Part-of-Speech Tagging With Neural Networks
By Helmut Schmid. 1994
Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method based on neural networks (Net-Tagger) is presented and its performance is compared to that of a HMM-tagger (Cutting et al., 1992) and a trigram-based tagger (Kempe, 1993). It is shown that the Net-Tagger performs as well as the trigram-based tagger and better than the HMM-tagger.
Subfields: Natural Language Processing, Information Extraction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Yonatan Belinkov
- Nadir Durrani
- Hassan Sajjad
- Fahim Dalvi
- James R. Glass
- Graham Neubig
- Philipp Koehn
- Jan Niehues
In Information Extraction
- Dan Roth
- Artur Janicki
- Stephan Vogel
- Hassan Sajjad
- Nadir Durrani
- A. Waibel
- Yonatan Belinkov
- Jan Niehues
Citations in the corpus
- Part of Speech Tagging Using a Network of Linear Separators (1998, methodology): “The most notable methods are based on Hidden Markov Models(HMM)(Kupiec, 1992; Sch(cid:127)utze, 1995), transformation rules(Brill, 1995; Brill, 1997), and multi-layer neural networks(Schmid, 1994).”
- Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder (2017, methodology): “We used the following tools recommended on the Moses website 1 to annotate the data: LoPar (Schmid, 2000) for German, Tree-tagger (Schmid, 1994) for Czech and MXPOST (Ratnaparkhi, 1998) for English.”
- A review on the long short-term memory model (2020, background): “A part of speech tagger is a system which automatically assigns the part of speech to words using contextual information (Schmid 1994).”
- Letter N-Gram-based Input Encoding for Continuous Space Language Models (2013, methodology): “Reordering was performed as a preprocessing step using part-of-speech (POS) information generated by the Tree-Tagger (Schmid, 1994).”
- On internal language representations in deep learning: an analysis of machine translation and speech recognition (2018, background): “corpora: MADAMIRA [272] for Arabic POS and morphological tags, Tree-Tagger [296] for Czech and French POS tags, LoPar [297] for German POS and morphological tags, and MXPOST [283] for English POS tags.”
- Application of Neural Networks for POS Tagging and Intonation Control in Speech Synthesis for Polish (2004, methodology): “The Polish language is morphologically rich, like other Slavic languages, unlike e.g. English.”