Prior work on personalized recommendations has focused exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit interests tastes: online chats between users. The develops an expressive model effective methods for personalizing search-based entity recommendations. User models derived augment re-ranking ...

CAS-ICT took part in the TREC conference for the first time this year. We have participated in three tracks of TREC-10. For adaptive filtering track, we paid more attention to feature selection and profile adaptation. For web track, we tried to integrate different ranking methods to improve system performance. For QA track, we focused on question type identification, named entity tagging and an...

In this paper we describe our participation in the INEX Entity Ranking track. We explored the relations between Wikipedia pages, categories and links. Our approach is to exploit both category and link information. Category information is used by calculating distances between document categories and target categories. Link information is used for relevance propagation and in the form of a docume...

This paper describes our joint participation in the TREC 2012 KBA task. The system is broken down as follows : first name variations of the entity topics are searched then documents containing at least one of them are retrieved. Finally documents go through two classifiers to categorize them as garbage, neutrals, relevant or centrals. This system got good results (3rd of 11) however first analy...

This paper describes algorithms which rerank the top N hypotheses from a maximum-entropy tagger, the application being the recovery of named-entity boundaries in a corpus of web data. The first approach uses a boosting algorithm for ranking problems. The second approach uses the voted perceptron algorithm. Both algorithms give comparable, significant improvements over the maximum-entropy baseli...

In recent years, improvement in ubiquitous technologies and sensor networks have motivated the application of data mining techniques to network organized data. Network data describe entities represented by nodes, which may be connected with (related to) each other by edges. Many network datasets are characterized by a form of autocorrelation where the value of a variable at a given node depends...

Introduction Bibliometric indicators ranking aggregate units have a long tradition, including criticisms of methodology, interpretation and application. Despite the criticism, there is a demand for these indicators, and recent developments have led to improvements of methodology and interpretation. An essential element of these interpretations is to provide estimates of the accuracy, robustness...

Ranking systems have proven to improve the quality of education and help build reputation academic institutions. Each current ranking is based on different methodologies, criteria, standards measurement. Academic employer reputations are subjective indicators some rankings determined through surveying that neither transparent nor traceable. The fall short providing transparency traceability fea...

Named Entities (NEs) play an important role in many natural language and speech processing tasks. A resource that identifies relations between NEs could potentially be very useful. We present such automatically generated knowledge resource from Wikipedia, Named Entity Network (NE-NET), that provides a list of related Named Entities (NEs) and the degree of relation for any given NE. Unlike some ...

Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applications for the contextual exploration of KGs help users explore information extracted from a KG, including SAs, while they are reading an input text. Because of the large number of SAs that can be ...