Semi-supervised Classification for Natural Language Processing

By Rushdi Shams. 2014

14 citations recorded by the source corpus.

Semi-supervised classification is an interesting idea where classification models are learned from both labeled and unlabeled data. It has several advantages over supervised classification in natural language processing domain. For instance, supervised classification exploits only labeled data that are expensive, often difficult to get, inadequate in quantity, and require human experts for annotation. On the other hand, unlabeled data are inexpensive and abundant. Despite the fact that many factors limit the wide-spread use of semi-supervised classification, it has become popular since its level of performance is empirically as good as supervised classification. This study explores the possibilities and achievements as well as complexity and limitations of semi-supervised classification for several natural langue processing tasks like parsing, biomedical information processing, text classification, and summarization.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Paolo Ferragina
2. Richard Durbin
3. Meng He
4. Ben Langmead
5. Chenyan Xiong
6. Heng Li
7. Gonzalo Navarro
8. Veli Mäkinen

In Natural Language Processing

1. Paolo Ferragina
2. Richard Durbin
3. Ben Langmead
4. Chenyan Xiong
5. Heng Li
6. Jamie Callan
7. Meng He

Citations in the corpus

• ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023, background): “Before 2017, most NLP models were trained using supervised learning for particular tasks and could be used only for the task they were trained on (9).”