70 research outputs found
Can MDL Improve Unsupervised Chinese Word Segmentation?
International audienceIt is often assumed that Minimum Descrip- tion Length (MDL) is a good criterion for unsupervised word segmentation. In this paper, we introduce a new approach to unsupervised word segmentation of Man- darin Chinese, that leads to segmentations whose Description Length is lower than what can be obtained using other algo- rithms previously proposed in the litera- ture. Suprisingly, we show that this lower Description Length does not necessarily corresponds to better segmentation results. Finally, we show that we can use very basic linguistic knowledge to coerce the MDL towards a linguistically plausible hypoth- esis and obtain better results than any pre- viously proposed method for unsupervised Chinese word segmentation with minimal human effort
Building Morphological Chains for Agglutinative Languages
In this paper, we build morphological chains for agglutinative languages by
using a log-linear model for the morphological segmentation task. The model is
based on the unsupervised morphological segmentation system called
MorphoChains. We extend MorphoChains log linear model by expanding the
candidate space recursively to cover more split points for agglutinative
languages such as Turkish, whereas in the original model candidates are
generated by considering only binary segmentation of each word. The results
show that we improve the state-of-art Turkish scores by 12% having a F-measure
of 72% and we improve the English scores by 3% having a F-measure of 74%.
Eventually, the system outperforms both MorphoChains and other well-known
unsupervised morphological segmentation systems. The results indicate that
candidate generation plays an important role in such an unsupervised log-linear
model that is learned using contrastive estimation with negative samples.Comment: 10 pages, accepted and presented at the CICLing 2017 (18th
International Conference on Intelligent Text Processing and Computational
Linguistics
Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar
A usage-based Construction Grammar (CxG) posits that slot-constraints
generalize from common exemplar constructions. But what is the best model of
constraint generalization? This paper evaluates competing frequency-based and
association-based models across eight languages using a metric derived from the
Minimum Description Length paradigm. The experiments show that
association-based models produce better generalizations across all languages by
a significant margin
Methods and algorithms for unsupervised learning of morphology
This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15
The accepted version of the publication may differ from the final published version.This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Published versio
Proceedings of the Morpho Challenge 2010 Workshop
In natural language processing many practical tasks, such as speech recognition, information retrieval and machine translation depend on a large vocabulary and statistical language models. For morphologically rich languages, such as Finnish and Turkish, the construction of a vocabulary and language models that have a sufficient coverage is particularly difficult, because of the huge amount of different word forms. In Morpho Challenge 2010 unsupervised and semi-supervised algorithms are suggested to provide morpheme analyses for words in different languages and evaluated in various practical applications. As a research theme, unsupervised morphological analysis has received wide attention in conferences and scientific journals focused on computational linguistic and its applications. This is the proceedings of the Morpho Challenge 2010 Workshop that contains one introduction article with a description of the tasks, evaluation and results and six articles describing the participating unsupervised and supervised learning algorithms. The Morpho Challenge 2010 Workshop was held at Espoo, Finland in 2-3 September, 2010.reviewe
Minimally-Supervised Morphological Segmentation using Adaptor Grammars
This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model selection method. In the model selection method, we train unsupervised Adaptor Grammars using an over-articulated metagrammar, then use a small labelled data set to select which potential morph boundaries identified by the metagrammar should be returned in the final output. We evaluate on five languages and show that semi-supervised training provides a boost over unsupervised training, while the model selection method yields the best average results over all languages and is competitive with state-of-the-art semi-supervised systems. Moreover, this method provides the potential to tune performance according to different evaluation metrics or downstream tasks.12 page(s
Unsupervised Language Acquisition
This thesis presents a computational theory of unsupervised language
acquisition, precisely defining procedures for learning language from ordinary
spoken or written utterances, with no explicit help from a teacher. The theory
is based heavily on concepts borrowed from machine learning and statistical
estimation. In particular, learning takes place by fitting a stochastic,
generative model of language to the evidence. Much of the thesis is devoted to
explaining conditions that must hold for this general learning strategy to
arrive at linguistically desirable grammars. The thesis introduces a variety of
technical innovations, among them a common representation for evidence and
grammars, and a learning strategy that separates the ``content'' of linguistic
parameters from their representation. Algorithms based on it suffer from few of
the search problems that have plagued other computational approaches to
language acquisition.
The theory has been tested on problems of learning vocabularies and grammars
from unsegmented text and continuous speech, and mappings between sound and
representations of meaning. It performs extremely well on various objective
criteria, acquiring knowledge that causes it to assign almost exactly the same
structure to utterances as humans do. This work has application to data
compression, language modeling, speech recognition, machine translation,
information retrieval, and other tasks that rely on either structural or
stochastic descriptions of language.Comment: PhD thesis, 133 page
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