65 research outputs found
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Finding Semantic Associations on Express Lane
This paper introduces a new codification scheme for efficient computation of measures in semantic networks. The scheme is particularly useful for fast computation of semantic associations between words and implementation of an informational retrieval operator for efficient search in semantic spaces. Other applications may also be possible
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary
CoSyne: A Framework for Multilingual Content Synchronization of Wikis
Wikis allow a large base of contributors easy access to shared content, and freedom in editing it. One of the side-effects of this freedom was the emergence of parallel and independently evolving versions in a variety of languages, reflecting the multilingual background of the pool of contributors. For the Wiki to properly represent the user-added content, this should be fully available in all its languages. Working on parallel Wikis in several European languages, we investigate the possibility to “synchronize” different language versions of the same document, by: i) pinpointing topically related pieces of information in the different languages, ii) identifying information that is missing or less detailed in one of the two versions,iii) translating this in the appropriate language, iv) inserting it in the appropriate place. Progress along such directions will allow users to share more easily content across language boundaries
multi level alignments as an extensible representation basis for textual entailment algorithms
A major problem in research on Textual Entailment (TE) is the high implementation effort for TE systems. Recently, interoperable standards for annotation and preprocessing have been proposed. In contrast, the algorithmic level remains unstandardized, which makes component re-use in this area very difficult in practice. In this paper, we introduce multi-level alignments as a central, powerful representation for TE algorithms that encourages modular, reusable, multilingual algorithm development. We demonstrate that a pilot open-source implementation of multi-level alignment with minimal features competes with state-of-theart open-source TE engines in three languages
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