scholarly journals On the Semantic Representation and Extraction of Complex Category Descriptors

2014 ◽  
pp. 45-50 ◽  
Author(s):  
André Freitas ◽  
Rafael Vieira ◽  
Edward Curry ◽  
Danilo Carvalho ◽  
João Carlos Pereira da Silva
Keyword(s):  
Semantic Representation

scholarly journals Joint Semantic Synthesis and Morphological Analysis of the Derived Word

2018 ◽  
Vol 6 ◽  
pp. 33-48 ◽  
Author(s):  
Ryan Cotterell ◽  
Hinrich Schütze
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Probabilistic Model ◽  
Morphological Analysis ◽  
Semantic Representation ◽  
English Word ◽  
Word Formation ◽  
Additive Models ◽  
Segmentation Accuracy ◽  
Morphological Productivity

Much like sentences are composed of words, words themselves are composed of smaller units. For example, the English word questionably can be analyzed as question+ able+ ly. However, this structural decomposition of the word does not directly give us a semantic representation of the word’s meaning. Since morphology obeys the principle of compositionality, the semantics of the word can be systematically derived from the meaning of its parts. In this work, we propose a novel probabilistic model of word formation that captures both the analysis of a word w into its constituent segments and the synthesis of the meaning of w from the meanings of those segments. Our model jointly learns to segment words into morphemes and compose distributional semantic vectors of those morphemes. We experiment with the model on English CELEX data and German DErivBase (Zeller et al., 2013) data. We show that jointly modeling semantics increases both segmentation accuracy and morpheme F1 by between 3% and 5%. Additionally, we investigate different models of vector composition, showing that recurrent neural networks yield an improvement over simple additive models. Finally, we study the degree to which the representations correspond to a linguist’s notion of morphological productivity.

scholarly journals Representation and Presentation of Culinary Tradition as Cultural Heritage

Heritage ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 612-640
Author(s):  
Nikolaos Partarakis ◽  
Danai Kaplanidi ◽  
Paraskevi Doulgeraki ◽  
Effie Karuzaki ◽  
Argyro Petraki ◽  
...  
Keyword(s):  
Social Sciences ◽  
Knowledge Representation ◽  
Cultural Heritage ◽  
Systematic Approach ◽  
Semantic Representation ◽  
Ethnographic Research ◽  
Abstract Form ◽  
Web Based ◽  
Social Sciences And Humanities ◽  
Authoring Environment

This paper presents a knowledge representation framework and provides tools to allow the representation and presentation of the tangible and intangible dimensions of culinary tradition as cultural heritage including the socio-historic context of its evolution. The representation framework adheres to and extends the knowledge representation standards for the Cultural Heritage (CH) domain while providing a widely accessible web-based authoring environment to facilitate the representation activities. In strong collaboration with social sciences and humanities, this work allows the exploitation of ethnographic research outcomes by providing a systematic approach for the representation of culinary tradition in the form of recipes, both in an abstract form for their preservation and in a semantic representation of their execution captured on-site during ethnographic research.

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 Clauses and the semantic representation of words

1985 ◽  
Vol 13 (4) ◽  
pp. 371-376 ◽  
Author(s):  
Barbara Von Eckardt ◽  
Mary C. Potter
Keyword(s):  
Semantic Representation

scholarly journals The Proposition Bank: An Annotated Corpus of Semantic Roles

2005 ◽  
Vol 31 (1) ◽  
pp. 71-106 ◽  
Author(s):  
Martha Palmer ◽  
Daniel Gildea ◽  
Paul Kingsbury
Keyword(s):  
Automatic System ◽  
Semantic Representation ◽  
Higher Order ◽  
Practical Approach ◽  
Semantic Role ◽  
Syntactic Parsing ◽  
Semantic Roles ◽  
Syntactic Structures ◽  
Annotation Process ◽  
Types Of Information

The Proposition Bank project takes a practical approach to semantic representation, adding a layer of predicate-argument information, or semantic role labels, to the syntactic structures of the Penn Treebank. The resulting resource can be thought of as shallow, in that it does not represent coreference, quantification, and many other higher-order phenomena, but also broad, in that it covers every instance of every verb in the corpus and allows representative statistics to be calculated. We discuss the criteria used to define the sets of semantic roles used in the annotation process and to analyze the frequency of syntactic/semantic alternations in the corpus. We describe an automatic system for semantic role tagging trained on the corpus and discuss the effect on its performance of various types of information, including a comparison of full syntactic parsing with a flat representation and the contribution of the empty “trace” categories of the treebank.

Semantic representation of Kinship systems

1975 ◽  
Vol 4 (2) ◽  
pp. 133-140 ◽  
Author(s):  
John H. Winkelman
Keyword(s):  
Semantic Representation
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