Search Results for author:
Found 22 papers, 8 papers with code
QA4IE: A Quality Assurance Tool for Information Extraction
In this paper we contribute QA4IE, a comprehensive QA tool for information extraction, which can (1) detect potential problems in text annotations in a timely manner, (2) accurately assess the quality of annotations, (3) visually display and summarize annotation discrepancies among annotation team members, (4) provide a comprehensive statistics report, and (5) support viewing of annotated documents interactively.
PerKGQA: Question Answering over Personalized Knowledge Graphs
The above concerns motivate our question answer- ing setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG.
Agent-Based Dynamic Collaboration Support in a Smart Office Space
For the past 15 years, in computer-supported collaborative learning applications, conversational agents have been used to structure group interactions in online chat-based environments.
Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction
Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval.
Multi-Scale Contrastive Co-Training for Event Temporal Relation Extraction
Our model uses a BERT-based language model to encode local context and a Graph Neural Network (GNN) to represent global document-level syntactic and temporal characteristics.
Adapting to the Long Tail: A Meta-Analysis of Transfer Learning Research for Language Understanding Tasks
We reflect on the question: have transfer learning methods sufficiently addressed the poor performance of benchmark-trained models on the long tail?
STAGE: Tool for Automated Extraction of Semantic Time Cues to Enrich Neural Temporal Ordering Models
We demonstrate the utility of extracted cues by integrating them with an event ordering model using a joint BiLSTM and ILP constraint architecture.
Evaluating the Impact of a Hierarchical Discourse Representation on Entity Coreference Resolution Performance
Recent work on entity coreference resolution (CR) follows current trends in Deep Learning applied to embeddings and relatively simple task-related features.
What Sounds ``Right'' to Me? Experiential Factors in the Perception of Political Ideology
In this paper, we challenge the assumption that political ideology is inherently built into text by presenting an investigation into the impact of experiential factors on annotator perceptions of political ideology.
Using Type Information to Improve Entity Coreference Resolution
Coreference resolution (CR) is an essential part of discourse analysis.
MedFilter: Improving Extraction of Task-relevant Utterances from Doctor-Patient Conversations through Integration of Discourse Structure and Ontological Knowledge
In this paper, we propose the novel modeling approach MedFilter, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task.
Adapting Event Extractors to Medical Data: Bridging the Covariate Shift
Our best-performing models reach F1 scores of 70. 0 and 72. 9 on notes and conversations respectively, using no labeled data from target domains.
Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets
To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.
TDDiscourse: A Dataset for Discourse-Level Temporal Ordering of Events
Prior work on temporal relation classification has focused extensively on event pairs in the same or adjacent sentences (local), paying scant attention to discourse-level (global) pairs.
Using Functional Schemas to Understand Social Media Narratives
We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures.
The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit's Quarantine Policy
Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies.
Deep Neural Model Inspection and Comparison via Functional Neuron Pathways
We introduce a general method for the interpretation and comparison of neural models.
Exploring Numeracy in Word Embeddings
In this work, we show that existing embedding models are inadequate at constructing representations that capture salient aspects of mathematical meaning for numbers, which is important for language understanding.
EQUATE: A Benchmark Evaluation Framework for Quantitative Reasoning in Natural Language Inference
Quantitative reasoning is a higher-order reasoning skill that any intelligent natural language understanding system can reasonably be expected to handle.
Stress Test Evaluation for Natural Language Inference
Natural language inference (NLI) is the task of determining if a natural language hypothesis can be inferred from a given premise in a justifiable manner.
Extracting Personal Medical Events for User Timeline Construction using Minimal Supervision
In this paper, we describe a system for automatic construction of user disease progression timelines from their posts in online support groups using minimal supervision.
