363 research outputs found
Enriching ontological user profiles with tagging history for multi-domain recommendations
Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites
Semantic Systems. The Power of AI and Knowledge Graphs
This open access book constitutes the refereed proceedings of the 15th International Conference on Semantic Systems, SEMANTiCS 2019, held in Karlsruhe, Germany, in September 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 88 submissions. They cover topics such as: web semantics and linked (open) data; machine learning and deep learning techniques; semantic information management and knowledge integration; terminology, thesaurus and ontology management; data mining and knowledge discovery; semantics in blockchain and distributed ledger technologies
Proceedings, MSVSCC 2018
Proceedings of the 12th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 19, 2018 at VMASC in Suffolk, Virginia. 155 pp
Visual Storytelling: Captioning of Image Sequences
In the space of automated captioning, the task of visual storytelling is a dimension. Given sequences of images as inputs, visual storytelling (VIST) is about automatically generating textual narratives as outputs. Automatically producing stories for an order of pictures or video frames have several potential applications in diverse domains ranging from multimedia consumption to autonomous systems. The task has evolved over recent years and is moving into adolescence. The availability of a dedicated VIST dataset for the task has mainstreamed research for visual storytelling and related sub-tasks.
This thesis work systematically reports the developments of standard captioning as a parent task with accompanying facets like dense captioning and gradually delves into the domain of visual storytelling. Existing models proposed for VIST are described by examining respective characteristics and scope. All the methods for VIST adapt from the typical encoder-decoder style design, owing to its success in addressing the standard image captioning task. Several subtle differences in the underlying intentions of these methods for approaching the VIST are subsequently summarized.
Additionally, alternate perspectives around the existing approaches are explored by re-modeling and modifying their learning mechanisms. Experiments with different objective functions are reported with subjective comparisons and relevant results. Eventually, the sub-field of character relationships within storytelling is studied and a novel idea called character-centric storytelling is proposed to account for prospective characters in the extent of data modalities
Sensor Independent Deep Learning for Detection Tasks with Optical Satellites
The design of optical satellite sensors varies widely, and this variety is mirrored in the data they produce. Deep learning has become a popular method for automating tasks in remote sensing, but currently it is ill-equipped to deal with this diversity of satellite data. In this work, sensor independent deep learning models are proposed, which are able to ingest data from multiple satellites without retraining. This strategy is applied to two tasks in remote sensing: cloud masking and crater detection. For cloud masking, a new dataset---the largest ever to date with respect to the number of scenes---is created for Sentinel-2. Combination of this with other datasets from the Landsat missions results in a state-of-the-art deep learning model, capable of masking clouds on a wide array of satellites, including ones it was not trained on. For small crater detection on Mars, a dataset is also produced, and state-of-the-art deep learning approaches are compared. By combining datasets from sensors with different resolutions, a highly accurate sensor independent model is trained. This is used to produce the largest ever database of crater detections for any solar system body, comprising 5.5 million craters across Isidis Planitia, Mars using CTX imagery. Novel geospatial statistical techniques are used to explore this database of small craters, finding evidence for large populations of distant secondary impacts. Across these problems, sensor independence is shown to offer unique benefits, both regarding model performance and scientific outcomes, and in the future can aid in many problems relating to data fusion, time series analysis, and on-board applications. Further work on a wider range of problems is needed to determine the generalisability of the proposed strategies for sensor independence, and extension from optical sensors to other kinds of remote sensing instruments could expand the possible applications of this new technique
Understanding comparative questions and retrieving argumentative answers
Making decisions is an integral part of everyday life, yet it can be a difficult and complex process. While peoplesβ wants and needs are unlimited, resources are often scarce, making it necessary to research the possible alternatives and weigh the pros and cons before making a decision. Nowadays, the Internet has become the main source of information when it comes to comparing alternatives, making search engines the primary means for collecting new information. However, relying only on term matching is not sufficient to adequately address requests for comparisons. Therefore, search systems should go beyond this approach to effectively address comparative information needs. In this dissertation, I explore from different perspectives how search systems can respond to comparative questions. First, I examine approaches to identifying comparative questions and study their underlying information needs. Second, I investigate a methodology to identify important constituents of comparative questions like the to-be-compared options and to detect the stance of answers towards these comparison options. Then, I address ambiguous comparative search queries by studying an interactive clarification search interface. And finally, addressing answering comparative questions, I investigate retrieval approaches that consider not only the topical relevance of potential answers but also account for the presence of arguments towards the comparison options mentioned in the questions. By addressing these facets, I aim to provide a comprehensive understanding of how to effectively satisfy the information needs of searchers seeking to compare different alternatives
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λ° μ°κ΅¬ λ¨μμ νμ©ν μ μμμ 보μ΄κ³ μ νλ€. λ³Έ μ°κ΅¬κ° λ€λ₯Έ μΈμ΄ λ° λλ©μΈμμλ κ³ μ§μ μΈ μ€μμ± λ¬Έμ λ₯Ό ν΄μνλ λ°μ λμμ΄ λκΈΈ λ°λΌλ©°, μ΄λ₯Ό μν΄ μ°κ΅¬λ₯Ό μ§ννλ λ°μ νμ©λ 리μμ€, κ²°κ³Όλ¬Ό λ° μ½λλ€μ 곡μ ν¨μΌλ‘μ¨ νκ³μ λ°μ μ μ΄λ°μ§νκ³ μ νλ€.Ambiguity in the language is inevitable. It is because, albeit language is a means of communication, a particular concept that everyone thinks of cannot be conveyed in a perfectly identical manner. As this is an inevitable factor, ambiguity in language understanding often leads to breakdown or failure of communication.
