Parallel Algorithms for Unsupervised Tagging

We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approx...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Transactions of the Association for Computational Linguistics Ročník 2; s. 105 - 118
Hlavní autori: Ravi, Sujith, Vassilivitskii, Sergei, Rastogi, Vibhor
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.07.2024
MIT Press Journals, The
The MIT Press
Predmet:
Algorithms
Approximation
Dictionaries
Grammar
Greedy algorithms
Heuristic
Labeling
Labels
Languages
Machine learning
Marking
Mathematical analysis
Minimization
Natural language processing
Optimization
Parallel processing
Speech
Tagging (Computational linguistics)
ISSN:2307-387X, 2307-387X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:We propose a new method for unsupervised tagging that finds minimal models which are then further improved by Expectation Maximization training. In contrast to previous approaches that rely on manually specified and multi-step heuristics for model minimization, our approach is a simple greedy approximation algorithm DMLC (D -M -L -C ) that solves this objective in a single step. We extend the method and show how to efficiently parallelize the algorithm on modern parallel computing platforms while preserving approximation guarantees. The new method easily scales to large data and grammar sizes, overcoming the memory bottleneck in previous approaches. We demonstrate the power of the new algorithm by evaluating on various sequence labeling tasks: Part-of-Speech tagging for multiple languages (including low-resource languages), with complete and incomplete dictionaries, and supertagging, a complex sequence labeling task, where the grammar size alone can grow to millions of entries. Our results show that for all of these settings, our method achieves state-of-the-art scalable performance that yields high quality tagging outputs.
Bibliografia:Volume, 2014
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2307-387X
2307-387X
DOI:10.1162/tacl_a_00169