Distributed Representation, LDA Topic Modelling and Deep Learning for Emerging Named Entity Recognition from Social Media

2017-09-01 · WS 2017 9 · Patrick Jansson, Shuhua Liu

This paper reports our participation in the W-NUT 2017 shared task on emerging and rare entity recognition from user generated noisy text such as tweets, online reviews and forum discussions. To accomplish this challenging task, we explore an approach that combines LDA topic modelling with deep learning on word level and character level embeddings. The LDA topic modelling generates topic representation for each tweet which is used as a feature for each word in the tweet. The deep learning component consists of two-layer bidirectional LSTM and a CRF output layer. Our submitted result performed at 39.98 (F1) on entity and 37.77 on surface forms. Our new experiments after submission reached a best performance of 41.81 on entity and 40.57 on surface forms.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)

Methods 이 논문이 사용한 방법론

LDA Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in…

Sigmoid Activation 설명 없음

Tanh Activation 설명 없음

CRF Conditional Random Fields or CRFs are a type of probabilistic graph model that take neighboring sample context into account for tasks like classification. Prediction is…

LSTM An LSTM is a type of recurrent neural network that addresses the vanishing gradient problem in vanilla…

textTOvec: Deep Contextualized Neural Autoregressive Topic Models of Language with Distributed Compositional Prior

2018-10-09 · ICLR 2019 5 · Pankaj Gupta, Yatin Chaudhary, Florian Buettner, Hinrich Schütze

We address two challenges of probabilistic topic modelling in order to better estimate the probability of a word in a given context, i.e., P(word|context): (1) No Language Structure in Context: Probabilistic topic models…

Information Extraction Information Retrieval Language Modelling Retrieval+2

Methodological Rigour in Algorithm Application: An Illustration of Topic Modelling Algorithm

2025-07-01 · Malmi Amadoru arxiv

The rise of advanced computational algorithms has opened new avenues for computationally intensive research approaches to theory development. However, the opacity of these algorithms and lack of transparency and rigour i…

TopicNet: Making Additive Regularisation for Topic Modelling Accessible

2020-05-01 · LREC 2020 5 · Victor Bulatov, Vasiliy Alekseev, Konstantin Vorontsov, Darya Polyudova 외

This paper introduces TopicNet, a new Python module for topic modeling. This package, distributed under the MIT license, focuses on bringing additive regularization topic modelling (ARTM) to non-specialists using a gener…

Model Selection

Probabilistic Topic Modelling with Transformer Representations

2024-03-06 · Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken 외

Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful models that rely on straightforward clus…

Diversity

Deep Learning based Topic Analysis on Financial Emerging Event Tweets

2020-08-03 · Shaan Aryaman, Nguwi Yok Yen

Financial analyses of stock markets rely heavily on quantitative approaches in an attempt to predict subsequent or market movements based on historical prices and other measurable metrics. These quantitative analyses mig…

Clustering Deep Learning Word Embeddings