Word clusters are useful for many NLP tasks including training neural network language models, but current increases in datasets are outpacing the ability of word clusterers to handle them. Little attention has been paid thus far on inducing high-quality word clusters at a large scale. The predictive exchange algorithm is quite scalable, but sometimes does not provide as good perplexity as othe...

Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others. Most of the work in these areas formulate their problem in a graph-based setting and apply unsupervised graph clustering to obtain a set of clusters. Recent studies suggest that graphs often exhibit a hierarchi...

In this paper we define two parallel data sets based on pseudowords, extracted from the same corpus. They both consist of word-centered graphs for each of 1225 different pseudowords, and use respectively first-order co-occurrences and secondorder semantic similarities. We propose an evaluation framework on these data sets for graph-based Word Sense Induction (WSI) focused on the case of coarseg...

We address the problem of clustering words (or constructing a thesaurus) based on cooccurrence data, and conducting syntactic disambiguation by using the acquired word classes. We view the clustering problem as that of estimating a class-based probability distribution specifying the joint probabilities of word pairs. We propose an efficient algorithm based on the Minimum Description Length (MDL...

We presented our system for PAN 2016 Author Clustering task. Our software used simple character n-grams to represent the document collection. We then ran K-Means clustering optimized using the Silhouette Coefficient. Our system yields competitive results and required only a short runtime. Character n-grams can capture a wide range of information, making them effective for authorship attribution...

The main disadvantage of collocation-based word sense disambiguation is that the recall is low, with relatively high precision. How to improve the recall without decrease the precision? In this paper, we investigate a word-class approach to extend the collocation list which is constructed from the manually sense-tagged corpus. But the word classes are obtained from a larger scale corpus which i...

Sense Induction is the process of identifying the word sense given its context, often treated as a clustering task. This paper explores the use of spectral cluster method which incorporates word features and ngram features to determine which cluster the word belongs to, each cluster represents one sense in the given document set.

In this paper, we investigated various word clustering methods, by studying two clustering algorithms: Brown clustering and exchange algorithm, and three objective functions derived from different class-based language models (CBLM): two-sided, predictive and conditional models. In particular, we focused on the implementation of the exchange algorithm with improved speed. In total, we compared s...

This paper presents an unsupervised method for automatic Chinese word sense induction. The algorithm is based on clustering the similar words according to the contexts in which they occur. First, the target word which needs to be disambiguated is represented as the vector of its contexts. Then, reconstruct the matrix constituted by the vectors of target words through singular value decompositio...