Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement

Constrained Inverse Adaptive Cluster Sampling

In adaptive cluster sampling, first proposed by Thompson (1990, 1992) and studied in detail by Thompson and Seber (1996), an initial sample of units is selected and, whenever the value of the variable of interest satisfies a specified condition, neighbouring units are added to the sample. The neighbourhood of a unit may be defined by spatial proximity or, in the case of human population, by soc...

HULTECH at the NTCIR-10 INTENT-2 Task: Discovering User Intents through Search Results Clustering

In this paper, we describe our participation in the Subtopic Mining subtasks of the NTCIR-10 Intent-2 task, for the English language. For this subtask, we experiment a state-ofthe-art algorithm for search results clustering, the HISGKmeans algorithm and define the users’ intents based on the cluster labels following a general framework. From the Web snippets returned for a given query, our fram...

Adaptive Refinement and Clustering of H-Matrices

Discovering syntactic deep structure via Bayesian statistics

In the Bayesian framework, a language learner should seek a grammar that explains observed data well and is also a priori probable. This paper proposes such a measure of prior probability. Indeed it develops a full statistical framework for lexicalized syntax. The learner’s job is to discover the system of probabilistic transformations (often called lexical redundancy rules) that underlies the ...

Correlation Clustering with Constrained Cluster Sizes and Extended Weights Bounds

We consider the problem of correlation clustering on graphs with constraints on both the cluster sizes and the positive and negative weights of edges. Our contributions are twofold: First, we introduce the problem of correlation clustering with bounded cluster sizes. Second, we extend the region of weight values for which the clustering may be performed with constant approximation guarantees in...