Brown Clustering

In this paper, we propose a new method for semantic class induction. First, we in- troduce a generative model of sentences, based on dependency trees and which takes into account …...

AbstractUnsupervised word classes induced from unannotated text corpora are increasingly used to help tasks addressed by supervised classification, such as standard named entity detection. This paper studies the contribution of …...

A central task in clinical information ex- traction is the classification of sentences to identify key information in publications, such as intervention and outcomes. Sur- face tokens and part-of-speech tags …...

Brown clustering, an unsupervised hier- archical clustering technique based on n- gram mutual information, has proven use- ful in many NLP applications. However, most uses of Brown clustering employ the …...

This paper describes a classification system that participated in SemEval-2016 Task 4: Sentiment Analysis in Twitter. The proposed approach competed in subtasks A, B, and C, which involved tweet polarity …...

AbstractWe address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context. We focus on two training objectives that are amenable to stochastic gradient descent …...

AbstractIn this paper we present a morpho-syntactic tagger dedicated to Computer-mediated Communication texts in Polish. Its construction is based on an expanded RNN-based neural network adapted to the work on …...

AbstractWord clustering groups words that exhibit similar properties. One popular method for this is Brown clustering, which uses short-range distributional information to construct clusters. Specifically, this is a hard hierarchical …...

AbstractIn this study, we investigate the use of Brown clustering for offensive language detection. Brown clustering has been shown to be of little use when the task involves distinguishing word …...