The stages of event extraction

Ahn, David

doi:10.3115/1629235.1629236

Cited by 388 publications

(236 citation statements)

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“…The most similar work to that describe in this paper is detailed in (Ahn, 2006), who treats the task of finding all event triggers (used to identify each event) as a word classification task where the task is to classify every term in a document with a label defined by 34 classes. Features used included various lexical, WordNet, dependency and related entity features.…”

Section: Background and Related Workmentioning

confidence: 99%

Investigating statistical techniques for sentence-level event classification

Naughton

Stokes

Carthy

2008

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08

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The ability to correctly classify sentences that describe events is an important task for many natural language applications such as Question Answering (QA) and Summarisation. In this paper, we treat event detection as a sentence level text classification problem. We compare the performance of two approaches to this task: a Support Vector Machine (SVM) classifier and a Language Modeling (LM) approach. We also investigate a rule based method that uses hand crafted lists of terms derived from WordNet. These terms are strongly associated with a given event type, and can be used to identify sentences describing instances of that type. We use two datasets in our experiments, and evaluate each technique on six distinct event types. Our results indicate that the SVM consistently outperform the LM technique for this task. More interestingly, we discover that the manual rule based classification system is a very powerful baseline that outperforms the SVM on three of the six event types.

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Section: Background and Related Workmentioning

confidence: 99%

Investigating statistical techniques for sentence-level event classification

Naughton

Stokes

Carthy

2008

Proceedings of the 22nd International Conference on Computational Linguistics - COLING '08

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“…Grishman et al (2005), Ahn (2006), Lu and Roth (2012), Li et al (2013) and Li et al (2014). Only few approaches, like Ji and Grishman (2008) and Liao and Grishman (2010) go beyond sentences and even beyond documents in order to exploit richer context for the extraction of events.…”

Section: Related Workmentioning

confidence: 99%

Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics

2015

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will take place in Denver on June 4 and 5 and is colocated with SemEval and NAACL. As in 2014 at COLING, also on this occasion *SEM and SemEval chose to coordinate their programs by featuring a joint invited talk. In this way, *SEM aims to bring together the ACL SIGLEX and ACL SIGSEM communities.The acceptance rate of *SEM 2015 was quite competitive: out of 98 submissions, we accepted 36 papers for an overall acceptance of 37%. The acceptance rate of long paper that were accepted for oral presentation (18 out of 62) is 29%. The papers cover a wide range of topics including distributional semantics; lexical semantics and lexical acquisition; formal and linguistic semantics; discourse semantics; lexical resources, linked data and ontologies; semantics for applications; and extra-propositional semantics: sentiment and figurative meaning.The *SEM 2015 program consists of oral presentations for selected long papers and a poster session for long and short papers.Day One, June 4th:• Joint *SEM SemEval keynote talk by Marco Baroni;• Oral presentation sessions on distributional semantics, lexical semantics, and extra-propositional semantics;• Poster session.Day Two, June 5th:• Keynote talk by Preslav Natkov;• Oral presentation sessions on semantics for applications, lexical resources and ontologies, formal semantics, and discourse semantics;• *SEM Best Paper Award.We cannot finish without saying that *SEM 2015 would not have been possible without the considerable efforts of our area chairs, their reviewers, and the computational semantics community in general.We hope you will enjoy *SEM 2015, Distributional semantic methods have some a priori appeal as models of human meaning acquisition, because they induce word representations from contextual distributions naturally occurring in corpus data without need for supervision. However, learning the meaning of a (concrete) word also involves establishing a link between the word and its typical visual referents, which is beyond the scope of classic, text-based distributional semantics. Since recently several proposals have been put forward about how to induce multimodal word representations from linguistic and visual contexts, it is natural to ask if this line of work, besides its practical implications, can help us to develop more realistic, grounded models of human word learning within the distributional semantics framework.In my talk, I will report about two studies in which we used multimodal distributional semantics (MDS) to simulate human word learning. In one study, we first measured the ability of subjects to link a nonce word to relevant linguistic and visual associates when prompted only by exposure to minimal corpus evidence about it. We then simulated the same task with an MDS model, finding its behavior remarkably similar to that of subjects. In the second study, we constructed a corpus in which child-directed speech is aligned with real-life pictures of the objects mentioned by care-givers. We then trained our MDS model on these data, and inspected the generaliza...

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“…Earlier work on event coreference resolution [14] identified corefering sentences based on semantic score and attribute score after semantic role labeling which was understood as annotating participants of the event. Later David Ahn [8] treated the problem as a classification problem with rich set of features. Zhen Chen et al [6] proposed an agglomerative clustering approach to solve the event coreference resolution problem by considering the features based on event participants and attributes.…”

Section: Introductionmentioning

confidence: 99%