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Recent trends suggest that neural-network-inspired word embedding models outperform traditional count-based distri-butional models on word similarity and analogy detection tasks. We reveal that much of the performance gains of word embeddings are due to certain system design choices and hyperparameter optimizations , rather than the embedding algorithms(More)
This paper describes the Second PASCAL Recognising Textual Entailment Challenge (RTE-2). 1 We describe the RTE-2 dataset and overview the submissions for the challenge. One of the main goals for this year's dataset was to provide more " realistic " text-hypothesis examples , based mostly on outputs of actual systems. The 23 submissions for the challenge(More)
This paper presents a new approach for resolving lexical ambiguities in one language using statistical data from a monolingual corpus of another language. This approach exploits the differences between mappings of words to senses in different languages. The paper concentrates on the problem of target word selection in machine translation, for which the(More)
In many applications of natural language processing (NLP) it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine which of the two word combinations " eat a peach " and " eat a beach " is more likely. Statistical NLP methods determine the likelihood of a word combination from its(More)
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's dataset, a number of longer texts were introduced to make the challenge more oriented to realistic scenarios. Additionally , a pool of resources was offered so(More)
Distributional word similarity is most commonly perceived as a symmetric relation. Yet, directional relations are abundant in lexical semantics and in many Natural Language Processing (NLP) settings that require lexical inference, making symmetric similarity measures less suitable for their identification. This paper investigates the nature of directional(More)
Recognizing shallow linguistic patterns, such as basic syntactic relationships between words, is a common task in applied natural language and text processing. The common practice for approaching this task is by tedious manual definition of possible pattern structures, often in the form of regular expressions or finite automata. This paper presents a novel(More)
Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and en-tailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent(More)