Semanlink - Machine learning: techniques AND Sequence labeling

Prompt Engineering NER

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2024-04-12 A propos

NER using DSPy

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2024-04-12 A propos

tomaarsen/span-marker-mbert-base-multinerd · Hugging Face

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2023-08-08 A propos

[1904.04458] Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition

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Knowledge Augmented
Language Model (KALM)

a language
model with access to information available in a
KB, no assumptions
about the availability of additional components
(such as Named Entity Taggers) or annotations

> While classes of
named entities (e.g., person or location) occur frequently,
each individual name (e.g, Atherton or
Zhouzhuang) may be observed infrequently even
in a very large corpus of text. As a result language
models learn to represent accurately only the most
popular named entities

> knowing that Alice is a name
used to refer to a person should give ample information
about the context in which the word may
occur (e.g., Bob visited Alice).

> ---

> extends a traditional **RNN LM**

> we enhance a traditional LM with a
gating mechanism that controls whether a particular
word is modeled as a general word or as a reference
to an entity
>
> We train the model end-to-end
with only the traditional predictive language modeling
perplexity objective
>
> KALM is trained end-to-end using
a predictive objective on large corpus of text.

> To the best of our knowledge, KALM is the first
unsupervised neural NER approach.

> KALM extends a traditional, RNN-based neural
LM.

2022-08-31 A propos

[1807.00745] Training a Neural Network in a Low-Resource Setting on Automatically Annotated Noisy Data

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2022-07-18 A propos

[2207.06300] Re2G: Retrieve, Rerank, Generate

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2022-07-14 A propos

[1910.06294] Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models

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2022-03-31 A propos

Studio Ousia sur Twitter : "Now using LUKE is easier than ever!" / Twitter

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2022-03-15 A propos

[2108.13934] Robust Retrieval Augmented Generation for Zero-shot Slot Filling

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2022-01-19 A propos

asahi417/tner: Language model finetuning on NER

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2021-03-04 A propos

Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020)

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2021-02-24 A propos

Named Entity Recognition without Labelled Data: A Weak Supervision Approach (2020) (slides)

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2021-02-24 A propos

kamalkraj/BERT-NER: Pytorch-Named-Entity-Recognition-with-BERT

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2021-02-07 A propos

[2011.06993] FLERT: Document-Level Features for Named Entity Recognition

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2020-12-01 A propos

Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge (ACL Anthology 2020)

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Aim to leverage both contextual representation of input text (deep LMs)  and  knowledge derived
from curated KBs ([Wordnet](tag:wordnet)) to improve [slot tagging](tag:slot_tagging) in the presence of [out-of-vocab](tag:oov) words ([few-shot scenario](tag:few_shot_learning))

Method:

1. retrieve potentially relevant KB entities and
encode them into distributed representations that
describe global graph-structured information
2. BERT encoder
layer to capture context-aware representations of
the sequence and attend to the KB embeddings
using multi-level graph attention
3. integrate
BERT embeddings and the KB embeddings
to predict the slot type

Contributions:

1. feasibility of applying lexical ontology
to facilitate recognizing OOV words. First to consider the large-scale background
knowledge for enhancing context-aware
slot tagging models.
2. a knowledge integration mechanism that uses multi-level graph
attention to model explicit lexical relations.
3.experiments on two benchmark datasets

> our method makes a notable difference in a
scenario where samples are linguistically diverse,
and large vocab exists.

(Better improvements when using RNN than BERT, because BERT already contains a lot of background knowledge)

2020-07-04 A propos

Unsupervised NER using BERT - Hands-on NLP model review - Quora

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2020-03-06 A propos

[2002.11402] Detecting Potential Topics In News Using BERT, CRF and Wikipedia

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2020-02-27 A propos

Interpretable Named entity recognition with keras and LIME – Depends on the definition

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2020-01-09 A propos

Named Entity Recognition with Bert – Depends on the definition

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2020-01-09 A propos

Joint Intent Classification and Slot Filling with Transformers (Jupyter Notebook Viewer)

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2020-01-09 A propos

[1902.10909] BERT for Joint Intent Classification and Slot Filling

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2020-01-09 A propos

How to build deep neural network for custom NER with Keras

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2020-01-07 A propos

Named Entity Recognition with Pytorch Transformers – Pierre-Yves Vandenbussche

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2019-12-11 A propos - Copie locale

[1806.04411] Named Entity Recognition with Extremely Limited Data

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2019-04-11 A propos

[1601.01343] Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

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2019-01-27 A propos

Effect of Non-linear Deep Architecture in Sequence Labeling

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2018-10-22 A propos

Named Entity Recognition and Classification with Scikit-Learn

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2018-09-16 A propos

zalandoresearch/flair: A very simple framework for state-of-the-art NLP

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2018-08-24 A propos

A Framework for Semi supervised Concept Extraction from MOOC content (2017)

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2018-08-12 A propos

[1404.5367] Lexicon Infused Phrase Embeddings for Named Entity Resolution

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2018-05-22 A propos

BiLSTM-CNN-CRF Implementation for Sequence Tagging

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2018-05-21 A propos

Named Entity Recognition and the Road to Deep Learning (2017)

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> the old
and the new-style NLP are not diametrically
opposed: just as it is possible (and useful!) to
incorporate neural-network features into a CRF,
CRFs have influenced some of the best deep
learning models for sequence labelling

This blog post go through the ways of doing NER, starting with CRF:

- When you develop a CRF,
a lot of time goes into finding feature functions (Does a word start with a capital? Is it uppercase? Is it a digit?...) 
- Pb: semantic similarity between words. 
- Gazetteers: lists with names of people, locations and organizations that are known in advance.
- feed Word Embeddings to a CRF: one way is to cluster a set
of word embeddings by distributional similarity, and
provide the CRF with the cluster IDs of a token and its
context words.
- Use word and character based embeddings
- LSTM not good enough -> biLSTM
- **biLSTM predict all labels independently of each other -> add a CRF layer** which outputs a matrix of transition scores between two states: dynamic programming can help find the optimal tag sequence for the sentence

2018-05-20 A propos

Named Entity Recognition and the Stanford NER Software (slides)

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2018-05-20 A propos

Stanford Named Entity Recognizer

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