Text Segmentation Algorithm

Context:
- It can be applied by a Text Segmentation System.
- It can be supported by a Part-of-Speech Tagging Algorithm.
- It can support an Natural Language Understanding algorithms, such as information extraction algorithm.
- It can range from being a Supervised Text Segmentation Algorithm to being an Unsupervised Text Segmentation Algorithm (such as a dictionary-based algorithm or a zero-shot text segmentation algorithm).
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Example(s):
- a Sentence Boundary Detection Algorithm, which identifies the start and end of sentences.
- an NLP Tagging Algorithm, often used for tagging parts of speech or other linguistic features in text.
- a Deep Neural Network-based Text Segmentation Algorithm, leveraging advanced neural network architectures for segmentation tasks.
- a Text Tokenization Algorithm, for segmenting text into tokens such as words and punctuation.
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Counter-Example(s):
- a DNA Segmentation Algorithm, which is used for segmenting genetic sequences.
- a Syntactic Parsing Algorithm, which is more focused on analyzing the grammatical structure of sentences.
See: CRFpp System.

References

(Zhai et al., 2017) ⇒ Feifei Zhai, Saloni Potdar, Bing Xiang, and Bowen Zhou. (2017). "Neural Models for Sequence Chunking." In: Proceedings of the AAAI conference on artificial intelligence, vol. 31, no. 1.

(Sha & Pereira, 2003a) ⇒ Fei Sha, and Fernando Pereira. (2003). "Shallow Parsing with Conditional Random Fields." In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology (HLT-NAACL 2003). doi:10.3115/1073445.1073473

(Kudo & Matsumoto, 2001) ⇒ Taku Kudo, and Yuji Matsumoto. (2001). "Chunking with Support Vector Machines." In: Proceedings of NAACL Conference (NAACL 2001).

(Abney, 1989) ⇒ Steven P. Abney. (1989). "Parsing By Chunks." The MIT Parsing Volume, 1988-89. Center for Cognitive Science, MIT.