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A Unified Semantics Space Model [chapter]

Juan Ye, Lorcan Coyle, Simon Dobson, Paddy Nixon
Location- and Context-Awareness  
This paper proposes a unified space model for more complex environments (e.g., city plan or forest). This space model provides a flexible, expressive, and powerful spatial representation.  ...  It also proposes a new data structure -an integrated lattice and graph model -to express comprehensive spatial relationships.  ...  A Unified Formal Model of Space This section will build a formal semantics to richly express and model spatial information and relationships.  ... 
doi:10.1007/978-3-540-75160-1_7 dblp:conf/loca/YeCDN07 fatcat:wodg54xtfzh2zgik3yqadfqdhm

Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model [article]

Benzhi Wang, Meiyu Liang, Feifei Kou, Mingying Xu
2022 arXiv   pre-print
paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL).It achieves a unified cross-media semantic representation by learning the semantic association  ...  Key words science and technology big data ; cross-media retrieval; cross-media semantic association learning; deep language model; semantic similarity  ...  similarity (3) 𝑥𝑦 It represents text features in unified semantic space and image features in unified semantic space.The overall process of the cross-media deep language model is shown in Table 1 .  ... 
arXiv:2203.15595v1 fatcat:wy4nvtsxq5awliyvxuodrcv4lu

UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations [article]

Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei Li, Weiwei Sun, Wei-Ying Ma
2019 arXiv   pre-print
We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint space of visual and textual concepts.  ...  The space unifies the concepts at different levels, including objects, attributes, relations, and full scenes.  ...  Our model differs with them in two aspects. First, Unified VSE is built upon a factorized semantic space instead of the syntactic knowledge.  ... 
arXiv:1904.05521v2 fatcat:kfsldaebbvawbf7xwd7yuy7izu

Cross-topic distributional semantic representations via unsupervised mappings [article]

Eleftheria Briakou, Nikos Athanasiou, Alexandros Potamianos
2019 arXiv   pre-print
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation.  ...  First, a separate DSM is trained for each topic and then each of the topic-based DSMs is aligned to a common vector space.  ...  Their model performs well for a variety of semantic similarity tasks; however, it lacks a unified representation of multiple senses in a common semantic space.  ... 
arXiv:1904.05674v1 fatcat:exey4gx2lvcu7hqdtku24yz56m

Cross-Topic Distributional Semantic Representations Via Unsupervised Mappings

Eleftheria Briakou, Nikos Athanasiou, Alexandros Potamianos
2019 Proceedings of the 2019 Conference of the North  
In traditional Distributional Semantic Models (DSMs) the multiple senses of a polysemous word are conflated into a single vector space representation.  ...  First, a separate DSM is trained for each topic and then each of the topic-based DSMs is aligned to a common vector space.  ...  Their model performs well for a variety of semantic similarity tasks; however, it lacks a unified representation of multiple senses in a common semantic space.  ... 
doi:10.18653/v1/n19-1110 dblp:conf/naacl/BriakouAP19 fatcat:lqmqsooqmvdlzic4chdwr6ugiy

A Unified Semantic Embedding: Relating Taxonomies and Attributes [article]

Sung Ju Hwang, Leonid Sigal
2014 arXiv   pre-print
By exploiting such a unified model for semantics, we enforce each category to be represented by a supercategory + sparse combination of attributes, with an additional exclusive regularization to learn  ...  We propose a method that learns a discriminative yet semantic space for object categorization, where we also embed auxiliary semantic entities such as supercategories and attributes.  ...  We propose a unified semantic model where we can learn to place categories, supercategories, and attributes as points (or vectors) in a hypothetical common semantic space.  ... 
arXiv:1411.5879v2 fatcat:biyuqqbndndc5hbymwdjuvg5aq

Learning joint representation for community question answering with tri-modal DBM

Baolin Peng, Wenge Rong, Yuanxin Ouyang, Chao Li, Zhang Xiong
2014 Proceedings of the 23rd International Conference on World Wide Web - WWW '14 Companion  
Traditionally used methods such as bag-of-words or latent semantic models consider queries, questions and answers in a same feature space.  ...  Answers dataset reveal using these unified representation to train a classifier judging semantic matching level between query and question outperforms models using bag-of-words or LSA representation significantly  ...  Afterwards another DBM is used to get a unified representation in a joint space. Experimental study on Yahoo!  ... 
doi:10.1145/2567948.2577341 dblp:conf/www/PengROLX14 fatcat:hzimjtp6qrfavmobn7feconszi

Towards a unified model of outdoor and indoor spaces

Sari Haj Hussein, Hua Lu, Torben Bach Pedersen
2012 Proceedings of the 20th International Conference on Advances in Geographic Information Systems - SIGSPATIAL '12  
This paper presents a unified model of outdoor and indoor spaces and receptor deployments in these spaces.  ...  What makes this impossible is the current absence of a unified account of these two types of spaces both in terms of modeling and reasoning about the models.  ...  Our work distinguishes itself from those aforementioned by capturing both O-and I-spaces in a unified model.  ... 
doi:10.1145/2424321.2424405 dblp:conf/gis/HusseinLP12 fatcat:jpzctndurrhppjri55nkjn3gua

