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The PASCAL Recognising Textual Entailment Challenge
This paper presents the Third PASCAL Recognising Textual Entailment Challenge (RTE-3), providing an overview of the dataset creating methodology and the submitted systems. In creating this year'sExpand
Improving Distributional Similarity with Lessons Learned from Word Embeddings
It is revealed that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations, rather than the embedding algorithms themselves, and these modifications can be transferred to traditional distributional models, yielding similar gains. Expand
The Sixth PASCAL Recognizing Textual Entailment Challenge
This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which TextualEntailment is performed on a real corpus in the Update Summarization scenario. Expand
Improving Hypernymy Detection with an Integrated Path-based and Distributional Method
An improved path-based algorithm is suggested, in which the dependency paths are encoded using a recurrent neural network, that achieves results comparable to distributional methods. Expand
context2vec: Learning Generic Context Embedding with Bidirectional LSTM
This work presents a neural model for efficiently learning a generic context embedding function from large corpora, using bidirectional LSTM, and suggests they could be useful in a wide variety of NLP tasks. Expand
The Second PASCAL Recognising Textual Entailment Challenge
This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2). 1 We describe the RTE2 dataset and overview the submissions for the challenge. One of the main goals for thisExpand
Directional distributional similarity for lexical inference
This paper investigates the nature of directional (asymmetric) similarity measures that aim to quantify distributional feature inclusion, identifies desired properties of such measures for lexical inference, specifies a particular measure based on Average Precision that addresses these properties, and demonstrates the empirical benefit of directional measures for two different NLP datasets. Expand
Supervised Open Information Extraction
A novel formulation of Open IE as a sequence tagging problem, addressing challenges such as encoding multiple extractions for a predicate, and a supervised model that outperforms the existing state-of-the-art Open IE systems on benchmark datasets. Expand
Do Supervised Distributional Methods Really Learn Lexical Inference Relations?
This work investigates a collection of distributional representations of words used in supervised settings for recognizing lexical inference relations between word pairs, and shows that they do not actually learn a relation between two words, but an independent property of a single word in the pair. Expand