News Article Retrieval in Context for Event-centric Narrative Creation

Voskarides, Nikos; Meij, Edgar; Sauer, Sabrina; Rijke, Maarten de

doi:10.1145/3471158.3472247

Cited by 3 publications

References 47 publications

(67 reference statements)

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SJORS: A Semantic Recommender System for Journalists

Garrido,

Pera,

Bobed

2023

Bus Inf Syst Eng

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Recommender Systems support a broad range of domains, each with peculiarities that recommendation algorithms must consider to produce appropriate suggestions. In the paper, we bring attention to a little-studied scenario related to the news domain: recommendations catering to media journalists. Based on the particular needs inherent to a newsroom, the authors introduce SJORS, a wire news Recommender System that takes into account the activities of each journalist as well as other critical factors that arise in this particular domain, such as wire news recency. Given the nature of the items recommended, SJORS deals with the inherent ambiguity of natural language by exploiting different semantic techniques and technologies. The authors have conducted several experiments in a media company, which validated the performance and applicability of the system. Outcomes emerging from this work could be extended to other domains of interest, such as online stores, streaming platforms, or digital libraries, to name a few.

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SJORS: A Semantic Recommender System for Journalists

Garrido,

Pera,

Bobed

2023

Bus Inf Syst Eng

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Supporting search engines with knowledge and context

Voskarides

2021

SIGIR Forum

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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.

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