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Fast Effective Rule Induction
- William W. Cohen
- Computer ScienceICML
- 9 July 1995
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
It is shown that HotpotQA is challenging for the latest QA systems, and the supporting facts enable models to improve performance and make explainable predictions.
Revisiting Semi-Supervised Learning with Graph Embeddings
On a large and diverse set of benchmark tasks, including text classification, distantly supervised entity extraction, and entity classification, the proposed semi-supervised learning framework shows improved performance over many of the existing models.
A Comparison of String Distance Metrics for Name-Matching Tasks
This work investigates a number of different metrics proposed by different communities, including edit-distance metrics, fast heuristic string comparators, token-based distance metrics, and hybrid methods, and finds the best-performing method is a hybrid scheme combining a TFIDF weighting scheme with the Jaro-Winkler string-distance scheme.
Semi-Markov Conditional Random Fields for Information Extraction
Intuitively, a semi-CRF on an input sequence x outputs a "segmentation" of x, in which labels are assigned to segments rather than to individual elements of xi, and transitions within a segment can be non-Markovian.
Random Walk Inference and Learning in A Large Scale Knowledge Base
It is shown that a soft inference procedure based on a combination of constrained, weighted, random walks through the knowledge base graph can be used to reliably infer new beliefs for theknowledge base.
Relational retrieval using a combination of path-constrained random walks
A novel learnable proximity measure is described which instead uses one weight per edge label sequence: proximity is defined by a weighted combination of simple “path experts”, each corresponding to following a particular sequence of labeled edges.
Gated-Attention Readers for Text Comprehension
- Bhuwan Dhingra, Hanxiao Liu, Zhilin Yang, William W. Cohen, R. Salakhutdinov
- Computer ScienceACL
- 5 June 2016
The Gated-Attention (GA) Reader, a model that integrates a multi-hop architecture with a novel attention mechanism, which is based on multiplicative interactions between the query embedding and the intermediate states of a recurrent neural network document reader, enables the reader to build query-specific representations of tokens in the document for accurate answer selection.
Learning to Order Things
An on-line algorithm for learning preference functions that is based on Freund and Schapire's "Hedge" algorithm is considered, and it is shown that the problem of finding the ordering that agrees best with a learned preference function is NP-complete.
Differentiable Learning of Logical Rules for Knowledge Base Reasoning
A framework, Neural Logic Programming, is proposed that combines the parameter and structure learning of first-order logical rules in an end-to-end differentiable model and outperforms prior work on multiple knowledge base benchmark datasets, including Freebase and WikiMovies.