ICTNET at Temporal Summarization Track TREC 2014

Abstract

In this paper, we describe our solutions of the Session Track at TREC 2014. Our main idea is to re-rank the documents the official supplies as RL1. In order to get good results of the re-ranked documents, we implement the learning to rank model which needs to extract some features. We use the relevance judgments of Session Track TREC 2013 as training set this year and also we use it as testing set by 5 -fold cross-validation. The rest of this paper is organized as follows. We detail our models in section 2. Section 3 describes our experiments, including our evaluation results. Conclusions are made in the last session.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2014
Accession Number
ADA618636

Entities

People

  • Dayong Wu
  • Hainan Zhang
  • Lei Chen
  • Qian Liu
  • Siying Li
  • Xueqi Cheng
  • Yue Liu
  • Zhiyuan Ji

Organizations

  • Chinese Academy of Sciences

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Abstracts
  • Applied Computer Science
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Automated Text Summarization
  • Computational Processes
  • Computer Science
  • Computing-Related Activities
  • Data Curation
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Information Operations
  • Machine Learning
  • Standards
  • Supervised Machine Learning
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Information Retrieval
  • Systems Analysis and Design