About

Entity Typing is an important task in text analysis. Assigning types (e.g., person, location, organization) to mentions of entities in documents enables effective structured analysis of unstructured text corpora. The extracted type information can be used in a wide range of ways (e.g., serving as primitives for information extraction and knowledge base (KB) completion, and assisting question answering). Traditional Entity Typing systems focus on a small set of coarse types (typically fewer than 10). Recent studies work on a much larger set of fine-grained types which form a tree-structured hierarchy (e.g., actor as a subtype of artist, and artist is a subtype of person).

Source: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Image Credit: Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

Benchmarks

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Datasets

Greatest papers with code

ERNIE: Enhanced Language Representation with Informative Entities

ACL 2019 thunlp/ERNIE

Neural language representation models such as BERT pre-trained on large-scale corpora can well capture rich semantic patterns from plain text, and be fine-tuned to consistently improve the performance of various NLP tasks.

ENTITY LINKING ENTITY TYPING KNOWLEDGE GRAPHS LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION SENTIMENT ANALYSIS

BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages

LREC 2018 bheinzerling/bpemb

We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE).

ENTITY TYPING TOKENIZATION WORD EMBEDDINGS

Representation Learning of Entities and Documents from Knowledge Base Descriptions

COLING 2018 wikipedia2vec/wikipedia2vec

In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB).

ENTITY TYPING REPRESENTATION LEARNING TEXT CLASSIFICATION

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

5 Feb 2020studio-ousia/luke

We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa.

DEPENDENCY PARSING ENTITY TYPING QUESTION ANSWERING RELATION CLASSIFICATION

Knowledge Enhanced Contextual Word Representations

IJCNLP 2019 allenai/kb

Contextual word representations, typically trained on unstructured, unlabeled text, do not contain any explicit grounding to real world entities and are often unable to remember facts about those entities.

Ranked #6 on Relation Extraction on TACRED (using extra training data)

ENTITY LINKING ENTITY TYPING LANGUAGE MODELLING RELATION EXTRACTION WORD SENSE DISAMBIGUATION

Hierarchical Losses and New Resources for Fine-grained Entity Typing and Linking

ACL 2018 chanzuckerberg/MedMentions

Extraction from raw text to a knowledge base of entities and fine-grained types is often cast as prediction into a flat set of entity and type labels, neglecting the rich hierarchies over types and entities contained in curated ontologies.

ENTITY LINKING ENTITY TYPING HIERARCHICAL STRUCTURE

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

23 Jan 2019roomylee/entity-aware-relation-classification

Our model not only utilizes entities and their latent types as features effectively but also is more interpretable by visualizing attention mechanisms applied to our model and results of LET.

ENTITY TYPING RELATION CLASSIFICATION

Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing

NAACL 2019 xwhan/Extremely-Fine-Grained-Entity-Typing

Existing entity typing systems usually exploit the type hierarchy provided by knowledge base (KB) schema to model label correlations and thus improve the overall performance.

ENTITY TYPING

Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding

17 Feb 2016shanzhenren/PLE

Current systems of fine-grained entity typing use distant supervision in conjunction with existing knowledge bases to assign categories (type labels) to entity mentions.

ENTITY TYPING SEMANTIC SIMILARITY SEMANTIC TEXTUAL SIMILARITY