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Found 20 papers, 13 papers with code
SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework
In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause.
Nibbling at the Hard Core of Word Sense Disambiguation
With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models.
Probing for Predicate Argument Structures in Pretrained Language Models
Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL).
UniteD-SRL: A Unified Dataset for Span- and Dependency-Based Multilingual and Cross-Lingual Semantic Role Labeling
Multilingual and cross-lingual Semantic Role Labeling (SRL) have recently garnered increasing attention as multilingual text representation techniques have become more effective and widely available.
Named Entity Recognition for Entity Linking: What Works and What’s Next
Entity Linking (EL) systems have achieved impressive results on standard benchmarks mainly thanks to the contextualized representations provided by recent pretrained language models.
Ranked #4 on
Entity Disambiguation
on ACE2004
AMuSE-WSD: An All-in-one Multilingual System for Easy Word Sense Disambiguation
Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest: recently proposed systems have shown the remarkable effectiveness of deep learning techniques in this task, especially when aided by modern pretrained language models.
InVeRo-XL: Making Cross-Lingual Semantic Role Labeling Accessible with Intelligible Verbs and Roles
Notwithstanding the growing interest in cross-lingual techniques for Natural Language Processing, there has been a surprisingly small number of efforts aimed at the development of easy-to-use tools for cross-lingual Semantic Role Labeling.
Universal Semantic Annotator: the First Unified API for WSD, SRL and Semantic Parsing
In this paper, we present the Universal Semantic Annotator (USeA), which offers the first unified API for high-quality automatic annotations of texts in 100 languages through state-of-the-art systems for Word Sense Disambiguation, Semantic Role Labeling and Semantic Parsing.
SemEval-2022 Task 9: R2VQ – Competence-based Multimodal Question Answering
In this task, we identify a challenge that is reflective of linguistic and cognitive competencies that humans have when speaking and reasoning.
Increasing Coverage and Precision of Textual Information in Multilingual Knowledge Graphs
Recent work in Natural Language Processing and Computer Vision has been using textual information -- e. g., entity names and descriptions -- available in knowledge graphs to ground neural models to high-quality structured data.
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities
Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs.
Echoes from Alexandria: A Large Resource for Multilingual Book Summarization
In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features.
Ranked #1 on
Text Summarization
on BookSum
Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures
One of the common traits of past and present approaches for Semantic Role Labeling (SRL) is that they rely upon discrete labels drawn from a predefined linguistic inventory to classify predicate senses and their arguments.
Entity Disambiguation with Entity Definitions
Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions.
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources
While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from PropBank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia.
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration
Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context.
Conception: Multilingually-Enhanced, Human-Readable Concept Vector Representations
To date, the most successful word, word sense, and concept modelling techniques have used large corpora and knowledge resources to produce dense vector representations that capture semantic similarities in a relatively low-dimensional space.
Bridging the Gap in Multilingual Semantic Role Labeling: a Language-Agnostic Approach
Recent research indicates that taking advantage of complex syntactic features leads to favorable results in Semantic Role Labeling.
InVeRo: Making Semantic Role Labeling Accessible with Intelligible Verbs and Roles
Semantic Role Labeling (SRL) is deeply dependent on complex linguistic resources and sophisticated neural models, which makes the task difficult to approach for non-experts.
VerbAtlas: a Novel Large-Scale Verbal Semantic Resource and Its Application to Semantic Role Labeling
We present VerbAtlas, a new, hand-crafted lexical-semantic resource whose goal is to bring together all verbal synsets from WordNet into semantically-coherent frames.
