Lev Ratinov

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Lev Ratinov is an author.

Publications

Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Analysis and enhancement of wikification for microblogs with context expansion Disambiguation context
Disambiguation to wikipedia (D2W)
Twitter
24th International Conference on Computational Linguistics - Proceedings of COLING 2012: Technical Papers English 2012 Disambiguation to Wikipedia (D2W) is the task of linking mentions of concepts in text to their corresponding Wikipedia entries. Most previous work has focused on linking terms in formal texts (e.g. newswire) to Wikipedia. Linking terms in short informal texts (e.g. tweets) is difficult for systems and humans alike as they lack a rich disambiguation context. We first evaluate an existing Twitter dataset as well as the D2W task in general. We then test the effects of two tweet context expansion methods, based on tweet authorship and topic-based clustering, on a state-of-the-art D2W system and evaluate the results. 0 0
Learning-based multi-sieve co-reference resolution with knowledge EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, Proceedings of the Conference English 2012 We explore the interplay of knowledge and structure in co-reference resolution. To inject knowledge, we use a state-of-the-art system which cross-links (or "grounds") expressions in free text to Wikipedia. We explore ways of using the resulting grounding to boost the performance of a state-of-the-art co-reference resolution system. To maximize the utility of the injected knowledge, we deploy a learning-based multi-sieve approach and develop novel entity-based features. Our end system outperforms the state-of-the-art baseline by 2 B3 F1 points on non-transcript portion of the ACE 2004 dataset. 0 0
Local and global algorithms for disambiguation to Wikipedia HLT English 2011 0 0
Importance of semantic representation: Dataless classification Proceedings of the National Conference on Artificial Intelligence English 2008 Traditionally, text categorization has been studied as the problem of training of a classifier using labeled data. However, people can categorize documents into named categories without any explicit training because we know the meaning of category names. In this paper, we introduce Dataless Classification, a learning protocol that uses world knowledge to induce classifiers without the need for any labeled data. Like humans, a dataless classifier interprets a string of words as a set of semantic concepts. We propose a model for dataless classification and show that the label name alone is often sufficient to induce classifiers. Using Wikipedia as our source of world knowledge, we get 85.29% accuracy on tasks from the 20 Newsgroup dataset and 88.62% accuracy on tasks from a Yahoo! Answers dataset without any labeled or unlabeled data from the datasets. With unlabeled data, we can further improve the results and show quite competitive performance to a supervised learning algorithm that uses 100 labeled examples. Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 0 0
Text categorization with knowledge transfer from heterogeneous data sources Proceedings of the National Conference on Artificial Intelligence English 2008 Multi-category classification of short dialogues is a common task performed by humans. When assigning a question to an expert, a customer service operator tries to classify the customer query into one of N different classes for which experts are available. Similarly, questions on the web (for example questions at Yahoo Answers) can be automatically forwarded to a restricted group of people with a specific expertise. Typical questions are short and assume background world knowledge for correct classification. With exponentially increasing amount of knowledge available, with distinct properties (labeled vs unlabeled, structured vs unstructured), no single knowledge-transfer algorithm such as transfer learning, multi-task learning or self-taught learning can be applied universally. In this work we show that bag-of-words classifiers performs poorly on noisy short conversational text snippets. We present an algorithm for leveraging heterogeneous data sources and algorithms with significant improvements over any single algorithm, rivaling human performance. Using different algorithms for each knowledge source we use mutual information to aggressively prune features. With heterogeneous data sources including Wikipedia, Open Directory Project (ODP), and Yahoo Answers, we show 89.4% and 96.8% correct classification on Google Answers corpus and Switchboard corpus using only 200 features/class. This reflects a huge improvement over bag of words approaches and 48-65% error reduction over previously published state of art (Gabrilovich et. al. 2006). Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. 0 0