Search Results for author:
Found 9 papers, 1 papers with code
Improving Query Graph Generation for Complex Question Answering over Knowledge Base
Most of the existing Knowledge-based Question Answering (KBQA) methods first learn to map the given question to a query graph, and then convert the graph to an executable query to find the answer.
Supervised Learning in the Presence of Noise: Application in ICD-10 Code Classification
We first identify ICD-10 codes that human coders tend to misuse or confuse, based on the codes' locations in the ICD-10 hierarchy, the types of the codes, and baseline classifier's prediction behaviors; we then develop a novel training strategy that accounts for such noise.
From Extreme Multi-label to Multi-class: A Hierarchical Approach for Automated ICD-10 Coding Using Phrase-level Attention
With our proposed approach, interpretability is achieved not through implicitly learned attention scores but by attributing each prediction to a particular sentence and words selected by human coders.
An Ensemble Approach for Automatic Structuring of Radiology Reports
We present an ensemble method that consolidates the predictions of three models, capturing various attributes of textual information for automatic labeling of sentences with section labels.
Pure-Exploration for Infinite-Armed Bandits with General Arm Reservoirs
This paper considers a multi-armed bandit game where the number of arms is much larger than the maximum budget and is effectively infinite.
Adaptively Pruning Features for Boosted Decision Trees
Boosted decision trees enjoy popularity in a variety of applications; however, for large-scale datasets, the cost of training a decision tree in each round can be prohibitively expensive.
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
We consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir distribution.
Measuring Human-perceived Similarity in Heterogeneous Collections
We present a technique for estimating the similarity between objects such as movies or foods whose proper representation depends on human perception.
Regularizing Model Complexity and Label Structure for Multi-Label Text Classification
Multi-label text classifiers need to be carefully regularized to prevent the severe over-fitting in the high dimensional space, and also need to take into account label dependencies in order to make accurate predictions under uncertainty.
