Search Results for author:
Found 54 papers, 18 papers with code
Detecting Online Hate Speech Using Context Aware Models
In the wake of a polarizing election, the cyber world is laden with hate speech.
HYTREL: Hypergraph-enhanced Tabular Data Representation Learning
Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks.
TAMU at KBP 2017: Event Nugget Detection and Coreference Resolution
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.
PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge
We present a new benchmark dataset called PARADE for paraphrase identification that requires specialized domain knowledge.
In Plain Sight: Media Bias Through the Lens of Factual Reporting
The increasing prevalence of political bias in news media calls for greater public awareness of it, as well as robust methods for its detection.
Composition-contrastive Learning for Sentence Embeddings
Vector representations of natural language are ubiquitous in search applications.
Weakly-supervised Fine-grained Event Recognition on Social Media Texts for Disaster Management
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.
Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Named entity recognition (NER) is well studied for the general domain, and recent systems have achieved human-level performance for identifying common entity types.
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition
General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city.
Building Context-aware Clause Representations for Situation Entity Type Classification
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.
Modeling Document-level Temporal Structures for Building Temporal Dependency Graphs
We propose to leverage news discourse profiling to model document-level temporal structures for building temporal dependency graphs.
Identifying Conspiracy Theories News based on Event Relation Graph
Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner.
Temporal Event Knowledge Acquisition via Identifying Narratives
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.
Recognizing Explicit and Implicit Hate Speech Using a Weakly Supervised Two-path Bootstrapping Approach
In the wake of a polarizing election, social media is laden with hateful content.
Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
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.
General Classification
Implicit Discourse Relation Classification
+2
Using Context Events in Neural Network Models for Event Temporal Status Identification
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.
A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Capabilities of detecting temporal relations between two events can benefit many applications.
Online Deception Detection Refueled by Real World Data Collection
The lack of large realistic datasets presents a bottleneck in online deception detection studies.
Event Coreference Resolution by Iteratively Unfolding Inter-dependencies among Events
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.
A Sequential Model for Classifying Temporal Relations between Intra-Sentence Events
We present a sequential model for temporal relation classification between intra-sentence events.
Improving Event Coreference Resolution by Modeling Correlations between Event Coreference Chains and Document Topic Structures
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 Event in a News Article by Mining Event Coreference Relations
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.
Fine-grained Structure-based News Genre Categorization
Journalists usually organize and present the contents of a news article following a well-defined structure.
Towards Accurate Event Detection in Social Media: A Weakly Supervised Approach for Learning Implicit Event Indicators
Accurate event detection in social media is very challenging because user generated contents are extremely noisy and sparse in content.
Learning Event Expressions via Bilingual Structure Projection
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.
CaseSummarizer: A System for Automated Summarization of Legal 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.
Improving Dialogue State Tracking by Discerning the Relevant Context
Specifically, we use the current user utterance and the most recent system utterance to determine the relevance of a system utterance.
Modeling Document-level Causal Structures for Event Causal Relation Identification
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.
A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
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.
One Classifier for All Ambiguous Words: Overcoming Data Sparsity by Utilizing Sense Correlations Across Words
Most supervised word sense disambiguation (WSD) systems build word-specific classifiers by leveraging labeled data.
Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event
Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event.
Ranked #5 on
Text Classification
on NewsDiscourse
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.
Automatic Data Acquisition for Event Coreference Resolution
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.
Reconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document.
Profiling News Discourse Structure Using Explicit Subtopic Structures Guided Critics
We present an actor-critic framework to induce subtopical structures in a news article for news discourse profiling.
Ranked #4 on
Text Classification
on NewsDiscourse
Predicting Sentence Deletions for Text Simplification Using a Functional Discourse Structure
Document-level text simplification often deletes some sentences besides performing lexical, grammatical or structural simplification to reduce text complexity.
Less is More: A Lightweight and Robust Neural Architecture for Discourse Parsing
Complex feature extractors are widely employed for text representation building.
RST-style Discourse Parsing Guided by Document-level Content Structures
Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how clauses, sentences, and large text spans compose a whole discourse and presents the rhetorical structure as a hierarchical tree.
Semi-supervised News Discourse Profiling with Contrastive Learning
News Discourse Profiling seeks to scrutinize the event-related role of each sentence in a news article and has been proven useful across various downstream applications.
All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison
Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets.
Discourse Structures Guided Fine-grained Propaganda Identification
Hence, we propose to incorporate both local and global discourse structures for propaganda discovery and construct two teacher models for identifying PDTB-style discourse relations between nearby sentences and common discourse roles of sentences in a news article respectively.
Hierarchical Fusion for Online Multimodal Dialog Act Classification
We propose a framework for online multimodal dialog act (DA) classification based on raw audio and ASR-generated transcriptions of current and past utterances.
Ranked #1 on
Dialogue Act Classification
on EMOTyDA
Sentence-level Media Bias Analysis with Event Relation Graph
Then, we incorporate event relation graph for bias sentences identification in two steps: an event-aware language model is built to inject the events and event relations knowledge into the basic language model via soft labels; further, a relation-aware graph attention network is designed to update sentence embedding with events and event relations information based on hard labels.
EMONA: Event-level Moral Opinions in News Articles
This paper initiates a new task to understand moral opinions towards events in news articles.
Polarity Calibration for Opinion Summarization
To address this issue and make the summarizer express both sides of opinions, we introduce the concept of polarity calibration, which aims to align the polarity of output summary with that of input text.
Indexing Analytics to Instances: How Integrating a Dashboard can Support Design Education
With the goal of making the analytics intelligible to instructors, we developed a research artifact integrating a design analytics dashboard with design instances, and the design environment that students use to create them.
