TempTabQA: Temporal Question Answering for Semi-Structured Tables

Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TempTabQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.Comment: EMNLP 2023(Main), 23 Figures, 32 Table

Old Questions, New Techniques: A Research Note on the Computational Identification of Political Elites

Merit, Expertise and Measuremen

Open Knowledge Enrichment for Long-tail Entities

Knowledge bases (KBs) have gradually become a valuable asset for many AI applications. While many current KBs are quite large, they are widely acknowledged as incomplete, especially lacking facts of long-tail entities, e.g., less famous persons. Existing approaches enrich KBs mainly on completing missing links or filling missing values. However, they only tackle a part of the enrichment problem and lack specific considerations regarding long-tail entities. In this paper, we propose a full-fledged approach to knowledge enrichment, which predicts missing properties and infers true facts of long-tail entities from the open Web. Prior knowledge from popular entities is leveraged to improve every enrichment step. Our experiments on the synthetic and real-world datasets and comparison with related work demonstrate the feasibility and superiority of the approach.Comment: Accepted by the 29th International World Wide Web Conference (WWW 2020

Exploring Entity Associations Over Time

We address the problem of entity-oriented search in the humanities and social sciences domain. We are particularly interested in retrieving entities related to a query entity and finding associations between these entities over time. Evidence from our target end users suggests that it is more informative to view these associations as dynamic phenomena that evolve over time than as static phenomena. We present work-in-progress on methods to extract these associations and their temporal extent, and discuss a way of presenting them in an exploratory search interface. This interface is intended to help users to discover interesting associations between entities over time