There are various hierarchies of language ambiguity. However, not all ambiguity needs to be resolved. Different aspects of ambiguity exist for each domain and task, and it is crucial to define the boundary after recognizing the ambiguity that can be well-defined and resolved.
In this dissertation, we investigate the types of ambiguity that appear in spoken language processing, especially in intention understanding, and conduct research to define and resolve it. Although this phenomenon occurs in various languages, its degree and aspect depend on the language investigated. The factor we focus on is cases where the ambiguity comes from the gap between the amount of information in the spoken language and the text.
Here, we study the Korean language, which often shows different sentence structures and intentions depending on the prosody. In the Korean language, a text is often read with multiple intentions due to multi-functional sentence enders, frequent pro-drop, wh-intervention, etc. We first define this type of ambiguity and construct a corpus that helps detect ambiguous sentences, given that such utterances can be problematic for intention understanding.
In constructing a corpus for intention understanding, we consider the directivity and rhetoricalness of a sentence. They make up a criterion for classifying the intention of spoken language into a statement, question, command, rhetorical question, and rhetorical command. Using the corpus annotated with sufficiently high agreement on a spoken language corpus, we show that colloquial corpus-based language models are effective in classifying ambiguous text given only textual data, and qualitatively analyze the characteristics of the task.
We do not handle ambiguity only at the text level. To find out whether actual disambiguation is possible given a speech input, we design an artificial spoken language corpus composed only of ambiguous sentences, and resolve ambiguity with various attention-based neural network architectures. In this process, we observe that the ambiguity resolution is most effective when both textual and acoustic input co-attends each feature, especially when the audio processing module conveys attention information to the text module in a multi-hop manner.
Finally, assuming the case that the ambiguity of intention understanding is resolved by proposed strategies, we present a brief roadmap of how the results can be utilized at the industry or research level. By integrating text-based ambiguity detection and speech-based intention understanding module, we can build a system that handles ambiguity efficiently while reducing error propagation. Such a system can be integrated with dialogue managers to make up a task-oriented dialogue system capable of chit-chat, or it can be used for error reduction in multilingual circumstances such as speech translation, beyond merely monolingual conditions.
Throughout the dissertation, we want to show that ambiguity resolution for intention understanding in prosody-sensitive language can be achieved and can be utilized at the industry or research level. We hope that this study helps tackle chronic ambiguity issues in other languages ββor other domains, linking linguistic science and engineering approaches.1 Introduction 1
1.1 Motivation 2
1.2 Research Goal 4
1.3 Outline of the Dissertation 5
2 Related Work 6
2.1 Spoken Language Understanding 6
2.2 Speech Act and Intention 8
2.2.1 Performatives and statements 8
2.2.2 Illocutionary act and speech act 9
2.2.3 Formal semantic approaches 11
2.3 Ambiguity of Intention Understanding in Korean 14
2.3.1 Ambiguities in language 14
2.3.2 Speech act and intention understanding in Korean 16
3 Ambiguity in Intention Understanding of Spoken Language 20
3.1 Intention Understanding and Ambiguity 20
3.2 Annotation Protocol 23
3.2.1 Fragments 24
3.2.2 Clear-cut cases 26
3.2.3 Intonation-dependent utterances 28
3.3 Data Construction . 32
3.3.1 Source scripts 32
3.3.2 Agreement 32
3.3.3 Augmentation 33
3.3.4 Train split 33
3.4 Experiments and Results 34
3.4.1 Models 34
3.4.2 Implementation 36
3.4.3 Results 37
3.5 Findings and Summary 44
3.5.1 Findings 44
3.5.2 Summary 45
4 Disambiguation of Speech Intention 47
4.1 Ambiguity Resolution 47
4.1.1 Prosody and syntax 48
4.1.2 Disambiguation with prosody 50
4.1.3 Approaches in SLU 50
4.2 Dataset Construction 51
4.2.1 Script generation 52
4.2.2 Label tagging 54
4.2.3 Recording 56
4.3 Experiments and Results 57
4.3.1 Models 57
4.3.2 Results 60
4.4 Summary 63
5 System Integration and Application 65
5.1 System Integration for Intention Identification 65
5.1.1 Proof of concept 65
5.1.2 Preliminary study 69
5.2 Application to Spoken Dialogue System 75
5.2.1 What is 'Free-running' 76
5.2.2 Omakase chatbot 76
5.3 Beyond Monolingual Approaches 84
5.3.1 Spoken language translation 85
5.3.2 Dataset 87
5.3.3 Analysis 94
5.3.4 Discussion 95
5.4 Summary 100
6 Conclusion and Future Work 103
Bibliography 105
Abstract (In Korean) 124
Acknowledgment 126λ°
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