33,992 research outputs found
Story Ending Generation with Incremental Encoding and Commonsense Knowledge
Generating a reasonable ending for a given story context, i.e., story ending
generation, is a strong indication of story comprehension. This task requires
not only to understand the context clues which play an important role in
planning the plot but also to handle implicit knowledge to make a reasonable,
coherent story.
In this paper, we devise a novel model for story ending generation. The model
adopts an incremental encoding scheme to represent context clues which are
spanning in the story context. In addition, commonsense knowledge is applied
through multi-source attention to facilitate story comprehension, and thus to
help generate coherent and reasonable endings. Through building context clues
and using implicit knowledge, the model is able to produce reasonable story
endings. context clues implied in the post and make the inference based on it.
Automatic and manual evaluation shows that our model can generate more
reasonable story endings than state-of-the-art baselines.Comment: Accepted in AAAI201
Annotation graphs as a framework for multidimensional linguistic data analysis
In recent work we have presented a formal framework for linguistic annotation
based on labeled acyclic digraphs. These `annotation graphs' offer a simple yet
powerful method for representing complex annotation structures incorporating
hierarchy and overlap. Here, we motivate and illustrate our approach using
discourse-level annotations of text and speech data drawn from the CALLHOME,
COCONUT, MUC-7, DAMSL and TRAINS annotation schemes. With the help of domain
specialists, we have constructed a hybrid multi-level annotation for a fragment
of the Boston University Radio Speech Corpus which includes the following
levels: segment, word, breath, ToBI, Tilt, Treebank, coreference and named
entity. We show how annotation graphs can represent hybrid multi-level
structures which derive from a diverse set of file formats. We also show how
the approach facilitates substantive comparison of multiple annotations of a
single signal based on different theoretical models. The discussion shows how
annotation graphs open the door to wide-ranging integration of tools, formats
and corpora.Comment: 10 pages, 10 figures, Towards Standards and Tools for Discourse
Tagging, Proceedings of the Workshop. pp. 1-10. Association for Computational
Linguistic
Analyzing the solutions of DEA through information visualization and data mining techniques: SmartDEA framework
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, SmartDEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for benchmarking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework
Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims
Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area
Visualizing recommendations to support exploration, transparency and controllability
Research on recommender systems has traditionally focused on the development of algorithms to improve accuracy of recommendations. So far, little research has been done to enable user interaction with such systems as a basis to support exploration and control by end users. In this paper, we present our research on the use of information visualization techniques to interact with recommender systems. We investigated how information visualization can improve user understanding of the typically black-box rationale behind recommendations in order to increase their perceived relevance and meaning and to support exploration and user involvement in the recommendation process. Our study has been performed using TalkExplorer, an interactive visualization tool developed for attendees of academic conferences. The results of user studies performed at two conferences allowed us to obtain interesting insights to enhance user interfaces that integrate recommendation technology. More specifically, effectiveness and probability of item selection both increase when users are able to explore and interrelate multiple entities - i.e. items bookmarked by users, recommendations and tags. Copyright © 2013 ACM
- …