Recall oriented search on the web using semantic annotations

Kaptein, Rianne; Broek, Egon L. van den; Koot, Gijs; Veld, M.A.A. Huis in 't

doi:10.1145/2513204.2513219

“…Kaptein et al [12] discusse a a number of possible approaches for reusing multiple existing web search engines to create a recall-oriented search engine. Specifically, three abstract techniques to re-order the retrieved results are discussed: clustering, reranking, or aggregation ("analysis").…”

Section: Accepted Papersmentioning

confidence: 99%

Report on the sixth workshop on exploiting semantic annotations in information retrieval (ESAIR'13)

Bennett

¹

,

Gabrilovich

²

,

Kamps

³

et al. 2014

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There is an increasing amount of structure on the web as a result of modern web languages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. ESAIR'13 focuses on two of the most challenging aspects to address in the coming years. First, there is a need to include the currently emerging knowledge resources (such as DBpedia, Freebase) as underlying semantic model giving access to an unprecedented scope and detail of factual information. Second, there is a need to include annotations beyond the topical dimension (think of sentiment, reading level, prerequisite level, etc) that contain vital cues for matching the specific needs and profile of the searcher at hand. There was a strong feeling that we made substantial progress. Specifically, the discussion contributed to our understanding of the way forward. First, emerging large scale knowledge bases form a crucial component for semantic search, providing a unified framework with zillions of entities and relations. Second, in addition to low level factual annotation, non-topical annotation of larger chunks of text can provide powerful cues on the expertise of the search and (un)suitability of information. Third, novel user interfaces are key to unleash powerful structured querying enabled by semantic annotation-the potential of rich document annotations can only be realized if matched by more articulate queries exploiting these powerful retrieval cues-and a more dynamic approach is emerging by exploiting new forms of query autosuggest.

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“…Kaptein et al [12] discusse a a number of possible approaches for reusing multiple existing web search engines to create a recalloriented search engine. Specifically, three abstract techniques to re-order the retrieved results are discussed: clustering, reranking, or aggregation ("analysis").…”

Section: Accepted Papersmentioning

confidence: 99%

Sixth workshop on exploiting semantic annotations in information retrieval (ESAIR'13)

Bennett

¹

,

Gabrilovich

²

,

Kamps

³

et al. 2013

Proceedings of the 22nd ACM International Conference on Information &Amp; Knowledge Management

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There is an increasing amount of structure on the web as a result of modern web languages, user tagging and annotation, emerging robust NLP tools, and an ever growing volume of linked data. These meaningful, semantic, annotations hold the promise to significantly enhance information access, by enhancing the depth of analysis of today's systems. Currently, we have only started exploring the possibilities and only begin to understand how these valuable semantic cues can be put to fruitful use. ESAIR'13 focuses on two of the most challenging aspects to address in the coming years. First, there is a need to include the currently emerging knowledge resources (such as DBpedia, Freebase) as underlying semantic model giving access to an unprecedented scope and detail of factual information. Second, there is a need to include annotations beyond the topical dimension (think of sentiment, reading level, prerequisite level, etc) that contain vital cues for matching the specific needs and profile of the searcher at hand.

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“…for automating search strate-gies or text extraction from documents (MacFarlane et al, 2021;Russell-Rose et al, 2021). However, even with the help of suitable tools, searching for specific text passages in large text corpora generally remains a difficult task, in particular when the search is recalloriented (Bache, 2011;Kaptein et al, 2013;Noor and Bashir, 2015). Turning to our own use case, the search for specific content in transcripts of genocide tribunals further proves difficult because transcripts are only accessible individually (usually one court day per transcript) and in different formats, depending on the tribunal.…”

Section: Introductionmentioning

confidence: 99%

A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

Schirmer¹,

Kruschwitz²,

Donabauer³

2022

Preprint

0

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Recent progress in natural language processing has been impressive in many different areas with transformer-based approaches setting new benchmarks for a wide range of applications. This development has also lowered the barriers for people outside the NLP community to tap into the tools and resources applied to a variety of domain-specific applications. The bottleneck however still remains the lack of annotated gold-standard collections as soon as one's research or professional interest falls outside the scope of what is readily available. One such area is genocide-related research (also including the work of experts who have a professional interest in accessing, exploring and searching large-scale document collections on the topic, such as lawyers). We present GTC (Genocide Transcript Corpus), the first annotated corpus of genocide-related court transcripts which serves three purposes: (1) to provide a first reference corpus for the community, (2) to establish benchmark performances (using state-of-the-art transformer-based approaches) for the new classification task of paragraph identification of violence-related witness statements, (3) to explore first steps towards transfer learning within the domain. We consider our contribution to be addressing in particular this year's hot topic on Language Technology for All.

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Recall oriented search on the web using semantic annotations

Cited by 4 publications

References 11 publications

Report on the sixth workshop on exploiting semantic annotations in information retrieval (ESAIR'13)

Report on the sixth workshop on exploiting semantic annotations in information retrieval (ESAIR'13)

Sixth workshop on exploiting semantic annotations in information retrieval (ESAIR'13)

A New Dataset for Topic-Based Paragraph Classification in Genocide-Related Court Transcripts

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