Search Results for author:
Found 35 papers, 22 papers with code
User-Aware Multilingual Abusive Content Detection in Social Media
Our observation indicates that a post's tendency to attract abusive comments, as well as features such as user history and social context, significantly aid in the detection of abusive content.
FinQAPT: Empowering Financial Decisions with End-to-End LLM-driven Question Answering Pipeline
However, at the pipeline level, we observed decreased performance due to challenges in extracting relevant context from financial reports.
Soft Measures for Extracting Causal Collective Intelligence
This study highlights the need for soft similarity measures tailored to FCM extraction, advancing collective intelligence modeling with NLP.
MASSFormer: Mobility-Aware Spectrum Sensing using Transformer-Driven Tiered Structure
The proposed method first computes tokens from the sequence of covariance matrices (CMs) for each SU and processes them in parallel using the SUtransformer network to learn the spatio-temporal features at SUlevel.
Knowledge-Driven Cross-Document Relation Extraction
Relation extraction (RE) is a well-known NLP application often treated as a sentence- or document-level task.
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs
Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra.
Revisiting Document-Level Relation Extraction with Context-Guided Link Prediction
Document-level relation extraction (DocRE) poses the challenge of identifying relationships between entities within a document as opposed to the traditional RE setting where a single sentence is input.
ReOnto: A Neuro-Symbolic Approach for Biomedical Relation Extraction
ReOnto employs a graph neural network to acquire the sentence representation and leverages publicly accessible ontologies as prior knowledge to identify the sentential relation between two entities.
Can Persistent Homology provide an efficient alternative for Evaluation of Knowledge Graph Completion Methods?
$\mathcal{KP}$ addresses this by representing the topology of the KG completion methods through the lens of topological data analysis, concretely using persistent homology.
Learnable Spectral Wavelets on Dynamic Graphs to Capture Global Interactions
Since static methods to learn the graph spectrum would not consider the history of the evolution of the spectrum as the graph evolves with time, we propose a novel approach to learn the graph wavelets to capture this evolving spectra.
Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs
The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG.
An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs
We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers.
Plumber: A Modular Framework to Create Information Extraction Pipelines
Information Extraction (IE) tasks are commonly studied topics in various domains of research.
How Expressive are Transformers in Spectral Domain for Graphs?
We further provide a theoretical analysis and prove that the spatial attention mechanism in the transformer cannot effectively capture the desired frequency response, thus, inherently limiting its expressiveness in spectral space.
Triple Classification for Scholarly Knowledge Graph Completion
Scholarly Knowledge Graphs (KGs) provide a rich source of structured information representing knowledge encoded in scientific publications.
SpecTRA: Spectral Transformer for Graph Representation Learning
Transformers have recently been applied in the more generic domain of graphs.
HopfE: Knowledge Graph Representation Learning using Inverse Hopf Fibrations
A few KGE techniques address interpretability, i. e., mapping the connectivity patterns of the relations (i. e., symmetric/asymmetric, inverse, and composition) to a geometric interpretation such as rotations.
VOGUE: Answer Verbalization through Multi-Task Learning
The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm.
KGPool: Dynamic Knowledge Graph Context Selection for Relation Extraction
We present a novel method for relation extraction (RE) from a single sentence, mapping the sentence and two given entities to a canonical fact in a knowledge graph (KG).
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks).
Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs
In this work, we propose a novel framework named CARTON, which performs multi-task semantic parsing for handling the problem of conversational question answering over a large-scale knowledge graph.
ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation
This paper presents ParaQA, a question answering (QA) dataset with multiple paraphrased responses for single-turn conversation over knowledge graphs (KG).
Better Call the Plumber: Orchestrating Dynamic Information Extraction Pipelines
In the last decade, a large number of Knowledge Graph (KG) information extraction approaches were proposed.
CHOLAN: A Modular Approach for Neural Entity Linking on Wikipedia and Wikidata
Our empirical study was conducted on two well-known knowledge bases (i. e., Wikidata and Wikipedia).
Ranked #1 on
Entity Linking
on MSNBC
QA2Explanation: Generating and Evaluating Explanations for Question Answering Systems over Knowledge Graph
In the era of Big Knowledge Graphs, Question Answering (QA) systems have reached a milestone in their performance and feasibility.
RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network
In this paper, we present a novel method named RECON, that automatically identifies relations in a sentence (sentential relation extraction) and aligns to a knowledge graph (KG).
Uncovering the Corona Virus Map Using Deep Entities and Relationship Models
We extract entities and relationships related to COVID-19 from a corpus of articles related to Corona virus by employing a novel entities and relationship model.
Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models
We further hypothesize that our proposed KG context can be standardized for Wikipedia, and we evaluate the impact of KG context on state-of-the-art NED model for the Wikipedia knowledge base.
Ranked #2 on
Entity Disambiguation
on AIDA-CoNLL
Template-Based Question Answering over Linked Geospatial Data
We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions.
Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from the text, and possibly linking them to proper matches in Knowledge Graphs (KGs).
Encoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking
In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata.
Towards Optimisation of Collaborative Question Answering over Knowledge Graphs
Collaborative Question Answering (CQA) frameworks for knowledge graphs aim at integrating existing question answering (QA) components for implementing sequences of QA tasks (i. e. QA pipelines).
Old is Gold: Linguistic Driven Approach for Entity and Relation Linking of Short Text
Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e. g. wrt.
No One is Perfect: Analysing the Performance of Question Answering Components over the DBpedia Knowledge Graph
Question answering (QA) over knowledge graphs has gained significant momentum over the past five years due to the increasing availability of large knowledge graphs and the rising importance of question answering for user interaction.
Robust Object Tracking with Crow Search Optimized Multi-cue Particle Filter
Particle Filter(PF) is used extensively for estimation of target Non-linear and Non-gaussian state.
