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DESIGN AND DEVELOPMENT OF MORPHOLOGICAL ANALYZER FOR TIGRIGNA VERBS USING HYBRID APPROACH

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Research Square

Research Square 2023年

作者： Gebremeskel, Hagos Gebremedhin Chong, Feng Heyan, Huang Beijing Engineering Research Center of High Volume Language Information Processing Cloud Computing Applications Laboratory School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science Mekelle University Tigray Mekelle231 Ethiopia

Morphological analyzer is the base for various high-level NLP applications such as information retrieval, spell checking, grammar checking, machine translation, speech recognition, POS tagging and automatic sentence construction. This paper is carefully designed for design and analysis of morphological analyzer Tigrigna verbs using hybrid of memory learning and rules based approaches. The experiment have conducted using Python 3 where TiMBL algorithms IB2 and TRIBL2, and Finite State Transducer rules are used. The performance of the system has been evaluated using 10 fold cross validation technique. Testing was conducted using optimized parameter settings for regular verbs and linguistic rules of the Tigrigna language allomorph and phonology for the irregular verbs. The accuracy of the memory based approach with optimized parameters of TiMBL algorithm IB2 and TRIBL2 was 93.24% and 92.31%, respectively. Finally, the hybrid approach had an actual performance of 95.6% using linguistic rules for handling irregular and copula verbs. © 2023, CC BY.

关键词： Machine learning

NNOSE: Nearest Neighbor Occupational Skill Extraction

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arXiv 2024年

作者： Zhang, Mike van der Goot, Rob Kan, Min-Yen Plank, Barbara Department of Computer Science IT University of Copenhagen Denmark Pioneer Centre for Artificial Intelligence Copenhagen Denmark School of Computing National University of Singapore Singapore Singapore MaiNLP Center for Information and Language Processing LMU Munich Germany Munich Germany

The labor market is changing rapidly, prompting increased interest in the automatic extraction of occupational skills from text. With the advent of English benchmark job description datasets, there is a need for systems that handle their diversity well. We tackle the complexity in occupational skill datasets tasks-combining and leveraging multiple datasets for skill extraction, to identify rarely observed skills within a dataset, and overcoming the scarcity of skills across datasets. In particular, we investigate the retrieval-augmentation of language models, employing an external datastore for retrieving similar skills in a dataset-unifying manner. Our proposed method, Nearest Neighbor Occupational Skill Extraction (NNOSE) effectively leverages multiple datasets by retrieving neighboring skills from other datasets in the datastore. This improves skill extraction without additional fine-tuning. Crucially, we observe a performance gain in predicting infrequent patterns, with substantial gains of up to 30% span-F1 in cross-dataset settings. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

Synthesis speech Based Data Augmentation for Low Resource Children ASR 23rd

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Synthesis Speech Based Data Augmentation for Low Resource Ch...

23rd International Conference on speech and computer, SPECOM 2021

作者： Kadyan, Virender Kathania, Hemant Govil, Prajjval Kurimo, Mikko Speech and Language Research Centre School of Computer Science University of Petroleum and Energy Studies Dehradun Uttarakhand India Department of Electronics and Communication Engineering National Institute of Sikkim Ravangla India Department of Signal Processing and Acoustics Aalto University Espoo Finland

ISBN: (纸本)9783030878016

Successful speech recognition for children requires large training data with sufficient speaker variability. The collection of such a training database of children’s voices is challenging and very expensive for zero/low resource language like Punjabi. In this paper, the data scarcity issue of the low resourced language Punjabi is addressed through two levels of augmentation. The original training corpus is first augmented by modifying the prosody parameters for pitch and speaking rate. Our results show that the augmentation improves the system performance over the baseline system. Then the augmented data combined with original data and used to train the TTS system to generate synthesis data and extended dataset is further used for augmented by generating children’s utterances using text-to-speech synthesis and sampling the language model with methods that increase the acoustic and lexical diversity. The final speech recognition performance indicates a relative improvement of 50.10% with acoustic and 57.40% with language diversity based augmentation in comparison to that of the baseline system respectively. © 2021, Springer Nature Switzerland AG.

