A Statistical Model for Parsing and Word-Sense Disambiguation

رفع ابهام معنایی واژگان مبهم فارسی با مدل موضوعی LDA

Word sense disambiguation is the task of identifying the correct sense for the word in a given context among a finite set of possible sense. In this paper a model for farsi word sense disambiguation is presented. The model use two group of features: first, all word and stop words around target word and topic models as second features. We extract topics from a farsi corpus with Latent Dirichlet ...

A Statisti al Model for Parsing and Word-Sense Disambiguation

Probabilistic Coordination Disambiguation in a Fully-Lexicalized Japanese Parser

This paper describes a probabilistic model for coordination disambiguation integrated into syntactic and case structure analysis. Our model probabilistically assesses the parallelism of a candidate coordinate structure using syntactic/semantic similarities and cooccurrence statistics. We integrate these probabilities into the framework of fully-lexicalized parsing based on largescale case frame...

Context clustering for Word Sense Disambiguation based on modeling pairwise context similarities

Traditionally, word sense disambiguation (WSD) involves a different context model for each individual word. This paper presents a new approach to WSD using weakly supervised learning. Statistical models are not trained for the contexts of each individual word, but for the similarities between context pairs at category level. The insight is that the correlation regularity between the sense disti...

Integrate Syntactic Information into Chinese Word Sense Disambiguation Technology

Word sense disambiguation (WSD) is important to many application problems in natural language processing fields. It plays a very important role in information retrieval systems, machine translation systems, text classification systems and automatic summarization systems. Its task is to automatically choose the intended sense of an ambiguous word in a given context. In this paper, a new supervis...