Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
We demonstrate B N , a system to mine, rank and visualize biomedical news events. B N takes biome... more We demonstrate B N , a system to mine, rank and visualize biomedical news events. B N takes biomedical queries such as "Ebola virus disease" and retrieves the k most relevant news events for them. To achieve this we rst mine the generic news events by clustering them on a daily basis using general named entities and textual features. ese clusters are also tagged with disambiguated biomedical entities which aid in biomedical news event exploration. ese clusters are then used to compute the importance scores for the event clusters based on a combination of textual, semantic, popularity and historical importance features. B N also visualizes the retrieved event clusters to highlight the top news events and corresponding news articles for the given query. e visualization also provides the context for news events using (1) a chain of historically relevant news event clusters, and (2) other non-biomedical events from the same day.
The incomprehensible amount of information available online has made it difficult to retrospect o... more The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address the linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best.
IV nizations contributed to the building of a large-scale test collection by creating topics, per... more IV nizations contributed to the building of a large-scale test collection by creating topics, performing retrieval runs and providing relevance assessments. The workshop concludes the results of this large-scale effort, summarizes and addresses encountered issues and devises a work plan for the future evaluation of XML retrieval systems. All INEX tracks start from having available suitable text collections. We gratefully acknowledge the data made available by: Amazon and LibraryThing (Social Book Search Track), Microsoft Research (Social Book Search Track), the DBpedia (Linked Data Track), and the Wikimedia Foundation (Linked Data, Relevance Feedback, Snippet Retrieval, and Tweet Contextualization Tracks). Finally, INEX is run for, but especially by, the participants. It is a result of tracks and tasks suggested by participants, topics created by particants, systems built by participants, and relevance judgments provided by participants. So the main thank you goes each of these individuals!
ABSTRACT INEX investigates focused retrieval from structured documents by providing large test co... more ABSTRACT INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organi- zations to compare their results. This paper reports on the INEX’12 evaluation campaign, which consisted of a five tracks: Linked Data, Relevance Feedback, Snippet Retrieval, Social Book Search, and Tweet Contextualization. INEX’12 was an exciting year for INEX in which we joined forces with CLEF and for the first time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums.
INEX investigates focused retrieval from structured documents by providing large test collections... more INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data (Social Book Search track); searching structured or semantic data (Linked Data track); and focused retrieval (Snippet Retrieval and Tweet Contextualization tracks). INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with (other activities in) CLEF and for the second time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results.
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
We demonstrate B N , a system to mine, rank and visualize biomedical news events. B N takes biome... more We demonstrate B N , a system to mine, rank and visualize biomedical news events. B N takes biomedical queries such as "Ebola virus disease" and retrieves the k most relevant news events for them. To achieve this we rst mine the generic news events by clustering them on a daily basis using general named entities and textual features. ese clusters are also tagged with disambiguated biomedical entities which aid in biomedical news event exploration. ese clusters are then used to compute the importance scores for the event clusters based on a combination of textual, semantic, popularity and historical importance features. B N also visualizes the retrieved event clusters to highlight the top news events and corresponding news articles for the given query. e visualization also provides the context for news events using (1) a chain of historically relevant news event clusters, and (2) other non-biomedical events from the same day.
The incomprehensible amount of information available online has made it difficult to retrospect o... more The incomprehensible amount of information available online has made it difficult to retrospect on past events. We propose a novel linking problem to connect excerpts from Wikipedia summarizing events to online news articles elaborating on them. To address the linking problem, we cast it into an information retrieval task by treating a given excerpt as a user query with the goal to retrieve a ranked list of relevant news articles. We find that Wikipedia excerpts often come with additional semantics, in their textual descriptions, representing the time, geolocations, and named entities involved in the event. Our retrieval model leverages text and semantic annotations as different dimensions of an event by estimating independent query models to rank documents. In our experiments on two datasets, we compare methods that consider different combinations of dimensions and find that the approach that leverages all dimensions suits our problem best.
IV nizations contributed to the building of a large-scale test collection by creating topics, per... more IV nizations contributed to the building of a large-scale test collection by creating topics, performing retrieval runs and providing relevance assessments. The workshop concludes the results of this large-scale effort, summarizes and addresses encountered issues and devises a work plan for the future evaluation of XML retrieval systems. All INEX tracks start from having available suitable text collections. We gratefully acknowledge the data made available by: Amazon and LibraryThing (Social Book Search Track), Microsoft Research (Social Book Search Track), the DBpedia (Linked Data Track), and the Wikimedia Foundation (Linked Data, Relevance Feedback, Snippet Retrieval, and Tweet Contextualization Tracks). Finally, INEX is run for, but especially by, the participants. It is a result of tracks and tasks suggested by participants, topics created by particants, systems built by participants, and relevance judgments provided by participants. So the main thank you goes each of these individuals!
ABSTRACT INEX investigates focused retrieval from structured documents by providing large test co... more ABSTRACT INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organi- zations to compare their results. This paper reports on the INEX’12 evaluation campaign, which consisted of a five tracks: Linked Data, Relevance Feedback, Snippet Retrieval, Social Book Search, and Tweet Contextualization. INEX’12 was an exciting year for INEX in which we joined forces with CLEF and for the first time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums.
INEX investigates focused retrieval from structured documents by providing large test collections... more INEX investigates focused retrieval from structured documents by providing large test collections of structured documents, uniform evaluation measures, and a forum for organizations to compare their results. This paper reports on the INEX 2013 evaluation campaign, which consisted of four activities addressing three themes: searching professional and user generated data (Social Book Search track); searching structured or semantic data (Linked Data track); and focused retrieval (Snippet Retrieval and Tweet Contextualization tracks). INEX 2013 was an exciting year for INEX in which we consolidated the collaboration with (other activities in) CLEF and for the second time ran our workshop as part of the CLEF labs in order to facilitate knowledge transfer between the evaluation forums. This paper gives an overview of all the INEX 2013 tracks, their aims and task, the built test-collections, and gives an initial analysis of the results.
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Papers by Arunav Mishra