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
30 Mar 2009
TL;DR: This paper compares a number of knowledge-based and corpus-based measures of text similarity, evaluates the effect of domain and size on the corpus- based measures, and introduces a novel technique to improve the performance of the system by integrating automatic feedback from the student answers.
Abstract: In this paper, we explore unsupervised techniques for the task of automatic short answer grading. We compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating automatic feedback from the student answers. Overall, our system significantly and consistently outperforms other unsupervised methods for short answer grading that have been proposed in the past.
277 citations
TL;DR: It is argued that a new class of prediction-based models that are trained on a text corpus and that measure semantic similarity between words bridge the gap between traditional approaches to distributional semantics and psychologically plausible learning principles.
277 citations
17 Sep 2007
TL;DR: The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
Abstract: This paper describes an unsupervised graph-based method for word sense disambiguation, and presents comparative evaluations using several measures of word semantic similarity and several algorithms for graph centrality. The results indicate that the right combination of similarity metrics and graph centrality algorithms can lead to a performance competing with the state-of-the-art in unsupervised word sense disambiguation, as measured on standard data sets.
275 citations
01 Oct 2012
TL;DR: This work presents a histogram-based representation for time series data, similar to the “bag of words” approach that is widely accepted by the text mining and information retrieval communities, and shows that it outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets.
Abstract: For more than a decade, time series similarity search has been given a great deal of attention by data mining researchers. As a result, many time series representations and distance measures have been proposed. However, most existing work on time series similarity search relies on shape-based similarity matching. While some of the existing approaches work well for short time series data, they typically fail to produce satisfactory results when the sequence is long. For long sequences, it is more appropriate to consider the similarity based on the higher-level structures. In this work, we present a histogram-based representation for time series data, similar to the "bag of words" approach that is widely accepted by the text mining and information retrieval communities. We performed extensive experiments and show that our approach outperforms the leading existing methods in clustering, classification, and anomaly detection on dozens of real datasets. We further demonstrate that the representation allows rotation-invariant matching in shape datasets.
272 citations
19 Aug 2017
TL;DR: This paper presents a novel approach for entity alignment via joint knowledge embeddings that jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities.
Abstract: Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on realworld datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.
272 citations