On a Combination of Probabilistic and Boolean IR Models for Question Answering

Yoshioka, Masaharu

doi:10.1007/978-3-642-17187-1_55

Cited by 3 publications

(3 citation statements)

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“…Another approach utilized a NER system and/or geographic resources to extract named entity information including geographic and temporal information from the queries and documents. The best performing NTCIR-8 Japanese run was a hybrid approach that combined the probabilistic approach and weighted Boolean query formulation based on the NER results (Yoshioka 2010). There were approaches that focused on geographic information including the hierarchical relationship among location names (e.g., Tokyo is a part of Japan) and the distance between the extracted location of the query and document, and there were several discussions about the temporal information.…”

Section: Ntcir-8 Geotime Taskmentioning

confidence: 99%

Temporal Information Access

Yoshioka

Joho

2020

The Information Retrieval Series

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Section: Ntcir-8 Geotime Taskmentioning

confidence: 99%

Temporal Information Access

Yoshioka

Joho

2020

The Information Retrieval Series

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“…Geotemporal information retrieval (GTIR) aims at extracting both geospatial and temporal information and incorporating this information into the retrieval model. A wide variety of approaches in NTCIR (Gey, Larson, Kando, Machado, & Sakai, ) was utilized for improving the geotemporal search (Cardoso & Silva, ; Harris, ; Machado, Borbinha, & Martins, ; Mata & Claramunt, ; Mori, ; Yoshioka, ). Although the combination of geospatial and temporal metadata into GTIR leads to significant improvements in traditional search engines, they were exploited separately either at the indexing stage or in a final reranking (Cardoso & Silva, ), or exploited for filtering purposes (Machado et al., ) or for geotemporal ontology‐driven retrieval (Mata & Claramunt,).…”

Section: Introductionmentioning

confidence: 99%

Modeling geographic, temporal, and proximity contexts for improving geotemporal search

Daoud

Huang

2012

J Am Soc Inf Sci Tec

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Traditional information retrieval (IR) systems show significant limitations on returning relevant documents thatsatisfy the user's information needs. In particular, to answer geographic and temporal user queries, the IR task becomes a nonstraightforward process where the available geographic and temporal information is often unstructured. In this article, we propose a geotemporal search approach that consists of modeling and exploiting geographic and temporal query context evidence that refers to implicit multivarying geographic and temporal intents behind the query. Modeling geographic and temporal query contexts is based on extracting and ranking geographic and temporal keywords found in pseudo-relevant feedback (PRF) documents for a given query. Our geotemporal search approach is based on exploiting the geographic and temporal query contexts separately into a probabilistic ranking model and jointly into a proximity ranking model. Our hypothesis is based on the concept that geographic and temporal expressions tend to co-occur within the document where the closer they are in the document, the more relevant the document is. Finally, geographic, temporal, and proximity scores are combined according to a linear combination formula. An extensive experimental evaluation conducted on a portion of the New York Times news collection and the TREC 2004 robust retrieval track collection shows that our geotemporal approach outperforms significantly a well-known baseline search and the best known geotemporal search approaches in the domain. Finally, an in-depth analysis shows a positive correlation between the geographic and temporal query sensitivity and the retrieval performance. Also, we find that geotemporal distance has a positive impact on retrieval performance generally.

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“…Based on these assumptions, we implemented IR system for Japanese Bar Exam question answering based on ABRIR [Yos10]. This system calculates basic similarity of question and documents by using Okapi/BM25 [RW00] and use a Boolean query to calculate penalty when the articles don't satisfy the Boolean query.…”

Section: Ir System In Coliee 2016mentioning

confidence: 99%

A Civil Code Article Information Retrieval System based on Phrase Alignment with Article Structure Analysis and Ensemble Approach

Yoshioka¹,

Onodera²

EPiC Series in Computing

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In this paper, we introduce a system for COLIEE task phase 1 that retrieves relevant civil code article(s) for making correct entailment to the questions of Japanese Bar Exam. This system is an extended version of our previous system that based on legal terminology and civil code article structure. However, the performance of the previous system is not as good as best performance system of the task. In this paper, we introduce concept of phrase alignment that takes into account the civil code article structure. In addition, due to the variations of the question types, the settings that are good for particular type of questions may not be good for other types of questions. Therefore, we propose to use systems with different settings and generate final answer by aggregating the output of different systems based on ensemble approach. Finally, we also discuss the difference between English task and Japanese task based on the retrieval results of Indri, one of the state-of-the-art information retrieval system.

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On a Combination of Probabilistic and Boolean IR Models for Question Answering

Cited by 3 publications

References 4 publications

Temporal Information Access

Temporal Information Access

Modeling geographic, temporal, and proximity contexts for improving geotemporal search

A Civil Code Article Information Retrieval System based on Phrase Alignment with Article Structure Analysis and Ensemble Approach

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