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104
Large scale learning to rank
- In NIPS 2009 Workshop on Advances in Ranking
, 2009
"... Pairwise learning to rank methods such as RankSVM give good performance, but suffer from the computational burden of optimizing an objective defined over O(n 2) possible pairs for data sets with n examples. In this paper, we remove this super-linear dependence on training set size by sampling pairs ..."
Abstract
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Cited by 28 (2 self)
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Pairwise learning to rank methods such as RankSVM give good performance, but suffer from the computational burden of optimizing an objective defined over O(n 2) possible pairs for data sets with n examples. In this paper, we remove this super-linear dependence on training set size by sampling pairs
Extending the Entity-based Coherence Model with Multiple Ranks
"... We extend the original entity-based coherence model (Barzilay and Lapata, 2008) by learning from more fine-grained coherence preferences in training data. We associate multiple ranks with the set of permutations originating from the same source document, as opposed to the original pairwise rankings. ..."
Abstract
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Cited by 2 (2 self)
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We extend the original entity-based coherence model (Barzilay and Lapata, 2008) by learning from more fine-grained coherence preferences in training data. We associate multiple ranks with the set of permutations originating from the same source document, as opposed to the original pairwise rankings
Learning to Rank from Distant Supervision: Exploiting Noisy Redundancy for Relational Entity Search∗
"... Abstract—In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent info ..."
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Abstract—In this paper, we study the task of relational entity search which aims at automatically learning an entity ranking function for a desired relation. To rank entities, we exploit the redundancy abound in their snippets; however, such redundancy is noisy as not all the snippets represent
Agdistis - agnostic disambiguation of named entities using linked open data
- In Submitted to 12th International Semantic Web Conference
, 2013
"... Abstract. Over the last decades, several billion Web pages have been made available on the Web. The ongoing transi-tion from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description Framework ..."
Abstract
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Cited by 6 (5 self)
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Abstract. Over the last decades, several billion Web pages have been made available on the Web. The ongoing transi-tion from the current Web of unstructured data to the Data Web yet requires scalable and accurate approaches for the extraction of structured data in RDF (Resource Description
more like these”: growing entity classes from seeds
- In CIKM
, 2007
"... We present a corpus-based approach to the class expansion task. For a given set of seed entities we use co-occurrence statistics taken from a text collection to define a member-ship function that is used to rank candidate entities for in-clusion in the set. We describe an evaluation framework that u ..."
Abstract
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Cited by 11 (0 self)
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We present a corpus-based approach to the class expansion task. For a given set of seed entities we use co-occurrence statistics taken from a text collection to define a member-ship function that is used to rank candidate entities for in-clusion in the set. We describe an evaluation framework
AGDISTIS- Graph-Based Disambiguation of Named Entities using Linked Data
"... Abstract. Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Re-source Description Framew ..."
Abstract
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Cited by 8 (4 self)
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Abstract. Over the last decades, several billion Web pages have been made available on the Web. The ongoing transition from the current Web of unstructured data to the Web of Data yet requires scalable and accurate approaches for the extraction of structured data in RDF (Re-source Description
Privacy-Preserving Ranking over Vertically Partitioned Data
"... Privacy concerns in many application domains prevents sharing of data, which limits data mining technology to identify patterns and trends from large amount of data. Traditional data mining algorithms have been developed within a centralized model. However, distributed knowledge discovery has been p ..."
Abstract
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Cited by 1 (0 self)
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to be combined to produce the globally valid result. Learning how to rank existing entities is a central part in many knowledge discovery problems. In this paper, we present a method for ranking problem based on SVMRank algorithm in situations where different sites contain different attributes for a common set
Learning to Rank Adaptively for Scalable Information Extraction
"... Information extraction systems extract structured data from natural language text, to support richer querying and anal-ysis of the data than would be possible over the unstruc-tured text. Unfortunately, information extraction is a com-putationally expensive task, so exhaustively processing all docum ..."
Abstract
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Cited by 1 (1 self)
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Information extraction systems extract structured data from natural language text, to support richer querying and anal-ysis of the data than would be possible over the unstruc-tured text. Unfortunately, information extraction is a com-putationally expensive task, so exhaustively processing all
Did You Know? —Mining Interesting Trivia for Entities from Wikipedia
"... Trivia is any fact about an entity which is interest-ing due to its unusualness, uniqueness, unexpect-edness or weirdness. In this paper, we propose a novel approach for mining entity trivia from their Wikipedia pages. Given an entity, our system ex-tracts relevant sentences from its Wikipedia page ..."
Abstract
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and produces a list of sentences ranked based on their interestingness as trivia. At the heart of our system lies an interestingness ranker which learns the notion of interestingness, through a rich set of domain-independent linguistic and entity based fea-tures. Our ranking model is trained by leveraging
Relations Expansion: Extracting Relationship Instances from the Web
- IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE AND INTELLIGENT AGENT TECHNOLOGY
, 2010
"... In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context pat ..."
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In this paper, we propose a Relation Expansion framework, which uses a few seed sentences marked up with two entities to expand a set of sentences containing target relations. During the expansion process, label propagation algorithm is used to select the most confident entity pairs and context
Results 1 - 10
of
104
