In a Web plagued by disappearing resources, Web archive collections provide a valuable means of preserving Web resources important to the study of past events ranging from elections to disease outbreaks. These archived collections start with seed URIs (Uniform Resource Identifiers) hand-selected by curators. Curators produce high quality seeds by removing non-relevant URIs and adding URIs from credible and authoritative sources, but this ability comes at a cost: it is time consuming to collect these seeds. Two main strategies adopted by curators for discovering seeds include scraping Web (e.g., Google) Search Engine Result Pages (SERPs) and social media (e.g., Twitter) SERPs. In this work, we studied three social media platforms in order to provide some insight on the characteristics of seeds generated from different sources. First, we developed a simple vocabulary for describing social media posts across different platforms. Second, we introduced a novel source for generating seeds from URIs in the threaded conversations of social media posts created by single or multiple users. Users on social media sites routinely create and share posts about news events consisting of hand-selected URIs of news stories, tweets, videos, etc. In this work, we call these posts micro-collections, whether shared on Reddit or Twitter, and we consider them as an important source for seeds. This is because, the effort taken to create micro-collections is an indication of editorial activity and a demonstration of domain expertise. Third, we generated 23,112 seed collections with text and hashtag queries from 449,347 social media posts from Reddit, Twitter, and Scoop.it. We collected in total 120,444 URIs from the conventional scraped SERP posts and micro-collections. We characterized the resultant seed collections across multiple dimensions including the distribution of URIs, precision, ages, diversity of webpages, etc. We showed that seeds generated by scraping SERPs had a higher median probability (0.63) of producing relevant URIs than micro-collections (0.5). However, micro-collections were more likely to produce seeds with a higher precision than conventional SERP collections for Twitter collections generated with hashtags. Also, micro-collections were more likely to produce older webpages and more non-HTML documents.
In a similar work, @cite_19 adapted some portions of the in a method to extract event-centric documents from Web archives based on a specialized focused extraction algorithm. They defined two broad kinds of events based on time: and . The goal of the event-centric extraction process is, given an event input and a Web archive, generate an interlinked collection of documents relevant to the input event that meet the . The differences of our research with Gossen's previous work @cite_16 transfer to this work. However, we adapted Gossen's categorization of events as either or , and we renamed to (Table ). Similar to , @cite_28 presented an approach for extracting event-centric sub-collection from Web Archives. Their method extracts documents not only related to the input event, but also documents describing related events (e.g., premises and consequences). 's method utilized Wikipedia pages as inputs to generate event-centric collections. In this work, however, we used Wikipedia references to generate our gold standard dataset.
@cite_28 @cite_19 @cite_16
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Event extraction is a particularly challenging type of information extraction (IE). Most current event extraction systems rely on local information at the phrase or sentence level. However, this local context may be insufficient to resolve ambiguities in identifying particular types of events; information from a wider scope can serve to resolve some of these ambiguities. In this paper, we use document level information to improve the performance of ACE event extraction. In contrast to previous work, we do not limit ourselves to information about events of the same type, but rather use information about other types of events to make predictions or resolve ambiguities regarding a given event. We learn such relationships from the training corpus and use them to help predict the occurrence of events and event arguments in a text. Experiments show that we can get 9.0 (absolute) gain in trigger (event) classification, and more than 8 gain for argument (role) classification in ACE event extraction. This paper addresses the extraction of event records from documents that describe multiple events. Specifically, we aim to identify the fields of information contained in a document and aggregate together those fields that describe the same event. To exploit the inherent connections between field extraction and event identification, we propose to model them jointly. Our model is novel in that it integrates information from separate sequential models, using global potentials that encourage the extracted event records to have desired properties. While the model contains high-order potentials, efficient approximate inference can be performed with dual-decomposition. We experiment with two data sets that consist of newspaper articles describing multiple terrorism events, and show that our model substantially outperforms traditional pipeline models. Event extraction is the task of detecting certain specified types of events that are mentioned in the source language data. The state-of-the-art research on the task is transductive inference (e.g. cross-event inference). In this paper, we propose a new method of event extraction by well using cross-entity inference. In contrast to previous inference methods, we regard entity-type consistency as key feature to predict event mentions. We adopt this inference method to improve the traditional sentence-level event extraction system. Experiments show that we can get 8.6 gain in trigger (event) identification, and more than 11.8 gain for argument (role) classification in ACE event extraction. Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suffer from error propagation since event triggers and arguments are predicted in isolation by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved. In addition, we propose to incorporate global features which explicitly capture the dependencies of multiple triggers and arguments. Experimental results show that our joint approach with local features outperforms the pipelined baseline, and adding global features further improves the performance significantly. Our approach advances state-ofthe-art sentence-level event extraction, and even outperforms previous argument labeling methods which use external knowledge from other sentences and documents.