Semantic Role Labeling

99 papers with code • 2 benchmarks • 6 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

Greatest papers with code

Deep contextualized word representations

zalandoresearch/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).

Ranked #2 on Citation Intent Classification on ACL-ARC (using extra training data)

Citation Intent Classification Conversational Response Selection +7

AllenNLP: A Deep Semantic Natural Language Processing Platform

allenai/allennlp WS 2018

This paper describes AllenNLP, a platform for research on deep learning methods in natural language understanding.

Language understanding Natural Language Understanding +2

N-LTP: An Open-source Neural Language Technology Platform for Chinese

HIT-SCIR/ltp EMNLP (ACL) 2021

We introduce \texttt{N-LTP}, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: {lexical analysis} (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling).

Chinese Word Segmentation Dependency Parsing +6

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.

Domain Adaptation Machine Translation +10

Glyce: Glyph-vectors for Chinese Character Representations

ShannonAI/glyce NeurIPS 2019

However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.

Chinese Dependency Parsing Chinese Named Entity Recognition +19

Deep Semantic Role Labeling: What Works and What's Next

luheng/deep_srl ACL 2017

We introduce a new deep learning model for semantic role labeling (SRL) that significantly improves the state of the art, along with detailed analyses to reveal its strengths and limitations.

Predicate Detection

Deep Semantic Role Labeling with Self-Attention

XMUNLP/Tagger 5 Dec 2017

Semantic Role Labeling (SRL) is believed to be a crucial step towards natural language understanding and has been widely studied.

Language understanding Natural Language Understanding +1