Semantic Role Labeling

118 papers with code • 4 benchmarks • 9 datasets

Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". BIO notation is typically used for semantic role labeling.

Example:

Housing starts are expected to quicken a bit from August’s pace
B-ARG1 I-ARG1 O O O V B-ARG2 I-ARG2 B-ARG3 I-ARG3 I-ARG3

Most implemented papers

Deep contextualized word representations

flairNLP/flair NAACL 2018

We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e. g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i. e., to model polysemy).

The Natural Language Decathlon: Multitask Learning as Question Answering

salesforce/decaNLP ICLR 2019

Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting.

An Incremental Parser for Abstract Meaning Representation

mdtux89/amr-evaluation EACL 2017

We describe a transition-based parser for AMR that parses sentences left-to-right, in linear time.

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

uclanlp/reducingbias EMNLP 2017

Language is increasingly being used to define rich visual recognition problems with supporting image collections sourced from the web.

LINSPECTOR: Multilingual Probing Tasks for Word Representations

UKPLab/linspector CL 2020

We present a reusable methodology for creation and evaluation of such tests in a multilingual setting.

Simple BERT Models for Relation Extraction and Semantic Role Labeling

Impavidity/relogic 10 Apr 2019

We present simple BERT-based models for relation extraction and semantic role labeling.

Generalizing Natural Language Analysis through Span-relation Representations

jzbjyb/SpanRel ACL 2020

Natural language processing covers a wide variety of tasks predicting syntax, semantics, and information content, and usually each type of output is generated with specially designed architectures.

A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling

diegma/neural-dep-srl CONLL 2017

However, when automatically predicted part-of-speech tags are provided as input, it substantially outperforms all previous local models and approaches the best reported results on the English CoNLL-2009 dataset.

Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling

diegma/neural-dep-srl EMNLP 2017

GCNs over syntactic dependency trees are used as sentence encoders, producing latent feature representations of words in a sentence.