Linguistic and Statistical Analysis of the CLEF Topics

Mandl, Thomas; Womser‐Hacker, Christa

doi:10.1007/978-3-540-45237-9_43

Cited by 15 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The purpose of these features is to describe some of the linguistic characteristics of a given text, and to study their correlations with themselves and other phenomena. [11] used such techniques, and manually identified some features on CLEF topics.…”

Section: Topic Featuresmentioning

confidence: 99%

See 1 more Smart Citation

Linguistic Analysis of Users' Queries: Towards an Adaptive Information Retrieval System

Mothe

Tanguy

2007

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System

View full text Add to dashboard Cite

show abstract

Section: Topic Featuresmentioning

confidence: 99%

“…The RIA workshop focused on query expansion issue and analysed both system and topic variability factors on TREC collections. [11] report a work on CLEF topics, studying correlation between system performance and query features. They found a correlation of 0.4 between the number of proper nouns and average precision.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Analysis of Users' Queries: Towards an Adaptive Information Retrieval System

Mothe

Tanguy

2007

2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System

View full text Add to dashboard Cite

show abstract

“…For example, topic 94 which more than doubled its MAP with QE, had terms appearing in only 31 documents across the entire collection. [17] demonstrated this fact when evaluating several CLEF runs. They observed a high standard deviation for the performance of the topics and a high standard deviation for the performance of each run.…”

Section: E Training Individual Topicsmentioning

confidence: 74%

Cell assemblies for query expansion in Information Retrieval

Volpe

Moreira

Huyck

2011

The 2011 International Joint Conference on Neural Networks

View full text Add to dashboard Cite

Abstract-One of the main tasks in Information Retrieval is to match a user query to the documents that are relevant for it. This matching is challenging because in many cases the keywords the user chooses will be different from the words the authors of the relevant documents have used. Throughout the years, many approaches have been proposed to deal with this problem. One of the most popular consists in expanding the query with related terms with the goal of retrieving more relevant documents. In this paper, we propose a new method in which a Cell Assembly model is applied for query expansion. Cell Assemblies are reverberating circuits of neurons that can persist long beyond the initial stimulus has ceased. They learn through Hebbian Learning rules and have been used to simulate the formation and the usage of human concepts. We adapted the Cell Assembly model to learn relationships between the terms in a document collection. These relationships are then used to augment the original queries. Our experiments use standard Information Retrieval test collections and show that some queries significantly improved their results with our technique.

show abstract

“…The case of difficult queries and topics has been specifically studied. Mandl and Womser-Hacker (2003) analyzed CLEF topics and the correlation between topic features and system performance. They found a correlation of 0.4 between the number of proper nouns and average precision.…”

Section: Variability Topic Difficulty and Topic Clusteringmentioning

confidence: 99%

Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and TREC topics

et al. 2011

View full text Add to dashboard Cite

Abstract.To evaluate Information Retrieval Systems on their effectiveness, evaluation programs such as TREC offer a rigorous methodology as well as benchmark collections. Whatever the evaluation collection used, effectiveness is generally considered globally, averaging the results over a set of information needs. As a result, the variability of system performance is hidden as the similarities and differences from one system to another are averaged. Moreover, the topics on which a given system succeeds or fails are left unknown. In this paper we propose an approach based on data analysis methods (correspondence analysis and clustering) to discover correlations between systems and to find trends in topic/system correlations. We show that it is possible to cluster topics and systems according to system performance on these topics, some system clusters being better on some topics. Finally, we propose a new method to consider complementary systems as based on their performances which can be applied for example in the case of repeated queries. We consider the system profile based on the similarity of the set of TREC topics on which systems achieve similar levels of performance. We show that this method is effective when using the TREC ad hoc collection.

show abstract

Linguistic and Statistical Analysis of the CLEF Topics

Cited by 15 publications

References 4 publications

Linguistic Analysis of Users' Queries: Towards an Adaptive Information Retrieval System

Linguistic Analysis of Users' Queries: Towards an Adaptive Information Retrieval System

Cell assemblies for query expansion in Information Retrieval

Fusing different information retrieval systems according to query-topics: a study based on correlation in information retrieval systems and TREC topics

Contact Info

Product

Resources

About