# Lexicon Infused Phrase Embeddings for Named Entity Resolution

arXiv: https://arxiv.org/abs/1404.5367 · HTML: https://mlsanity.com/paper/1404.5367/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2014.

## Built on by (most cited first, 16 of 16)

- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Query and Extract: Refining Event Extraction as Type-oriented Binary Decoding](https://mlsanity.com/paper/2110.07476/) (2021)
- [Distantly Supervised Named Entity Recognition via Confidence-Based Multi-Class Positive and Unlabeled Learning](https://mlsanity.com/paper/2204.09589/) (2022)
- [A Privacy-Preserving Approach to Extraction of Personal Information through Automatic Annotation and Federated Learning](https://mlsanity.com/paper/2105.09198/) (2021)
- [Recent Trends in Named Entity Recognition (NER)](https://mlsanity.com/paper/2101.11420/) (2021)
- [Deanthropomorphising NLP: Can a Language Model Be Conscious?](https://mlsanity.com/paper/2211.11483/) (2022)
- [ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer](https://mlsanity.com/paper/2207.00785/) (2022)
- [Deep Learning Based Named Entity Recognition Models for Recipes](https://mlsanity.com/paper/2402.17447/) (2024)
- [#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs](https://mlsanity.com/paper/2108.03670/) (2021)
- [Multi-Granularity Cross-Modality Representation Learning for Named Entity Recognition on Social Media](https://mlsanity.com/paper/2210.14163/) (2022)
- [Plug-Tagger: A Pluggable Sequence Labeling Framework Using Language Models](https://mlsanity.com/paper/2110.07331/) (2021)
- [AlbNER: A Corpus for Named Entity Recognition in Albanian](https://mlsanity.com/paper/2309.08741/) (2023)
- [Computational Approaches to Arabic-English Code-Switching](https://mlsanity.com/paper/2410.13318/) (2024)
- [DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants](https://mlsanity.com/paper/2108.06633/) (2021)
- [Learning Energy-Based Approximate Inference Networks for Structured Applications in NLP](https://mlsanity.com/paper/2108.12522/) (2021)
- [Fine Tuning Named Entity Extraction Models for the Fantasy Domain](https://mlsanity.com/paper/2402.10662/) (2024)

## Abstract (arXiv)

Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.