关键词： speech synthesis

Can LLMs Generate Tabular Summaries of science Papers? Rethinking the Evaluation Protocol

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arXiv 2025年

作者： Wang, Weiqi Ou, Jiefu Song, Yangqiu Van Durme, Benjamin Khashabi, Daniel Center for Speech and Language Processing Johns Hopkins University BaltimoreMD United States Department of Computer Science and Engineering HKUST Hong Kong

Literature review tables are essential for summarizing and comparing collections of scientific papers. We explore the task of generating tables that best fulfill a user’s informational needs given a collection of scientific papers. Building on recent work (Newman et al., 2024), we extend prior approaches to address real-world complexities through a combination of LLM-based methods and human annotations. Our contributions focus on three key challenges encountered in real-world use: (i) User prompts are often under-specified;(ii) Retrieved candidate papers frequently contain irrelevant content;and (iii) Task evaluation should move beyond shallow text similarity techniques and instead assess the utility of inferred tables for information-seeking tasks (e.g., comparing papers). To support reproducible evaluation, we introduce ARXIV2TABLE, a more realistic and challenging benchmark for this task, along with a novel approach to improve literature review table generation in real-world scenarios. Our extensive experiments on this benchmark show that both open-weight and proprietary LLMs struggle with the task, highlighting its difficulty and the need for further advancements. Our dataset and code are available at https://***/JHU-CLSP/arXiv2Table. Copyright © 2025, The Authors. All rights reserved.

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Concurrent validity of instrumented insoles measuring gait and balance metrics in Parkinson’s disease

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Concurrent validity of instrumented insoles measuring gait a...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sophia A. Watkinson Anthony Anderson Michael Caiola David Eguren Michael Gonzalez Laureano Moro Velazquez Najim Dehak Chelsea Motley Emile Moukheiber Kelly Mills Brittney Muir Ankur Butala Kimberly Kontson Division of Biomedical Physics US Food and Drug Administration MD USA Center for Language and Speech Processing Johns Hopkins University Baltimore MD USA Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Neurology Johns Hopkins University School of Medicine Baltimore MD USA Department of Veterans Affairsy Center for Limb Loss and Mobility Seattle WA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Several commercially available instrumented insole systems have been examined for validity and repeatability, but very few studies have focused on validation of the Moticon OpenGo sensor insoles in measuring gait and balance parameters in a clinical population. Given the paucity of studies examining the validity of these novel technologies in PD, there were two main goals of this research: (1) assess the concurrent validity of the Moticon OpenGo sensor insoles for gait and balance assessment in people with PD using a pressure-sensitive electronic walkway (Protokinetics Zeno™ walkway) as a reference system and (2) compare the gait metrics derived from the insole and walkway systems during a walking and turning task in order to assess the output of the systems under more real-world conditions. Twelve participants (5F/7M; mean age 71.3 ± 6.8 years) with a diagnosis of Parkinson’s disease and a score of 2.5 or 3 on the Modified Hoehn & Yahr score performing straight-line walking, walking with turning, and balance tasks were included in this analysis. Differences in the estimated gait metrics from each system were evaluated through Bland-Altman analysis and calculation of Pearson’s correlation coefficient (r). Results showed strong agreement between the insoles and the reference walkway system during both self-selected pace and hurried pace walking tasks. Although agreement is not as strong when estimating spatial metrics, the limits of agreement (LOA) still indicate that clinically important differences can be seen. Additional investigation into the insole derived center of pressure metrics from the insole sensor systems may be necessary as well as investigation into improved algorithms for capturing gait metrics during more real-world conditions in both types of systems.

关键词： Measurement Legged locomotion Correlation coefficient Instruments Turning Sensor systems Engineering in medicine and biology Diseases

Decoding speech Categorization using Microstate Cortical EEG Signals and Machine Learning

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Decoding Speech Categorization using Microstate Cortical EEG...

IEEE Signal processing in Medicine and Biology Symposium (SPMB)

作者： M. Mahmud M. Hasan M. Yeasin G. Bidelman Div. of General Internal Medicine Univ. of Tennessee Health Science Center Memphis TN USA Department of Electrical and Computer Engineering University of Memphis TN USA Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Department of Speech Language and Hearing Sciences Indiana University Bloomington IN USA

ISBN: (数字)9798350388572

ISBN: (纸本)9798350388589

Categorical perception (CP) is a perceptual phenomenon that refers to the tendency of humans to group speech sounds into discrete units. In this work, we used cortical event-related potential (ERP) signals, recorded during a speech identification task, as input to a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and machine learning (ML) classifiers to examine how neural signatures of the brain distinguish prototypical vs. ambiguous speech token categories. Particularly, we used extreme gradient boosting (XGBoost), support vector machine, and random forest classifiers. Our analysis shows that using source reconstructed whole-brain data, the XGBoost classifier yielded the best classification accuracy of 94.12% with (area under the curve (AUC) 94.13%, Precision 94.00%, F1-score 94.00%) in the 197-258 ms time window after the stimulus onset. We also identified 15 important brain regions that distinguished vowel classes with accuracy of 90.28% (AUC 90.17%, F1-score 90.00%, Precision 90.00%), 4% less than whole-brain data. Remarkably, out of these 15 critical brain regions, nine were from the left hemisphere, consistent with the left-brain dominance for language processing. The best speech decoding was obtained from early portions of the ERP (~180-320 ms), which corroborates notions that sensory encoding is critical for successful phonetic processing.

