22 research outputs found
Mining Entity Synonyms with Efficient Neural Set Generation
Mining entity synonym sets (i.e., sets of terms referring to the same entity)
is an important task for many entity-leveraging applications. Previous work
either rank terms based on their similarity to a given query term, or treats
the problem as a two-phase task (i.e., detecting synonymy pairs, followed by
organizing these pairs into synonym sets). However, these approaches fail to
model the holistic semantics of a set and suffer from the error propagation
issue. Here we propose a new framework, named SynSetMine, that efficiently
generates entity synonym sets from a given vocabulary, using example sets from
external knowledge bases as distant supervision. SynSetMine consists of two
novel modules: (1) a set-instance classifier that jointly learns how to
represent a permutation invariant synonym set and whether to include a new
instance (i.e., a term) into the set, and (2) a set generation algorithm that
enumerates the vocabulary only once and applies the learned set-instance
classifier to detect all entity synonym sets in it. Experiments on three real
datasets from different domains demonstrate both effectiveness and efficiency
of SynSetMine for mining entity synonym sets.Comment: AAAI 2019 camera-ready versio
Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement
Identifying new user intents is an essential task in the dialogue system.
However, it is hard to get satisfying clustering results since the definition
of intents is strongly guided by prior knowledge. Existing methods incorporate
prior knowledge by intensive feature engineering, which not only leads to
overfitting but also makes it sensitive to the number of clusters. In this
paper, we propose constrained deep adaptive clustering with cluster refinement
(CDAC+), an end-to-end clustering method that can naturally incorporate
pairwise constraints as prior knowledge to guide the clustering process.
Moreover, we refine the clusters by forcing the model to learn from the high
confidence assignments. After eliminating low confidence assignments, our
approach is surprisingly insensitive to the number of clusters. Experimental
results on the three benchmark datasets show that our method can yield
significant improvements over strong baselines.Comment: Accepted by AAAI202