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.

Essay Assessment with Latent Semantic Analysis

Abstract: Latent semantic analysis (LSA) is an automated, statistical technique for comparing the semantic similarity of words or documents. In this article, I examine the application of LSA to automated essay scoring. I compare LSA methods to earlier statistical methods for assessing essay quality, and critically review contemporary essay-scoring systems built on LSA, including the Intelligent Essay Assessor, Summary Street, State the Essence, Apex, and Select-a-Kibitzer. Finally, I discuss current avenues of research, including LSA's application to computer-measured readability assessment and to automatic summarization of student essays.

Quantitative assessment of relationship between sequence similarity and function similarity.

Abstract: Comparative sequence analysis is considered as the first step towards annotating new proteins in genome annotation. However, sequence comparison may lead to creation and propagation of function assignment errors. Thus, it is important to perform a thorough analysis for the quality of sequence-based function assignment using large-scale data in a systematic way. We present an analysis of the relationship between sequence similarity and function similarity for the proteins in four model organisms, i.e., Arabidopsis thaliana, Saccharomyces cerevisiae, Caenorrhabditis elegans, and Drosophila melanogaster. Using a measure of functional similarity based on the three categories of Gene Ontology (GO) classifications (biological process, molecular function, and cellular component), we quantified the correlation between functional similarity and sequence similarity measured by sequence identity or statistical significance of the alignment and compared such a correlation against randomly chosen protein pairs. Various sequence-function relationships were identified from BLAST versus PSI-BLAST, sequence identity versus Expectation Value, GO indices versus semantic similarity approaches, and within genome versus between genome comparisons, for the three GO categories. Our study provides a benchmark to estimate the confidence in assignment of functions purely based on sequence similarity.

Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing

Abstract: Spoken dialogue systems typically use predefined semantic slots to parse users' natural language inputs into unified semantic representations. To define the slots, domain experts and professional annotators are often involved, and the cost can be expensive. In this paper, we ask the following question: given a collection of unlabeled raw audios, can we use the frame semantics theory to automatically induce and fill the semantic slots in an unsupervised fashion? To do this, we propose the use of a state-of-the-art frame-semantic parser, and a spectral clustering based slot ranking model that adapts the generic output of the parser to the target semantic space. Empirical experiments on a real-world spoken dialogue dataset show that the automatically induced semantic slots are in line with the reference slots created by domain experts: we observe a mean averaged precision of 69.36% using ASR-transcribed data. Our slot filling evaluations also indicate the promising future of this proposed approach.

Semantic SPARQL similarity search over RDF knowledge graphs

Abstract: RDF knowledge graphs have attracted increasing attentions these years. However, due to the schema-free nature of RDF data, it is very difficult for users to have full knowledge of the underlying schema. Furthermore, the same kind of information can be represented in diverse graph fragments. Hence, it is a huge challenge to formulate complex SPARQL expressions by taking the union of all possible structures.In this paper, we propose an effective framework to access the RDF repository even if users have no full knowledge of the underlying schema. Specifically, given a SPARQL query, the system could return as more answers that match the query based on the semantic similarity as possible. Interestingly, we propose a systematic method to mine diverse semantically equivalent structure patterns. More importantly, incorporating both structural and semantic similarities we are the first to propose a novel similarity measure, semantic graph edit distance. In order to improve the efficiency performance, we apply the semantic summary graph to summarize the knowledge graph, which supports both high-level pruning and drill-down pruning. We also devise an effective lower bound based on the TA-style access to each of the candidate sets. Extensive experiments over real datasets confirm the effectiveness and efficiency of our approach.

Query Adaptive Similarity for Large Scale Object Retrieval

Abstract: Many recent object retrieval systems rely on local features for describing an image. The similarity between a pair of images is measured by aggregating the similarity between their corresponding local features. In this paper we present a probabilistic framework for modeling the feature to feature similarity measure. We then derive a query adaptive distance which is appropriate for global similarity evaluation. Furthermore, we propose a function to score the individual contributions into an image to image similarity within the probabilistic framework. Experimental results show that our method improves the retrieval accuracy significantly and consistently. Moreover, our result compares favorably to the state-of-the-art.