Topic
Semantic similarity
About: Semantic similarity is a research topic. Over the lifetime, 14605 publications have been published within this topic receiving 364659 citations. The topic is also known as: semantic relatedness.
Papers published on a yearly basis
Papers
More filters
01 Jan 2005
TL;DR: This paper presents a general-purpose knowledge integration framework that employs Bayesian networks in integrating both low-level and semantic features, and demonstrates that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data.
Abstract: Current research in content-based semantic image understanding is largely confined to exemplar-based approaches built on low-level feature extraction and classification. The ability to extract both low-level and semantic features and perform knowledge integration of different types of features is expected to raise semantic image understanding to a new level. Belief networks, or Bayesian networks (BN), have proven to be an effective knowledge representation and inference engine in artificial intelligence and expert systems research. Their effectiveness is due to the ability to explicitly integrate domain knowledge in the network structure and to reduce a joint probability distribution to conditional independence relationships. In this paper, we present a general-purpose knowledge integration framework that employs BN in integrating both low-level and semantic features. The efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images. The first application aims at detecting main photographic subjects in an image, the second aims at selecting the most appealing image in an event, and the third aims at classifying images into indoor or outdoor scenes. With these diverse examples, we demonstrate that effective inference engines can be built within this powerful and flexible framework according to specific domain knowledge and available training data to solve inherently uncertain vision problems.
138 citations
TL;DR: It is argued that the comprehension of visual narrative is guided by an interaction between structure and meaning, and a combination of narrative structure and semantic relatedness can facilitate semantic processing of upcoming panels.
138 citations
13 Feb 2005
TL;DR: This paper compares the performance of several automatic evaluation metrics using a corpus of automatically generated paraphrases and shows that these evaluation metrics can at least partially measure adequacy, but are not good measures of fluency.
Abstract: Recent years have seen increasing interest in automatic metrics for the evaluation of generation systems. When a system can generate syntactic variation, automatic evaluation becomes more difficult. In this paper, we compare the performance of several automatic evaluation metrics using a corpus of automatically generated paraphrases. We show that these evaluation metrics can at least partially measure adequacy (similarity in meaning), but are not good measures of fluency (syntactic correctness). We make several proposals for improving the evaluation of generation systems that produce variation.
137 citations
15 Dec 2003
TL;DR: A suite of methods that utilizes both the semantics of the identifiers of WSDL descriptions and the structure of their operations, messages and data types to assess the similarity of two WSDD files are described and experimentally evaluated.
Abstract: The web-services stack of standards is designed to support the reuse and interoperation of software components on the web. A critical step in the process of developing applications based on web services is service discovery, i.e., the identification of existing web services that can potentially be used in the context of a new web application. UDDI, the standard API for publishing web-services specifications, provides a simple browsing-by-business-category mechanism for developers to review and select published services. To support programmatic service discovery, we have developed a suite of methods that utilizes both the semantics of the identifiers of WSDL descriptions and the structure of their operations, messages and data types to assess the similarity of two WSDL files. Given only a textual description of the desired service, a semantic information-retrieval method can be used to identify and order the most similar service-description files. This step assesses the similarity of the provided description of the desired service with the available services. If a (potentially partial) specification of the desired service behavior is also available, this set of likely candidates can be further refined by a semantic structure-matching step assessing the structural similarity of the desired vs. the retrieved services and the semantic similarity of their identifier. In this paper, we describe and experimentally evaluate our suite of service-similarity assessment methods.
136 citations
25 Aug 2015
TL;DR: In this article, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead, which is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.
Abstract: ROUGE is a widely adopted, automatic evaluation measure for text summarization. While it has been shown to correlate well with human judgements, it is biased towards surface lexical similarities. This makes it unsuitable for the evaluation of abstractive summarization, or summaries with substantial paraphrasing. We study the effectiveness of word embeddings to overcome this disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps, word embeddings are used to compute the semantic similarity of the words used in summaries instead. Our experimental results show that our proposal is able to achieve better correlations with human judgements when measured with the Spearman and Kendall rank coefficients.
136 citations