3,275 research outputs found
Recommended from our members
Minimally supervised induction of morphology through bitexts
textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems.
Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis.
While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic
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
Induction of root and pattern lexicon for unsupervised morphological analysis of Arabic
We propose an unsupervised approach to learning non-concatenative morphology, which we apply to induce a lexicon of Arabic roots and pattern templates. The approach is based on the idea that roots and patterns may be revealed through mutually recursive scoring based on hypothesized pattern and root frequencies. After a further iterative refinement stage, morphological analysis with the induced lexicon achieves a root identification accuracy of over 94%. Our approach differs from previous work on unsupervised learning of Arabic morphology in that it is applicable to naturally-written, unvowelled text
Inducing a Semantically Annotated Lexicon via EM-Based Clustering
We present a technique for automatic induction of slot annotations for
subcategorization frames, based on induction of hidden classes in the EM
framework of statistical estimation. The models are empirically evalutated by a
general decision test. Induction of slot labeling for subcategorization frames
is accomplished by a further application of EM, and applied experimentally on
frame observations derived from parsing large corpora. We outline an
interpretation of the learned representations as theoretical-linguistic
decompositional lexical entries.Comment: 8 pages, uses colacl.sty. Proceedings of the 37th Annual Meeting of
the ACL, 199
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Unsupervised induction of Arabic root and pattern lexicons using machine learning
We describe an approach to building a morphological analyser of Arabic by inducing a lexicon of root and pattern templates from an unannotated corpus. Using maximum entropy modelling, we capture orthographic features from surface words, and cluster the words based on the similarity of their possible roots or patterns. From these clusters, we extract root and pattern lexicons, which allows us to morphologically analyse words. Further enhancements are applied, adjusting for morpheme length and structure. Final root extraction accuracy of 87.2% is achieved. In contrast to previous work on unsupervised learning of Arabic morphology, our approach is applicable to naturally-written, unvowelled Arabic text
- …