Combinatory Categorial Grammar (CCG), a lexicalized formalism known for its flexible constituency, is well-suited for modeling headfinal languages with flexible word order like Turkish.Building on Kuzgun et al. (2023), we first develop a Turkish CCG lexicon by automatically inducing categories from a dependency treebank.By leveraging standard and extended operations tailored to Turkish syntax, our parser achieves a robust coverage of 92.5%.Furthermore, we introduce the first (partially) incremental, left-to-right CCG parser for Turkish, designed to facilitate the immediate integration of words into the evolving representation.Finally, we present an example experiment showing that CCG parsers can model psycholinguistic evidence for extra processing costs associated with arguments in noncanonical positions, via the frequency of orderreversing operations.These findings provide evidence that CCG offers a cognitively plausible framework for modeling real-time processing in languages like Turkish. RulesCategory

To understand what kinds of linguistic knowledge are encoded by pretrained Chinese language models (LMs), we introduce the benchmark of Sino LINGuistics (SLING), which consists of 38K minimal sentence pairs in Mandarin Chinese grouped into 9 high-level linguistic phenomena.Each pair demonstrates the acceptability contrast of a specific syntactic or semantic phenomenon (e.g., The keys are lost vs.The keys is lost), and an LM should assign lower perplexity to the acceptable sentence.In contrast to the CLiMP dataset (Xiang et al., 2021), which also contains Chinese minimal pairs and was created by translating the vocabulary of the English BLiMP dataset, the minimal pairs in SLING are derived primarily by applying syntactic and lexical transformations to naturally-occurring, linguist-annotated sentences from the Chinese Treebank 9.0, thus addressing severe issues in CLiMP's data generation process.We test 18 publicly available pretrained monolingual (e.g., BERT-base-zh, CPM) and multi-lingual (e.g., mT5, XLM) language models on SLING.Our experiments show that the average accuracy for LMs is far below human performance (69.7% vs. 97.1%),while BERT-base-zh achieves the highest accuracy (84.8%) of all tested LMs, even much larger ones.Additionally, we find that most LMs have a strong gender and number (singular/plural) bias, and they perform better on local phenomena than hierarchical ones. 1

In image processing, it is well known that mean square error criteria is perceptually inadequate. Consequently, image quality assessment (IQA) has emerged as a new branch to overcome this issue, and this has led to the discovery of one of the most popular perceptual measures, namely, the structural similarity index (SSIM). This measure is mathematically simple, yet powerful enough to express the quality of an image. Therefore, it is natural to deploy SSIM in model based applications, such as denoising, restoration, classification, etc. However, the non-convex nature of this measure makes this task difficult. Our attempt in this work is to discuss problems associated with its convex program and take remedial action in the process of obtaining a generalized convex framework. The obtained framework has been seen as a component of an alternative learning scheme for the case of a regularized linear model. Subsequently, we develop a relevant dictionary learning module as a part of alternative learning. This alternative learning scheme with sparsity prior is finally used in denoising and deblurring applications. To further boost the performance, an iterative scheme is developed based on the statistical nature of added noise. Experiments on image denoising and deblurring validate the effectiveness of the proposed scheme. Furthermore, it has been shown that the proposed framework achieves highly competitive performance with respect to other schemes in literature and performs better in natural images in terms of SSIM and visual inspection.

Sparse representation based image and video processing have recently drawn much attention. Dictionary learning is an essential task in this framework. Our novel proposition involves direct computation of the dictionary by analyzing the distribution of training data in the metric space. The resulting representation is applied in the domain of grey scale image denoising. Denoising is one of the fundamental problems in image processing. Sparse representation deals efficiently with this problem. In this regard, dictionary learning from noisy images, improves denoising performance. Experimental results indicate that our proposed approach outperforms the ones using K-SVD for additive high-level Gaussian noise while for the medium range of noise level, our results are comparable.