Dan Roth

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Dan Roth is an author.


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Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Exploiting the Wikipedia structure in local and global classification of taxonomic relations Natural Language Engineering English 2012 Determining whether two terms have an ancestor relation (e.g. Toyota Camry and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in Natural Language Processing applications such as Question Answering, Summarization, and Textual Entailment. Significant work has been done on developing knowledge sources that could support these tasks, but these resources usually suffer from low coverage, noise, and are inflexible when dealing with ambiguous and general terms that may not appear in any stationary resource, making their use as general purpose background knowledge resources difficult. In this paper, rather than building a hierarchical structure of concepts and relations, we describe an algorithmic approach that, given two terms, determines the taxonomic relation between them using a machine learning-based approach that makes use of existing resources. Moreover, we develop a global constraint-based inference process that leverages an existing knowledge base to enforce relational constraints among terms and thus improves the classifier predictions. Our experimental evaluation shows that our approach significantly outperforms other systems built upon the existing well-known knowledge sources. 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
Learning better transliterations Multi-lingual information retrieval
Probabilistic models
International Conference on Information and Knowledge Management, Proceedings English 2009 We introduce a new probabilistic model for transliteration that performs significantly better than previous approaches, is language-agnostic, requiring no knowledge of the source or target languages, and is capable of both generation (creating the most likely transliteration of a source word) and discovery (selecting the most likely transliteration from a list of candidate words). Our experimental results demonstrate improved accuracy over the existing state-of-the-art by more than 10% in Chinese, Hebrew and Russian. While past work has commonly made use of fixed-size n-gram features along with more traditional models such as HMM or Perceptron, we utilize an intuitive notion of "productions", where each source word can be segmented into a series of contiguous, non-overlapping substrings of any size, each of which independently transliterates to a substring in the target language with a given probability. (e.g. P(wash⇒ BaIII) = 0:95). To learn these parameters, we employ Expectation-Maximization (EM), with the alignment between substrings in the source and target word training pairs as our latent data. Despite the size of the parameter space and the 2 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
Which "Apple" are you talking about ? Clustering
Proceeding of the 17th International Conference on World Wide Web 2008, WWW'08 English 2008 In a higher level task such as clustering of web results or word sense disambiguation, knowledge of all possible distinct concepts in which an ambiguous word can be expressed would be advantageous, for instance in determining the number of clusters in case of clustering web search results. We propose an algorithm to generate such a ranked list of distinct concepts associated with an ambiguous word. Concepts which are popular in terms of usage are ranked higher. We evaluate the coverage of the concepts inferred from our algorithm on the results retrieved by querying the ambiguous word using a major search engine and show a coverage of 85% for top 30 documents averaged over all keywords. 0 0