In the last few years, open-domain question answering (ODQA) has advanced rapidly due to the development of deep learning techniques and the availability of large-scale QA datasets. However, the current datasets are essentially designed for synchronic document collections (e.g., Wikipedia). Temporal news collections such as long-term news archives spanning several decades, are rarely used in training the models despite they are quite valuable for our society. To foster the research in the field of ODQA on such historical collections, we present ArchivalQA, a large question answering dataset consisting of 532,444 question-answer pairs which is designed for temporal news QA. We divide our dataset into four subparts based on the question difficulty levels and the containment of temporal expressions, which we believe are useful for training and testing ODQA systems characterized by different strengths and abilities. The novel QA dataset-constructing framework that we introduce can be also applied to generate non-ambiguous questions of good quality over other types of temporal document collections.
National Library of Finland, Centre for Preservation and Digitization（芬兰国家图书馆，保存与数字化中心）；Aalto University, Semantic Computing Research Group（阿尔托大学，语义计算研究组）；University of Helsinki, Department of Modern Languages（赫尔辛基大学，现代语言系）；Department of Linguistics and English Language, Lancaster University（兰卡斯特大学，语言学与英语语言系）
Named Entity Recognition (NER), search, classification and tagging of names and name like frequent informational elements in texts, has become a standard information extraction procedure for textual data. NER has been applied to many types of texts and different types of entities: newspapers, fiction, historical records, persons, locations, chemical compounds, protein families, animals etc. In general a NER system's performance is genre and domain dependent and also used entity categories vary (Nadeau and Sekine, 2007). The most general set of named entities is usually some version of three pa…
University of Eastern Finland（东芬兰大学）；Tampere University（坦佩雷大学）；University of Helsinki（赫尔辛基大学）；The National Library of Finland（芬兰国家图书馆）
Introduction. We study effect of different quality optical character recognition in interactive information retrieval with a collection of one digitized historical Finnish newspaper. Method. This study is based on the simulated interactive information retrieval work task model. Thirty-two users made searches to an article collection of Finnish newspaper Uusi Suometar 1869-1918 with ca. 1.45 million auto segmented articles. Our article search database had two versions of each article with different quality optical character recognition. Each user performed six pre-formulated and six self-formul…
The National Library of Finland has digitized the historical newspapers published in Finland between 1771 and 1910. This collection contains approximately 1.95 million pages in Finnish and Swedish. Finnish part of the collection consists of about 2.40 billion words. The National Library's Digital Collections are offered via the digi.kansalliskirjasto.fi web service, also known as Digi. Part of the newspaper material (from 1771 to 1874) is also available freely downloadable in The Language Bank of Finland provided by the FINCLARIN consortium. The collection can also be accessed through the Korp…
University of Eastern Finland（东芬兰大学）；Tampere University（坦佩雷大学）；University of Helsinki（赫尔辛基大学）
Effects of Optical Character Recognition (OCR) quality on historical information retrieval have so far been studied in data-oriented scenarios regarding the effectiveness of retrieval results. Such studies have either focused on the effects of artificially degraded OCR quality (see, e.g., [1-2]) or utilized test collections containing texts based on authentic low quality OCR data (see, e.g., [3]). In this paper the effects of OCR quality are studied in a user-oriented information retrieval setting. Thirty-two users evaluated subjectively query results of six topics each (out of 30 topics) base…
The University of Western Australia（西澳大学）；Leipzig University（莱比锡大学）；National Library of Norway（挪威国家图书馆）；Inria Paris（法国国家信息与自动化研究所巴黎分院）；Leiden University（莱顿大学）；Bayerische Staatsbibliothek（巴伐利亚州立图书馆）；British Library（大英图书馆）
In this work, we explore whether the recently demonstrated zero-shot abilities of the T0 model extend to Named Entity Recognition for out-of-distribution languages and time periods. Using a historical newspaper corpus in 3 languages as test-bed, we use prompts to extract possible named entities. Our results show that a naive approach for prompt-based zero-shot multilingual Named Entity Recognition is error-prone, but highlights the potential of such an approach for historical languages lacking labeled datasets. Moreover, we also find that T0-like models can be probed to predict the publication…
Generalized Stock Price Prediction for Multiple Stocks Combined with News Fusion
