scholarly journals Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially

2019 ◽  
Vol 33 ◽  
pp. 5845-5852
Author(s):  
Yudong Zhang ◽  
Wenhao Zheng ◽  
Ming Li
Keyword(s):  
Natural Language ◽  
Programming Language ◽  
Semantic Representation ◽  
Feature Learning ◽  
Feature Space ◽  
General Purpose ◽  
Semantic Features ◽  
Textual Data ◽  
Software Mining ◽  
Sequential Information

Semantic feature learning for natural language and programming language is a preliminary step in addressing many software mining tasks. Many existing methods leverage information in lexicon and syntax to learn features for textual data. However, such information is inadequate to represent the entire semantics in either text sentence or code snippet. This motivates us to propose a new approach to learn semantic features for both languages, through extracting three levels of information, namely global, local and sequential information, from textual data. For tasks involving both modalities, we project the data of both types into a uniform feature space so that the complementary knowledge in between can be utilized in their representation. In this paper, we build a novel and general-purpose feature learning framework called UniEmbed, to uniformly learn comprehensive semantic representation for both natural language and programming language. Experimental results on three real-world software mining tasks show that UniEmbed outperforms state-of-the-art models in feature learning and prove the capacity and effectiveness of our model.

scholarly journals Multimodal Feature Learning for Video Captioning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sujin Lee ◽  
Incheol Kim
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Feature Learning ◽  
Visual Features ◽  
Semantic Features ◽  
Video Captioning ◽  
Video Feature ◽  
Proposed Model ◽  
Generation Network ◽  
Benchmark Datasets

Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD) and Microsoft Research Video-to-Text (MSR-VTT), demonstrate the performance of the proposed model.

scholarly journals Scene Complexity: A New Perspective on Understanding the Scene Semantics of Remote Sensing and Designing Image-Adaptive Convolutional Neural Networks

2021 ◽  
Vol 13 (4) ◽  
pp. 742
Author(s):  
Jian Peng ◽  
Xiaoming Mei ◽  
Wenbo Li ◽  
Liang Hong ◽  
Bingyu Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Fundamental Problem ◽  
Semantic Representation ◽  
Feature Learning ◽  
Essential Elements ◽  
Complex Scene ◽  
Feature Representations ◽  
The Right ◽  
The Relationship

Scene understanding of remote sensing images is of great significance in various applications. Its fundamental problem is how to construct representative features. Various convolutional neural network architectures have been proposed for automatically learning features from images. However, is the current way of configuring the same architecture to learn all the data while ignoring the differences between images the right one? It seems to be contrary to our intuition: it is clear that some images are easier to recognize, and some are harder to recognize. This problem is the gap between the characteristics of the images and the learning features corresponding to specific network structures. Unfortunately, the literature so far lacks an analysis of the two. In this paper, we explore this problem from three aspects: we first build a visual-based evaluation pipeline of scene complexity to characterize the intrinsic differences between images; then, we analyze the relationship between semantic concepts and feature representations, i.e., the scalability and hierarchy of features which the essential elements in CNNs of different architectures, for remote sensing scenes of different complexity; thirdly, we introduce CAM, a visualization method that explains feature learning within neural networks, to analyze the relationship between scenes with different complexity and semantic feature representations. The experimental results show that a complex scene would need deeper and multi-scale features, whereas a simpler scene would need lower and single-scale features. Besides, the complex scene concept is more dependent on the joint semantic representation of multiple objects. Furthermore, we propose the framework of scene complexity prediction for an image and utilize it to design a depth and scale-adaptive model. It achieves higher performance but with fewer parameters than the original model, demonstrating the potential significance of scene complexity.

scholarly journals GCNGAN: Translating Natural Language to Programming Language based on GAN

2021 ◽  
Vol 1873 (1) ◽  
pp. 012070
Author(s):  
Hongming Dai ◽  
Chen Chen ◽  
Yunjing Li ◽  
Yanghao Yuan
Keyword(s):  
Natural Language ◽  
Programming Language

scholarly journals A Runtime System for XML Transformations in Java

2004 ◽  
Vol 11 (33) ◽  
Author(s):  
Aske Simon Christensen ◽  
Christian Kirkegaard ◽  
Anders Møller
Keyword(s):  
Static Analysis ◽  
Programming Language ◽  
Program Analysis ◽  
Companion Paper ◽  
General Purpose ◽  
Runtime System ◽  
Xml Documents ◽  
Level Data ◽  
High Level ◽  
The Given

We show that it is possible to extend a general-purpose programming language with a convenient high-level data-type for manipulating XML documents while permitting (1) precise static analysis for guaranteeing validity of the constructed XML documents relative to the given DTD schemas, and (2) a runtime system where the operations can be performed efficiently. The system, named Xact, is based on a notion of immutable XML templates and uses XPath for deconstructing documents. A companion paper presents the program analysis; this paper focuses on the efficient runtime representation.

scholarly journals How well do similarity measures predict priming in abstract and concrete concepts?

2018 ◽  
Author(s):  
Maria Montefinese ◽  
Erin Michelle Buchanan ◽  
David Vinson
Keyword(s):  
Word Association ◽  
Semantic Representation ◽  
Similarity Measures ◽  
Semantic Features ◽  
Association Strength ◽  
Abstract Concepts ◽  
Strategic Processes ◽  
Different Types ◽  
Two Measures ◽  
Concrete Concepts

Models of semantic representation predict that automatic priming is determined by associative and co-occurrence relations (i.e., spreading activation accounts), or to similarity in words' semantic features (i.e., featural models). Although, these three factors are correlated in characterizing semantic representation, they seem to tap different aspects of meaning. We designed two lexical decision experiments to dissociate these three different types of meaning similarity. For unmasked primes, we observed priming only due to association strength and not the other two measures; and no evidence for differences in priming for concrete and abstract concepts. For masked primes there was no priming regardless of the semantic relation. These results challenge theoretical accounts of automatic priming. Rather, they are in line with the idea that priming may be due to participants’ controlled strategic processes. These results provide important insight about the nature of priming and how association strength, as determined from word-association norms, relates to the nature of semantic representation.

scholarly journals Emerging trends: A gentle introduction to fine-tuning

2021 ◽  
Vol 27 (6) ◽  
pp. 763-778
Author(s):  
Kenneth Ward Church ◽  
Zeyu Chen ◽  
Yanjun Ma
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Question Answering ◽  
General Purpose ◽  
Fine Tuning ◽  
Language Engineering ◽  
Training Models ◽  
Emerging Trends ◽  
Foundation Model ◽  
Programming Skills

AbstractThe previous Emerging Trends article (Church et al., 2021. Natural Language Engineering27(5), 631–645.) introduced deep nets to poets. Poets is an imperfect metaphor, intended as a gesture toward inclusion. The future for deep nets will benefit by reaching out to a broad audience of potential users, including people with little or no programming skills, and little interest in training models. That paper focused on inference, the use of pre-trained models, as is, without fine-tuning. The goal of this paper is to make fine-tuning more accessible to a broader audience. Since fine-tuning is more challenging than inference, the examples in this paper will require modest programming skills, as well as access to a GPU. Fine-tuning starts with a general purpose base (foundation) model and uses a small training set of labeled data to produce a model for a specific downstream application. There are many examples of fine-tuning in natural language processing (question answering (SQuAD) and GLUE benchmark), as well as vision and speech.

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