Wide-coverage parsing, semantics and morphology

Date

2018-10-01

Author

Bozşahin, Hüseyin Cem

Metadata

Show full item record

Item Usage Stats

163
views

0
downloads

Wide-coverage parsing poses three demands: broad coverage over preferably free text, depth in semantic representation for purposes such as inference in question answering, and computational efficiency. We show for Turkish that these goals are not inherently contradictory when we assign categories to sub-lexical elements in the lexicon. The presumed computational burden of processing such lexicons does not arise when we work with automata-constrained formalisms that are trainable on word-meaning correspondences at the level of predicate-argument structures for any string, which is characteristic of radically lexicalizable grammars. This is helpful in morphologically simpler languages too, where word-based parsing has been shown to benefit from sub-lexical training.

URI

https://hdl.handle.net/11511/85216

Relation

Turkish Natural language processing

Collections

Graduate School of Informatics, Book / Book chapter

Suggestions

OpenMETU
Core

Extended Target Tracking Using Gaussian Processes Wahlström, Niklas; Özkan, Emre (2015-08-15) In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
Deep Spectral Convolution Network for Hyperspectral Unmixing Akar, Gözde (2018-10-10) In this paper, we propose a novel hyperspectral unmixing technique based on deep spectral convolution networks (DSCN). Particularly, three important contributions are presented throughout this paper. First, fully-connected linear operation is replaced with spectral convolutions to extract local spectral characteristics from hyperspectral signatures with a deeper network architecture. Second, instead of batch normalization, we propose a spectral normalization layer which improves the selectivity of filters b...
Deep metric learning with distance sensitive entangled triplet losses Karaman, Kaan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2021-2-12) Metric learning aims to define a distance that is able to measure the semantic difference between the instances in a dataset. The most recent approaches in this area mostly utilize deep neural networks as their models to map the input data into a feature space by finding appropriate distance metrics between the features. A number of loss functions are already defined in the literature based on these similarity metrics to discriminate instances in the feature space. In this thesis, we particularly focus on t...
Semi-supervised generative guidance for zero-shot semantic segmentation Önem, Abdullah Cem; Cinbiş, Ramazan Gökberk; Department of Computer Engineering (2022-1) Collecting fully-annotated data to train deep networks for semantic image segmentation can be prohibitively costly due to difficulty of making pixel-by-pixel annotations. In this context, zero-shot learning based formulations relax the labelled data requirements by enabling the recognition of classes without training examples. Recent studies on zero-shot learning of semantic segmentation models, however, highlight the difficulty of the problem. This thesis proposes techniques towards improving zero-shot gen...
Low-level image segmentation based scene classification Akbaş, Emre (2010-08-26) This paper is aimed at evaluating the semantic information content of multiscale, low-level image segmentation. As a method of doing this, we use selected features of segmentation for semantic classification of real images. To estimate the relative measure of the information content of our features, we compare the results of classifications we obtain using them with those obtained by others using the commonly used patch/grid based features. To classify an image using segmentation based features, we model th...

Citation Formats

H. C. Bozşahin, Wide-coverage parsing, semantics and morphology. 2018, p. 174.