Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity.
We first obtain the initial set of event pairs that are likely to have the subevent relation, by exploiting two observations that 1) subevents are temporally contained by the parent event, and 2) the definitions of the parent event can be used to further guide the identification of subevents.
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.
We propose to leverage lexical paraphrases and high precision rules informed by news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles.
Event information is usually scattered across multiple sentences within a document.
We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
People increasingly use social media to report emergencies, seek help or share information during disasters, which makes social networks an important tool for disaster management.
Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event.
Most supervised word sense disambiguation (WSD) systems build word-specific classifiers by leveraging labeled data.
We argue that external commonsense knowledge and linguistic constraints need to be incorporated into neural network models for mitigating data sparsity issues and further improving the performance of discourse parsing.
The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection.
We aim to comprehensively identify all the event causal relations in a document, both within a sentence and across sentences, which is important for reconstructing pivotal event structures.
Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance.
Capabilities to categorize a clause based on the type of situation entity (e. g., events, states and generic statements) the clause introduces to the discourse can benefit many NLP applications.
This paper proposes a novel approach for event coreference resolution that models correlations between event coreference chains and document topical structures through an Integer Linear Programming formulation.
Identifying the most dominant and central event of a document, which governs and connects other foreground and background events in the document, is useful for many applications, such as text summarization, storyline generation and text segmentation.
Inspired by the double temporality characteristic of narrative texts, we propose a novel approach for acquiring rich temporal "before/after" event knowledge across sentences in narrative stories.
We argue that semantic meanings of a sentence or clause can not be interpreted independently from the rest of a paragraph, or independently from all discourse relations and the overall paragraph-level discourse structure.
Our simple system designed using minimal features achieved the micro-average F1 scores of 57. 72, 44. 27 and 42. 47 for event span detection, type identification and realis status classification tasks respectively.
In the wake of a polarizing election, social media is laden with hateful content.
Focusing on the task of identifying event temporal status, we find that events directly or indirectly governing the target event in a dependency tree are most important contexts.
Capabilities of detecting temporal relations between two events can benefit many applications.
We introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event chains.
Aiming to resolve high complexities of event descriptions, previous work (Huang and Riloff, 2013) proposes multi-faceted event recognition and a bootstrapping method to automatically acquire both event facet phrases and event expressions from unannotated texts.
Attorneys, judges, and others in the justice system are constantly surrounded by large amounts of legal text, which can be difficult to manage across many cases.
Accurate event detection in social media is very challenging because user generated contents are extremely noisy and sparse in content.