Graduate Institute of Electrical Engineering, National Taiwan University（国立台湾大学电子工程研究所）；Institute of Information Science, Academia Sinica（学术院信息科学研究所）；United Link Co., Ltd.（联合链接公司）；University of Washington（华盛顿大学）；National Tsing Hua University（国立清华大学）
University of Bonn（波恩大学）；Farfetch（Farfetch公司）；Fraunhofer IAIS（弗劳恩霍夫智能分析与信息系统研究所）
We propose an approach to predict the natural gas price in several days using historical price data and events extracted from news headlines. Most previous methods treats price as an extrapolatable time series, those analyze the relation between prices and news either trim their price data correspondingly to a public news dataset, manually annotate headlines or use off-the-shelf tools. In comparison to off-the-shelf tools, our event extraction method detects not only the occurrence of phenomena but also the changes in attribution and characteristics from public sources. Instead of using senten…
University of La Rochelle（拉罗谢尔大学）；EPFL（洛桑联邦理工学院）；University of Toulouse（图卢兹大学）
Large language models (LLMs) have been leveraged for several years now, obtaining state-of-the-art performance in recognizing entities from modern documents. For the last few months, the conversational agent ChatGPT has "prompted" a lot of interest in the scientific community and public due to its capacity of generating plausible-sounding answers. In this paper, we explore this ability by probing it in the named entity recognition and classification (NERC) task in primary sources (e.g., historical newspapers and classical commentaries) in a zero-shot manner and by comparing it with state-of-th…
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
Yale University, New Haven, CT, USA（耶鲁大学）；McGill University, Montreal, QC, Canada（麦吉尔大学）；University of Texas Rio Grande Valley, TX, USA（德克萨斯大学里奥格兰德谷分校）
HistNERo: Historical Named Entity Recognition for the Romanian Language
Research Institute for Artificial Intelligence “Mihai Drăgănescu”, Romanian Academy（罗马尼亚科学院“米哈伊·德勒格内斯库”人工智能研究所）；National University of Science and Technology POLITEHNICA Bucharest（布加勒斯特理工国立科技大学）
Shanghai University of Finance and Economics（上海财经大学）
Research on crude oil price forecasting has attracted tremendous attention from scholars and policymakers due to its significant effect on the global economy. Besides supply and demand, crude oil prices are largely influenced by various factors, such as economic development, financial markets, conflicts, wars, and political events. Most previous research treats crude oil price forecasting as a time series or econometric variable prediction problem. Although recently there have been researches considering the effects of real-time news events, most of these works mainly use raw news headlines or…
The Paradigm Shift: A Comprehensive Survey on Large Vision Language Models for Multimodal Fake News Detection
College of Computer and Mathematics, Central South University of Forestry and Technology（计算机与数学学院，中央南大学林业科技学院）；College of Computer Science and Electronic Engineering, Hunan University（计算机科学与电子工程学院，湖南大学）；College of Economics and Management, Central South University of Forestry and Technology（经济管理学院，中央南大学林业科技学院）；Department of Computer Science, State University of New York（计算机科学系，纽约州立大学）
American Stories: A Large-Scale Structured Text Dataset of Historical U.S. Newspapers
Harvard University（哈佛大学）；Massachusetts Institute of Technology（麻省理工学院）；Princeton University（普林斯顿大学）；National Bureau of Economic Research（美国国家经济研究局）；New York University（纽约大学）；Northwestern University（西北大学）
Detecting Historical Turning Points in Italian Media: A Complex Systems Approach to a Diachronic News Corpus
Department of Physics and Chemistry, University of Palermo（帕尔米洛大学物理与化学系）；Institute for Cross-Disciplinary Physics and Complex Systems (IFISC), UIB-CSIC（跨学科物理与复杂系统研究所（IFISC）， UIB-CSIC）
We extend the well-known word analogy task to a one-to-X formulation, including one-to-none cases, when no correct answer exists. The task is cast as a relation discovery problem and applied to historical armed conflicts datasets, attempting to predict new relations of type `location:armed-group' based on data about past events. As the source of semantic information, we use diachronic word embedding models trained on English news texts. A simple technique to improve diachronic performance in such task is demonstrated, using a threshold based on a function of cosine distance to decrease the num…
L3S Research Center, Leibniz Universität Hannover（汉诺威莱布尼茨大学L3S研究中心）；Leibniz Universität Hannover（汉诺威莱布尼茨大学）
One of the key requirements to facilitate semantic analytics of information regarding contemporary and historical events on the Web, in the news and in social media is the availability of reference knowledge repositories containing comprehensive representations of events and temporal relations. Existing knowledge graphs, with popular examples including DBpedia, YAGO and Wikidata, focus mostly on entity-centric information and are insufficient in terms of their coverage and completeness with respect to events and temporal relations. EventKG presented in this paper is a multilingual event-centri…
