Open domain event extraction from twitter

By Alan Ritter, Mausam, Oren Etzioni, Sam Clark. Knowledge Discovery and Data Mining, 2012

659 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/c5262730d8854f88106bdc204860ccf236b3345f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

Citations in the corpus

SemEval-2013 Task 2: Sentiment Analysis in Twitter (2013, background): “We then identified popular topics as those named entities that are frequently mentioned in association with a specific date (Ritter et al., 2012).”
Exploiting the Ground-Truth: An Adversarial Imitation Based Knowledge Distillation Approach for Event Detection (2019, background): “Amongst, a major challenge faced by ED is the ambiguity in natural language expressions (Poon and Vanderwende 2010; Ritter et al. 2012; Li, Ji, and Huang 2013).”
Adaptive Co-attention Network for Named Entity Recognition in Tweets (2018, background): “…of social media (e.g., Twitter, Facebook), tweets have become important resources for various applications such as breaking news aggregation (Ritter et al. 2012; Cui et al. 2012), the identification of cyber-attacks (Ritter et al. 2015) or natural disasters (Neubig et al. 2011; Bruns and…”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, background): “As noted by Ritter et al., building up a true timeline of event occurrences requires resolution of the temporal expressions accompanying such non-concurrent personal experience reports [38].”
Learning to Automatically Solve Algebra Word Problems (2014, background): “However, some recent work has studied the automatic induction of the set of possible templates from data (Chambers and Jurafsky, 2011; Ritter et al., 2012).”
Measuring Social Jetlag in Twitter Data (2016, background): “2011), earthquake observations (Sakaki, Okazaki, and Matsuo 2010), event extraction (Ritter et al. 2012), etc.”
A survey on sentiment analysis methods, applications, and challenges (2022, background): “It is important to remember that developing a classification model requires first identifying relevant features in dataset (Ritter et al. 2012).”
MAVEN: A Massive General Domain Event Detection Dataset (2020, background): “…event extraction on special domains, like the bio-medical domain (Pyysalo et al., 2007; Kim et al., 2008; Thompson et al., 2009; Buyko et al., 2010; N´edellec et al., 2013), literature (Sims et al., 2019), Twitter (Ritter et al., 2012; Guo et al., 2013) and breaking news (Pustejovsky et al., 2003).”
How Twitter is Changing the Nature of Financial News Discovery (2016, background): “Event tweet clustering can help [41, 46], but natural language processing of Twitter is substantially more difficult than news [15].”
That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets (2015, methodology): “…named entity recognition (Ritter et al., 2011), summarization (O’Connor et al., 2010b), sentiment analysis (Agarwal et al., 2011), event extraction (Ritter et al., 2012; Li et al., 2014a), paraphrasing (Xu et al., 2014), machine translation (Ling et al., 2013), and dependency parsing (Kong et…”
Short Text Topic Modeling Techniques, Applications, and Performance: A Survey (2019, background): “The important task of event tracking is to observe and track the popular events or topics that evolve over time [52]–[54].”
Few-Shot Named Entity Recognition: A Comprehensive Study (2020, background): “NER serves as an important ﬁrst component for tasks such as information extraction (Ritter et al., 2012), information retrieval (Guo et al., 2009), question answering (Mollá et al., 2006), task-oriented dialogues (Peng et al., 2020a; Gao et al., 2019) and other language understanding applications…”
Semantics-driven Event Clustering in Twitter Feeds (2015, background): “Semantic information is often extracted after the events are detected to classify them into high level categories [16].”
Social media analytics - Challenges in topic discovery, data collection, and data preparation (2018, methodology): “However, three of the publications would be considered relevant according to the criteria used earlier (Petrovi ć et al., 2010; Ritter et al., 2012; Weng et al., 2011).”
Open Event Extraction from Online Text using a Generative Adversarial Network (2019, methodology): “ value for each event, Benson et al. (2011) proposed an approach based on a structured graphical model. By representing an event with a binary tuple which is constituted by a named entity and a date, Ritter et al. (2012) employed some statistic to measure the strength of associations between a named”
Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer (2020, background): “…of many information extraction tasks including entity linking (Derczynski et al., 2015), opinion mining (Maynard et al., 2012), and event detection (Ritter et al., 2012), named entity recognition (NER) has attracted much attention in the research community in the past two decades (Li et al., 2018).”
Humpty Dumpty: Controlling Word Meanings via Corpus Poisoning (2020, background): “For example, NER for tweets [45, 47, 59] can detect events or trends [44, 60].”