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Found 35 papers, 9 papers with code
Concept Wikification for COVID-19
In this paper, we address the problem of concept wikification for COVID-19, which is to automatically recognize mentions of concepts related to COVID-19 in text and resolve them into Wikipedia titles.
Improving Cross-Lingual Sentiment Analysis via Conditional Language Adversarial Nets
Sentiment analysis has come a long way for high-resource languages due to the availability of large annotated corpora.
Joint Extraction of Entities, Relations, and Events via Modeling Inter-Instance and Inter-Label Dependencies
However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance.
Modal Dependency Parsing via Language Model Priming
The task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text.
Exploring Pre-Trained Transformers and Bilingual Transfer Learning for Arabic Coreference Resolution
In this paper, we develop bilingual transfer learning approaches to improve Arabic coreference resolution by leveraging additional English annotation via bilingual or multilingual pre-trained transformers.
Weakly Supervised Subevent Knowledge Acquisition
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.
Annotating Temporal Dependency Graphs via Crowdsourcing
We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text.
Modeling Document-Level Context for Event Detection via Important Context Selection
To address this issue, we propose a novel method to model document-level context for ED that dynamically selects relevant sentences in the document for the event prediction of the target sentence.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction
An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended.
Document-Level Event Argument Extraction via Optimal Transport
Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger.
Crosslingual Transfer Learning for Relation and Event Extraction via Word Category and Class Alignments
To address this issue, we propose a novel crosslingual alignment method that leverages class information of REE tasks for representation learning.
From Instructions to Constraints: Language Model Alignment with Automatic Constraint Verification
We investigate common constraints in NLP tasks, categorize them into three classes based on the types of their arguments, and propose a unified framework, ACT (Aligning to ConsTraints), to automatically produce supervision signals for user alignment with constraints.
A Multi-Modal Multilingual Benchmark for Document Image Classification
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents such as forms, emails, and other such documents.
Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning
We present a novel approach for structured data-to-text generation that addresses the limitations of existing methods that primarily focus on specific types of structured data.
Textual Entailment for Event Argument Extraction: Zero- and Few-Shot with Multi-Source Learning
In this work we show that entailment is also effective in Event Argument Extraction (EAE), reducing the need of manual annotation to 50% and 20% in ACE and WikiEvents respectively, while achieving the same performance as with full training.
Ranked #1 on
Event Argument Extraction
on WikiEvents
ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations
The current workflow for Information Extraction (IE) analysts involves the definition of the entities/relations of interest and a training corpus with annotated examples.
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction
FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration.
Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey
Large, pre-trained transformer-based language models such as BERT have drastically changed the Natural Language Processing (NLP) field.
Factuality Assessment as Modal Dependency Parsing
In this paper, we frame factuality assessment as a modal dependency parsing task that identifies the events and their sources, formally known as conceivers, and then determine the level of certainty that the sources are asserting with respect to the events.
ExcavatorCovid: Extracting Events and Relations from Text Corpora for Temporal and Causal Analysis for COVID-19
Timely responses from policy makers to mitigate the impact of the COVID-19 pandemic rely on a comprehensive grasp of events, their causes, and their impacts.
Towards Few-Shot Event Mention Retrieval: An Evaluation Framework and A Siamese Network Approach
Previous approaches treat event extraction as {``}one size fits all{''} with an ontology defined a priori.
Exploring Contextualized Neural Language Models for Temporal Dependency Parsing
Extracting temporal relations between events and time expressions has many applications such as constructing event timelines and time-related question answering.
Measure Country-Level Socio-Economic Indicators with Streaming News: An Empirical Study
Socio-economic conditions are difficult to measure.
Towards Machine Reading for Interventions from Humanitarian-Assistance Program Literature
Solving long-lasting problems such as food insecurity requires a comprehensive understanding of interventions applied by governments and international humanitarian assistance organizations, and their results and consequences.
A Case Study on Learning a Unified Encoder of Relations
Typical relation extraction models are trained on a single corpus annotated with a pre-defined relation schema.
Rapid Customization for Event Extraction
We present a system for rapidly customizing event extraction capability to find new event types and their arguments.
Learning Transferable Representation for Bilingual Relation Extraction via Convolutional Neural Networks
The learnt representation is discriminative and transferable between languages.
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier.
Probabilistic Inference for Cold Start Knowledge Base Population with Prior World Knowledge
Building knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search.
Challenges in the Knowledge Base Population Slot Filling Task
The Knowledge Based Population (KBP) evaluation track of the Text Analysis Conferences (TAC) has been held for the past 3 years.
