Semantic Modelling of Citation Contexts for Context-Aware Citation Recommendation

Saier, Tarek; Färber, Michael

doi:10.1007/978-3-030-45439-5_15

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The task of context-aware citation recommendation is to find articles that are relevant to a specific piece of text a writer has generated [17]. It has mainly been studied in the scientific domain [13,18,20,39], but also in the news domain [25]. The main difference between the aforementioned works and our task is that we aim to retrieve articles to expand existing incomplete narratives instead of finding citations for complete narratives.…”

Section: Context-aware Citation Recommendationmentioning

confidence: 99%

News Article Retrieval in Context for Event-centric Narrative Creation

Voskarides¹,

Meij²,

Sauer

et al. 2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

View full text Add to dashboard Cite

Writers such as journalists often use automatic tools to find relevant content to include in their narratives. In this paper, we focus on supporting writers in the news domain to develop event-centric narratives. Given an incomplete narrative that specifies a main event and a context, we aim to retrieve news articles that discuss relevant events that would enable the continuation of the narrative. We formally define this task and propose a retrieval dataset construction procedure that relies on existing news articles to simulate incomplete narratives and relevant articles. Experiments on two datasets derived from this procedure show that state-of-the-art lexical and semantic rankers are not sufficient for this task. We show that combining those with a ranker that ranks articles by reverse chronological order outperforms those rankers alone. We also perform an in-depth quantitative and qualitative analysis of the results that sheds light on the characteristics of this task. CCS CONCEPTS• Information systems → Content ranking.

show abstract

Section: Context-aware Citation Recommendationmentioning

confidence: 99%

News Article Retrieval in Context for Event-centric Narrative Creation

Voskarides¹,

Meij²,

Sauer

et al. 2021

Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval

View full text Add to dashboard Cite

show abstract

“…The task of context-aware citation recommendation is to find articles that are relevant to a specific piece of text a writer has generated [76]. It has mainly been studied in the scientific domain [57,82,86,158], but also in the news domain [102]. The Table 6.9: Examples from the WaPo dev.…”

Section: Context-aware Citation Recommendationmentioning

confidence: 99%

Supporting search engines with knowledge and context

Voskarides

2021

SIGIR Forum

View full text Add to dashboard Cite

Search engines leverage knowledge to improve information access. Such knowledge comes in different forms: unstructured knowledge (e.g., textual documents) and structured knowledge (e.g., relationships between real-world objects and topics). In order to effectively leverage knowledge, search engines should account for context, i.e., information about the user and query. In this thesis, we aim to support search engines in leveraging knowledge while accounting for context. In the first part of this thesis, we study how to make structured knowledge more accessible to the user when the search engine proactively provides such knowledge as context to enrich search results. As a first task, we study how to retrieve descriptions of knowledge facts from a text corpus. Next, we study how to automatically generate knowledge fact descriptions. And finally, we study how to contextualize knowledge facts, that is, to automatically find facts related to a query fact. In the second part of this thesis, we study how to improve interactive knowledge gathering. We focus on conversational search, where the user interacts with the search engine to gather knowledge over large unstructured knowledge repositories. We study multi-turn passage retrieval as an instance of conversational search and focus on query resolution, that is, add missing context from the conversation history to the current turn. We model query resolution as a term classification task and propose a method to address it. In the final part of this thesis, we focus on search engine support for professional writers in the news domain. We study how to support such writers create event-narratives by exploring knowledge from a corpus of news articles. We propose a dataset construction procedure for this task that relies on existing news articles to simulate incomplete narratives and relevant articles. We study the performance of multiple rankers, lexical and semantic, and provide insights into the characteristics of this task. Awarded by : University of Amsterdam, Amsterdam, The Netherlands on 5 February 2021. Supervised by : Maarten de Rijke. Available at : https://hdl.handle.net/11245.1/78187b29-2403-4711-800a-0f92fcb9b15c.

show abstract