[PDF] Top 20 Entity attribute ranking using learning to rank
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Entity attribute ranking using learning to rank
... assessing attribute ranking algorithms is extremely difficult in the absence of a standard ...human ranking annotations for entity ...that entity and that this ranking should be ... See full document
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Scaling Learning to Rank to Big Data: Using MapReduce to parallelise Learning to Rank
... of Learning to Rank methods has on the speed-up that can be achieved by parallelising the methods with the MapRe- duce model is still to be ...a Learning to Rank algorithm basically turns it ... See full document
125
Learning to Rank using Query-Level Rules
... existing learning to rank methods neglect query-sensitive information while producing functions to estimate the relevance of documents ...improving ranking performance. We present novel ... See full document
15
Learning to Rank Relevant Files for Bug Reports Using Domain knowledge, Replication and Extension of a Learning-to-Rank Approach
... before using them in a learning to rank ...the learning-to-rank ...Since learning to rank is a supervised machine learning method, the data needs to be di- vided ... See full document
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Learning to rank diversified results for biomedical information retrieval from multiple features
... general learning-to-rank ...for learning-to-rank, we compare the extracted passages with the official defined passages with golden standard of relevance and assume whenever there is an ... See full document
10
An evolutionary strategy with machine learning for learning to rank in information retrieval
... machine learning is a competitive approach to tackle the LTR problem in information ...machine learning (linear regression and support vector machines) strikes a good balance between effectiveness and ... See full document
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Attribute ranking based lazy learning associative classification
... this, ranking mechanism of attribute selection method is also proposed in this ...proposed ranking mechanism are compared with different data sets and Time taken to predict single instance for ... See full document
8
Cost-Sensitive Learning to Rank
... could rank instances from highest predicted risk to lowest predicted ...applying Learning to Rank over Regression can require IR-only assumptions ...not rank well: consider the performance of ... See full document
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Answering questions by learning to rank Learning to rank by answering questions
... documents. Using a token-based retrieval (Lucene, ElasticSearch) and keeping the most relevant document as dictated by a lexical score ...semantically ranking the first N ... See full document
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Learning Image Re-Rank: Query-Dependent Image Re-Ranking Using Semantic Signature
... of using high-level semantic concepts which is also called attributes, and to represent human actions from videos and argue that attributes enable the construction of more descriptive models for human action ... See full document
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Soil Data Classification Using Attribute Group Rank With Filter Based Instance Selection Model
... machine learning algorithms, research on agricultural components such as soil, crops, rainfall and price prediction have gained massive attraction from research ...machine learning techniques have become ... See full document
7
How Many Pairwise Preferences Do We Need to Rank a Graph Consistently?
... to ranking on ...matrix rank. Nevertheless, we compare with Rank Centrality (works under BTL model) and Inductive Pairwise Ranking (requires item features), but as expected both perform ... See full document
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Collaborative Ranking: A Case Study on Entity Linking
... a learning algorithm that can work best on all types of ...by learning al- ...the ranking system, it would be more desirable to form a larger- scale “collaborative” group for the query and make a ... See full document
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Evaluate with Confidence Estimation: Machine ranking of translation outputs using grammatical features
... machine learning techniques to model the human annotators’ ...Machine Learning methods over previously released evalua- tion data have been already used for tuning com- plex statistical evaluation metrics ... See full document
6
Extended Named Entity Ontology with Attribute Information
... Named Entity (ENE), which has about 200 categories (Sekine and Nobata ...knowledge using a Japanese encyclopedia, which contains abundant descriptions of ENE ... See full document
6
Mining Opinion Features in Customer Reviews.
... the ranking scores is ...two ranking algorith ms: a consumer- oriented ranking base that ranks reviews according to their expected helpfulness and a manufacturer-oriented base that ranks reviews ... See full document
5
Graph Ranking for Collective Named Entity Disambiguation
... graph ranking, where all nodes are ranked according to the link ...graph rank with the initial confidence. The highest rank is not always correct, so in the third step a selection al- gorithm is used ... See full document
6
Is Urban Economic Growth Inclusive in India?
... inclusion. Using the broad-based growth process in terms of mean-based averages of income and absolute-norm based measures of deprivation, the tentative estimates indicate that the growth process between 1993-94 ... See full document
27
Structural, Transitive and Latent Models for Biographic Fact Extraction
... This latent model can be further extended us- ing the multilingual nature of Wikipedia. We take the corresponding German pages of the train- ing names and model the German word distribu- tions characterizing each seed ... See full document
9
Unsupervised Semantic Abstractive Summarization
... The cases that we examined in Table 1 are cases where the words don’t have a perfect identity but rather a near identity. This points out that co- reference resolution is not a simple yes/no ques- tion but rather a ... See full document
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