In this paper we present the TakeLab-QA entry to SemEval 2017 task 3, which is a question-comment re-ranking problem. We present a classification based approach, including two supervised learning models – Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We use features based on different semantic similarity models (e.g., Latent Dirichlet Allocation), as well as features ba...

The LETOR website contains three information retrieval datasets used as a benchmark for testing machine learning ideas for ranking. Algorithms participating in the challenge are required to assign score values to search results for a collection of queries, and are measured using standard IR ranking measures (NDCG, precision, MAP) that depend only the relative score-induced order of the results....

* This value represents an average weighted score of the environmental issues associated to the concern. For further details see Detailed scoring tables (Annex II). ** This value represents the overall score including environmental, socio-economic and likely future impacts. For further details see Detailed scoring tables (Annex II). *** Priority refers to the ranking of GIWA concerns. Increased...

This paper describes a novel approach of improving multi-document summarization based on cross-document information extraction (IE). We describe a method to automatically incorporate IE results into sentence ranking. Experiments have shown our integration methods can significantly improve a high-performing multi-document summarization system, according to the ROUGE-2 and ROUGE-SU4 metrics (7.38...

This paper describes our participation in the 2008 TREC Blog track. Our system consists of 3 components: data preprocessing, topic retrieval, and opinion finding. In the topic retrieval task, we applied Lemur IR toolkit and used various techniques for query expansion. In the opinion finding and polarization task, we employed a feature-based classification approach. Then re-ranking was performed...

In this paper, we present a way of translating Korean words to Chinese using Chinese character information. A mapping table of Korean and Chinese characters is constructed and used to obtain possible combinations as translation candidates. The candidates are ranked by the combination score which accounts for the possibility of the character combination and context similarity score, which indica...

Using Lexical and Compositional Semantics to Improve HPSG Parse Selection Chair of the Supervisory Committee: Dr. Emily Bender University of Washington Accurate parse ranking is essential for deep linguistic processing applications and is one of the classic problems for academic research in NLP. Despite significant advances, there remains a big need for improvement, especially for domains where...

In 2010 Blog Track, there are two tasks including Faceted Blog Distillation Task and Top Stories Identification Task. We mainly focus on the Top Stories Identification Task. In this task, there are two issues to solve. The first issue is ranking the important news stories on the specified day, named Story Ranking Task. The second issue is named News Blog Post Ranking Task. News Blog Post Rankin...

We propose a new pointwise collaborative ranking approach for recommender systems, which focuses on improving ranking performance at the top of recommended list. Our approach is different from common pointwise methods in that we consider user ratings as ordinal rather than viewing them as real values or categorical labels. In addition, positively rated items (higher rating scores) are emphasize...