关键词： Support vector machines Accuracy Hidden Markov models Phonetics Signal processing Electroencephalography Real-time systems Decoding speech processing Random forests

Generating Sequences by Learning to Self-Correct

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arXiv 2022年

作者： Welleck, Sean Lu, Ximing West, Peter Brahman, Faeze Shen, Tianxiao Khashabi, Daniel Choi, Yejin Allen Institute for Artificial Intelligence United States Center for Language and Speech Processing Johns Hopkins University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States

Sequence generation applications require satisfying semantic constraints, such as ensuring that programs are correct, using certain keywords, or avoiding undesirable content. language models, whether fine-tuned or prompted with few-shot demonstrations, frequently violate these constraints, and lack a mechanism to iteratively revise their outputs. Moreover, some powerful language models are of extreme scale or inaccessible, making it inefficient, if not infeasible, to update their parameters for task-specific adaptation. We present SELF-CORRECTION, an approach that decouples an imperfect base generator (an off-the-shelf language model or supervised sequence-to-sequence model) from a separate corrector that learns to iteratively correct imperfect generations. To train the corrector, we propose an online training procedure that can use either scalar or natural language feedback on intermediate imperfect generations. We show that SELF-CORRECTION improves upon the base generator in three diverse generation tasks-mathematical program synthesis, lexically-constrained generation, and toxicity control- even when the corrector is much smaller than the base generator. © 2022, CC BY.

关键词： Iterative methods

Enabling Local Editing in Diffusion Models by Joint and Individual Component Analysis

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arXiv 2024年

作者： Kouzelis, Theodoros Plitsis, Manos Nicolaou, Mihalis A. Panagakis, Yannis National Technical University of Athens Athens Greece Archimedes AI Athena RC Athens Greece Institute for Language and Speech Athena RC Processing Athens Greece Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece Computation-based Science and Technology Research Center The Cyprus Institute Nicosia Cyprus

Recent advances in Diffusion Models (DMs) have led to significant progress in visual synthesis and editing tasks, establishing them as a strong competitor to Generative Adversarial Networks (GANs). However, the latent space of DMs is not as well understood as that of GANs. Recent research has focused on unsupervised semantic discovery in the latent space of DMs by leveraging the bottleneck layer of the denoising network, which has been shown to exhibit properties of a semantic latent space. However, these approaches are limited to discovering global attributes. In this paper we address, the challenge of local image manipulation in DMs and introduce an unsupervised method to factorize the latent semantics learned by the denoising network of pre-trained DMs. Given an arbitrary image and defined regions of interest, we utilize the Jacobian of the denoising network to establish a relation between the regions of interest and their corresponding subspaces in the latent space. Furthermore, we disentangle the joint and individual components of these subspaces to identify latent directions that enable local image manipulation. Once discovered, these directions can be applied to different images to produce semantically consistent edits, making our method suitable for practical applications. Experimental results on various datasets demonstrate that our method can produce semantic edits that are more localized and have better fidelity compared to the state-of-the-art. https://***/localdiff/. © 2024, CC BY.

关键词： Generative adversarial networks

SAMRS: scaling-up remote sensing segmentation dataset with segment anything model 23

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SAMRS: scaling-up remote sensing segmentation dataset with s...

Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Di Wang Jing Zhang Bo Du Minqiang Xu Lin Liu Dacheng Tao Liangpei Zhang School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China School of Computer Science Faculty of Engineering The University of Sydney Australia National Engineering Research Center of Speech and Language Information Processing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object detection datasets to develop an efficient pipeline for generating a large-scale RS segmentation dataset, dubbed SAMRS. SAMRS totally possesses 105,090 images and 1,668,241 instances, surpassing existing high-resolution RS segmentation datasets in size by several orders of magnitude. It provides object category, location, and instance information that can be used for semantic segmentation, instance segmentation, and object detection, either individually or in combination. We also provide a comprehensive analysis of SAMRS from various aspects. Moreover, preliminary experiments highlight the importance of conducting segmentation pre-training with SAMRS to address task discrepancies and alleviate the limitations posed by limited training data during fine-tuning. The code and dataset will be available at https://***/ViTAE-Transformer/SAMRS.

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