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Fast Effective Rule Induction
TLDR
This paper evaluates the recently-proposed rule learning algorithm IREP on a large and diverse collection of benchmark problems, and proposes a number of modifications resulting in an algorithm RIPPERk that is very competitive with C4.5 and C 4.5rules with respect to error rates, but much more efficient on large samples. Expand
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
TLDR
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. Expand
Revisiting Semi-Supervised Learning with Graph Embeddings
TLDR
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. Expand
A Comparison of String Distance Metrics for Name-Matching Tasks
TLDR
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. Expand
Semi-Markov Conditional Random Fields for Information Extraction
TLDR
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. Expand
Random Walk Inference and Learning in A Large Scale Knowledge Base
TLDR
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. Expand
Gated-Attention Readers for Text Comprehension
TLDR
The model, the Gated-Attention (GA) Reader, 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, which enables the reader to build query-specific representations of tokens in the document for accurate answer selection. Expand
Relational retrieval using a combination of path-constrained random walks
TLDR
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. Expand
Learning to Order Things
TLDR
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. Expand
Learning Trees and Rules with Set-Valued Features
TLDR
It is argued that many decision tree and rule learning algorithms can be easily extended to set-valued features, and it is shown by example that many real-world learning problems can be efficiently and naturally represented with set- valued features. Expand
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