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

End-to-End Reinforcement Learning for Automatic Taxonomy Induction

We present a novel end-to-end reinforcement learning approach to automatic taxonomy induction from a set of terms. While prior methods treat the problem as a two-phase task (i.e., detecting hypernymy pairs followed by organizing these pairs into a tree-structured hierarchy), we argue that such two-phase methods may suffer from error propagation, and cannot effectively optimize metrics that capture the holistic structure of a taxonomy. In our approach, the representations of term pairs are learned using multiple sources of information and used to determine \textit{which} term to select and \textit{where} to place it on the taxonomy via a policy network. All components are trained in an end-to-end manner with cumulative rewards, measured by a holistic tree metric over the training taxonomies. Experiments on two public datasets of different domains show that our approach outperforms prior state-of-the-art taxonomy induction methods up to 19.6\% on ancestor F1.Comment: 11 Pages. ACL 2018 Camera Read

Toward new liquid crystal phases of DNA mesogens

Short, partially complementary, single-stranded (ss)DNA strands can form nanostructures with a wide variety of shapes and mechanical properties. It is well known that semiflexible, linear dsDNA can undergo an isotropic to nematic (IN) phase transition and that sufficiently bent structures can form a biaxial nematic phase. Here, we use numerical simulations to explore how the phase behavior of linear DNA constructs changes as we tune the mechanical properties of the constituent DNA by changing the nucleotide sequence. The IN-phase transition can be suppressed in so-called DNA “nunchakus”: structures consisting of two rigid dsDNA arms, separated by a sufficiently flexible spacer. In this paper, we use simulations to explore what phase behavior to expect for different linear DNA constructs. To this end, we first performed numerical simulations exploring the structural properties of a number of different DNA oligonucleotides using the oxDNA package. We then used the structural information generated in the oxDNA simulations to construct more coarse-grained models of the rod-like, bent-core, and nunchaku DNA. These coarse-grained models were used to explore the phase behavior of suspensions of the various DNA constructs. The approach explored in this paper makes it possible to “design” the phase behavior of DNA constructs by a suitable choice of the constituent nucleotide sequence

Research Status and Thinking of Minimal Clinically Important Difference in Patient-reported Outcome Assessment Tool for Allergic Rhinitis

As the patient-reported outcome (PRO) assessment tool has been increasingly used in the clinical efficacy evaluation of patients with allergic rhinitis (AR) , the judgment and interpretation of changes in measurement results of assessment tools have become increasingly important. For the judgment of clinical research results, not only the statistical differences, but also the minimum clinically important difference (MCID) must be paid attention to. This paper systematically sorts out the MCID of the AR-PRO assessment tool, in order to provide a reference for AR-related clinical diagnosis and treatment decision-making and the objectification of the PRO assessment tool