widely used for natural language processing. Part-of-speech tagging, which assigns the most likely tag to each word in a given sentence, is one. of tire problems …...

We relate the problem of finding the best application of a Synchronous Context- Free Grammar (SCFG) rule during pars- ing to a Markov Random Field. This representation allows us to …...

Identifying background (context) informa- tion in scientific articles can help schol- ars understand major contributions in their research area more easily. In this paper, we propose a general framework based …...

Many NLP tasks interact with syntax. The presence of a named entity span, for example, is often a clear indicator of a noun phrase in the parse tree, while a …...

Many NLP tasks make predictions that are in- herently coupled to syntactic relations, but for many languages the resources required to pro- vide such syntactic annotations are unavail- able. For …...

This paper presents a general, statistical framework for modeling phrase translation via Markov random fields. The model al- lows for arbituary features extracted from a phrase pair to be incorporated …...

Most existing systems for subcategoriza- tion frame (SCF) acquisition rely on su- pervised parsing and infer SCF distribu- tions at type, rather than instance level. These systems suffer from poor …...

In this study, we investigated unsupervised learning based Korean word sense disambiguation (WSD) using CoreNet, a Korean lexical semantic network. To facilitate the application of WSD to practical natural language …...

AbstractDeep-learning-based models have been successfully applied to the problem of detecting fake news on social media. While the correlations among news articles have been shown to be effective cues for …...