Unified Semantic Parsing with Weak Supervision

Priyanka Agrawal, Ayushi Dalmia, Parag Jain, Abhishek Bansal, Ashish Mittal, Karthik Sankaranarayanan
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
To overcome this, we propose a novel framework to build a unified multi-domain enabled semantic parser trained only with weak supervision (denotations).  ...  Weakly supervised training is particularly arduous as the program search space grows exponentially in a multi-domain setting.  ...  Unified Model for multiple domains For the unified semantic parser, we use the same sequence-to-sequence model described in Section 3.1.  ... 
doi:10.18653/v1/p19-1473 dblp:conf/acl/AgrawalDJBMS19 fatcat:shwrsvoqfbag7kzhtrc7gvvm64

Semantic Modeling Approach of 3D City Models and Applications in Visual Exploration

Weiping Xu, Qing Zhu, Yeting Zhang
2010 International Journal of Virtual Reality  
Next, for the promotion of semantic modeling by this model, a semi-automatic process of semantic enrichment is implemented in a data integration tool.  ...  One is indoor routing, which adopts this model to extract the geometric path and thus enrich traditional semantic-enhanced navigation routine; another case is unified profiler, where semantics are intergrated  ...  CONCLUSION This paper presents a semantic model based on CityGML, which is extended to support geological model and specified Spaces such as Stair and Corridor for indoor navigation.  ... 
doi:10.20870/ijvr.2010.9.3.2781 fatcat:d4lk3xvo5vbzxprwiejznvmwem

Towards a Semantically Unified Environmental Information Space [chapter]

Saša Nešić, Andrea Emilio Rizzoli, Ioannis N. Athanasiadis
2011 IFIP Advances in Information and Communication Technology  
Semantic annotations and semantic links will then enable semantic discovery of environmental data over such unified information space.  ...  data by typed (semantic) links will enable the integration of disconnected environmental data sets into the semantically unified environmental information space.  ...  unified environmental information space is a universal data representation model.  ... 
doi:10.1007/978-3-642-22285-6_44 fatcat:5d55uyzulnd67pmzki5lo3qqdm

Unified Semantic Typing with Meaningful Label Inference [article]

James Y. Huang, Bangzheng Li, Jiashu Xu, Muhao Chen
2022 arXiv   pre-print
In this paper, we present UniST, a unified framework for semantic typing that captures label semantics by projecting both inputs and labels into a joint semantic embedding space.  ...  In addition, multiple semantic typing tasks can be jointly trained within the unified framework, leading to a single compact multi-tasking model that performs comparably to dedicated single-task models  ...  Hence, UNIST provides a possible solution for learning a compact, unified model with a joint semantic embedding space across different semantic typing tasks.  ... 
arXiv:2205.01826v1 fatcat:mc3ck4e4dvhudamcx3eyt55r2u

Lifelong Zero-Shot Learning

Kun Wei, Cheng Deng, Xu Yang
2020 Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence  
Specifically, considering those datasets containing different semantic embeddings, we utilize Variational Auto-Encoder to obtain unified semantic representations.  ...  Then, we leverage selective retraining strategy to preserve the trained weights of previous tasks and avoid negative transfer when fine-tuning the entire model.  ...  With the unified semantic embeddings, the latent space of different tasks is learned and fixed respectively.  ... 
doi:10.24963/ijcai.2020/77 dblp:conf/ijcai/WeiDY20 fatcat:xsnaenqalbhqxftm2kn4ug7com

Cross-Media Retrieval via Semantic Entity Projection [chapter]

Lei Huang, Yuxin Peng
2016 Lecture Notes in Computer Science  
To address this challenging problem, most existing approaches project heterogeneous features into a unified feature space to facilitate their similarity computation.  ...  Then, an entity projection is learned by minimizing both cross-media correlation error and single-media reconstruction error from low-level features to the entity level, with which a unified feature space  ...  TTI projects heterogeneous features into a unified latent topic space without the exact semantic meanings. GMA also generates a unified feature space without explicit semantic meanings.  ... 
doi:10.1007/978-3-319-27671-7_23 fatcat:zhy4dmfgarho5oe2pwfahmvudu

Topic Models, Latent Space Models, Sparse Coding, and All That: A Systematic Understanding of Probabilistic Semantic Extraction in Large Corpus

Eric P. Xing
2012 Annual Meeting of the Association for Computational Linguistics  
Probabilistic topic models have recently gained much popularity in informational retrieval and related areas. Via such models, one can project high-dimensional objects  ...  such as text documents into a low dimensional space where their latent semantics are captured and modeled; can integrate multiple sources of information-to "share statistical strength" among components  ...  I will offer a simple and unifying view of all these techniques under the framework multi-view latent space embedding, and online the roadmap of model extension and algorithmic design to-ward different  ... 
dblp:conf/acl/Xing12 fatcat:zhfppnbuvrg5bozhpppz3kqbui
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