13 research outputs found
Paradigm Completion for Derivational Morphology
The generation of complex derived word forms has been an overlooked problem
in NLP; we fill this gap by applying neural sequence-to-sequence models to the
task. We overview the theoretical motivation for a paradigmatic treatment of
derivational morphology, and introduce the task of derivational paradigm
completion as a parallel to inflectional paradigm completion. State-of-the-art
neural models, adapted from the inflection task, are able to learn a range of
derivation patterns, and outperform a non-neural baseline by 16.4%. However,
due to semantic, historical, and lexical considerations involved in
derivational morphology, future work will be needed to achieve performance
parity with inflection-generating systems.Comment: EMNLP 201
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
MORSE: Semantic-ally Drive-n MORpheme SEgment-er
We present in this paper a novel framework for morpheme segmentation which
uses the morpho-syntactic regularities preserved by word representations, in
addition to orthographic features, to segment words into morphemes. This
framework is the first to consider vocabulary-wide syntactico-semantic
information for this task. We also analyze the deficiencies of available
benchmarking datasets and introduce our own dataset that was created on the
basis of compositionality. We validate our algorithm across datasets and
present state-of-the-art results
Unsupervised learning of allomorphs in Turkish
© 2017 The Author. Published by The Scientific and Technological Research Council of Turkey. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisher’s website: https://journals.tubitak.gov.tr/elektrik/issues/elk-17-25-4/elk-25-4-57-1605-216.pdfOne morpheme may have several surface forms that correspond to allomorphs. In English, ed and d are
surface forms of the past tense morpheme, and s, es, and ies are surface forms of the plural or present tense morpheme.
Turkish has a large number of allomorphs due to its morphophonemic processes. One morpheme can have tens of different
surface forms in Turkish. This leads to a sparsity problem in natural language processing tasks in Turkish. Detection
of allomorphs has not been studied much because of its difficulty. For example, t¨u and di are Turkish allomorphs (i.e.
past tense morpheme), but all of their letters are different. This paper presents an unsupervised model to extract the
allomorphs in Turkish. We are able to obtain an F-measure of 73.71% in the detection of allomorphs, and our model
outperforms previous unsupervised models on morpheme clustering.Published versio
Modeling Syntactic Context Improves Morphological Segmentation
The connection between part-of-speech (POS) categories and morphological properties is well-documented in linguistics but underutilized in text processing systems. This paper proposes a novel model for morphological segmentation that is driven by this connection. Our model learns that words with common affixes are likely to be in the same syntactic category and uses learned syntactic categories to refine the segmentation boundaries of words. Our results demonstrate that incorporating POS categorization yields substantial performance gains on morphological segmentation of Arabic.United States. Army Research Office (contract/grant number W911NF-10-1-0533)U.S. Army Research Laboratory (contract/grant number W911NF-10-1-0533
Inferring Morphological Rules from Small Examples using 0/1 Linear Programming
We show how to express the problem of finding an optimal morpheme segmentation from a set of labelled words as a 0/1 linear programming problem, and how to build on this to analyse a language’s morphology. The result is an automatic method for segmentation and labelling that works well even when there is very little training data available
Joint Bayesian Morphology learning of Dravidian Languages
In this paper a methodology for learning the complex agglutinative morphology of some Indian languages using Adaptor Grammars and morphology rules is presented. Adaptor grammars are a compositional Bayesian framework for grammatical inference, where we define a morphological grammar for agglutinative languages and morphological boundaries are inferred from a plain text corpus. Once morphological segmentations are produce,
regular expressions for sandhi rules and orthography are applied to achieve the final segmentation. We test our algorithm in the case of two complex languages from the Dravidian family. The same morphological model and results are evaluated comparing to other state-of-the art unsupervised morphology learning systemsPostprint (published version