Semantic Role Labeling

131 papers with code • 7 benchmarks • 13 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.

Large-Scale QA-SRL Parsing

uwnlp/qasrl-bank ACL 2018

We present a new large-scale corpus of Question-Answer driven Semantic Role Labeling (QA-SRL) annotations, and the first high-quality QA-SRL parser.

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.

Natural Language Processing (almost) from Scratch

faramarzmunshi/d2l-nlp 2 Mar 2011

We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including: part-of-speech tagging, chunking, named entity recognition, and semantic role labeling.