6,716 research outputs found
Word Embedding based Correlation Model for Question/Answer Matching
With the development of community based question answering (Q&A) services, a
large scale of Q&A archives have been accumulated and are an important
information and knowledge resource on the web. Question and answer matching has
been attached much importance to for its ability to reuse knowledge stored in
these systems: it can be useful in enhancing user experience with recurrent
questions. In this paper, we try to improve the matching accuracy by overcoming
the lexical gap between question and answer pairs. A Word Embedding based
Correlation (WEC) model is proposed by integrating advantages of both the
translation model and word embedding, given a random pair of words, WEC can
score their co-occurrence probability in Q&A pairs and it can also leverage the
continuity and smoothness of continuous space word representation to deal with
new pairs of words that are rare in the training parallel text. An experimental
study on Yahoo! Answers dataset and Baidu Zhidao dataset shows this new
method's promising potential.Comment: 8 pages, 2 figure
Mind the Gap: Another look at the problem of the semantic gap in image retrieval
This paper attempts to review and characterise the problem of the semantic gap in image retrieval and the attempts being made to bridge it. In particular, we draw from our own experience in user queries, automatic annotation and ontological techniques. The first section of the paper describes a characterisation of the semantic gap as a hierarchy between the raw media and full semantic understanding of the media's content. The second section discusses real users' queries with respect to the semantic gap. The final sections of the paper describe our own experience in attempting to bridge the semantic gap. In particular we discuss our work on auto-annotation and semantic-space models of image retrieval in order to bridge the gap from the bottom up, and the use of ontologies, which capture more semantics than keyword object labels alone, as a technique for bridging the gap from the top down
Semantic Modeling of Analytic-based Relationships with Direct Qualification
Successfully modeling state and analytics-based semantic relationships of
documents enhances representation, importance, relevancy, provenience, and
priority of the document. These attributes are the core elements that form the
machine-based knowledge representation for documents. However, modeling
document relationships that can change over time can be inelegant, limited,
complex or overly burdensome for semantic technologies. In this paper, we
present Direct Qualification (DQ), an approach for modeling any semantically
referenced document, concept, or named graph with results from associated
applied analytics. The proposed approach supplements the traditional
subject-object relationships by providing a third leg to the relationship; the
qualification of how and why the relationship exists. To illustrate, we show a
prototype of an event-based system with a realistic use case for applying DQ to
relevancy analytics of PageRank and Hyperlink-Induced Topic Search (HITS).Comment: Proceedings of the 2015 IEEE 9th International Conference on Semantic
Computing (IEEE ICSC 2015
情報検索における意味的ギャップの解消 : トピックモデルを用いた先進的画像探索
Tohoku University徳山豪課
Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches
Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches
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