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Showing 1 to 20 of 63 for “"Semantic similarity"”.
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Methods for Measuring Semantic Similarity of Texts
Measuring semantic similarity is a task needed in many Natural Language Processing (NLP) applications. For example, in Machine Translation evaluation, semantic similarity is used to assess the quality of the machine translation output by measuring the degree of equivalence between a reference …
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Explorations in the distributional and semantic similarity of words
… word' of the list) leads to the question: How semantically similar to the head word are the tail words; that is: how similar are their meanings to its meaning? And can we do better? The experiment was done on nearly 18,000 most frequent nouns in a Finnish newsgroup corpus. These nouns are …
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Measuring Short Text Semantic Similarity with Deep Learning Models
… method, for the problem of measuring short text semantic similarity in NLP area. In particular, we propose a novel deep neural network architecture to identify semantic similarity for pairs of question sentence. In the proposed network, multiple channels of knowledge for pairs of question text …
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Context for semantic similarity calculation in scenario template creation
… task, the estimation of verb-centric text span similarity is the key. Various approaches have been proposed. Contextual information by intuition would enhance text span similarity estimation. But it has yet to be well exploited. In this thesis, I first devise an intrinsic similarity measure for …
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Representation Learning beyond Semantic Similarity: Character-aware and Function-specific Approaches
… encodes general information about word similarity. Words or phrases with similar meaning obtain similar representations in a vector space constructed for this purpose. This established methodology excels for morphologically-simple languages such as English, and in data-rich settings. …
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Exploiting Semantic Similarity Between Citation Contexts For Direct Citation Weighting And Residual Citation
This study used the semantic similarity between citation contexts to develop one scheme for weighting direct citations, and another scheme for allocating residual citations to a publication from its nth citation generation level publication. A relationship between the new direct citation weighting …
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GOGO: An Improved Algorithm to Measure the Semantic Similarity Between Gene Ontology Terms
<p>Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid …
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A new semantic similarity join method using diffusion maps and long string table attributes
… Most of the previous work has concentrated on similarity join when the join attribute is a short string attribute, such as person name and address. However, most databases contain long string attributes as well, such as product description and paper abstract, and up to our knowledge, no work …
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An exploratory study using the predicate-argument structure to develop methodology for measuring semantic similarity of radiology sentences
… studies consistently have problems addressing semantics and none have addressed the issue of semantic similarity (or synonymy) to achieve data reduction. To achieve data reduction, a successful methodology for data reduction is dependent on a framework that can represent currently popular …
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A semantic contribution to verbal short-term memory: a test of operational definitions of ‘semantic similarity’ and input versus output processes
… short-term memory is phonological and that semantic codes are employed in long-term memory. Semantic coding in short-term memory has been investigated to a far lesser degree than phonological codes and the findings have been inconsistent. Some theorists propose that semantic coding is …
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Enhancing Retrieval Augmented Generation Through Robust Information Retrieval
… does not necessarily correlate with higher similarity/lexical scores. We detail the internal workings of exhaustive and partial semantic similarity and lexical, rule-based, retrieval algorithms, and provide formal representation for deterministic evaluation metrics and error analysis …
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Computational Linguistic Models of Mental Spaces
… provides a detailed background of partitioned semantic relations. These relationships can be constrained by Frames and Scripts. We use pre-existing computer tools to develop a model that mimics this framework. Fauconnier's and Turner's work on Conceptual Integration and current theories of …
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Representing meaning: a feature-based model of object and action words
… and neurally plausible model of lexical-semantic representations, not only for words referring to concrete objects but also for words referring to actions and events using a common set of assumptions across domains. In order to do so, features of meaning are generated by naïve speakers, …
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Modeling False Memory in the Deese-Roediger-McDermott Paradigm: An Approach Using Holographic Declarative Memory
… (DRM) paradigm demonstrates how semantic associations can induce false recall and recognition (Roediger & McDermott, 1995). While cognitive architectures are able to model control processes, they often struggle with large-scale semantic representations which are required for …
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Automatic verification of VHDL models
… can describe timing relation between signals. A semantic similarity between temporal operators and VHDL timings and delays has been drawn and an algorithm for comparing the VHDL model and temporal specifications has been developed. Comparisons are made between the simulation results on the VHDL …
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A content analysis of student's perceptions of instructors
… Cadwell and Jenkins (1985) hypothesized that the semantic similarity of individual items was the underlying influence to the robust factor structure found in Marsh's SEEQ and other rating instruments. Their findings suggested that the synonymous wording of items within scales artificially inflates …
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Transparent Analysis of Multi-Modal Embeddings
Vector Space Models of Distributional Semantics – or Embeddings – serve as useful statistical models of word meanings, which can be applied as proxies to learn about human concepts. One of their main benefits is that not only textual, but a wide range of data types can be mapped to a space, where …
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Addressing Semantic Interoperability and Text Annotations. Concerns in Electronic Health Records using Word Embedding, Ontology and Analogy
… and laboratory reports etc. due to a lack of semantic interoperability. Hence, there is a need of semantic web technologies for addressing healthcare interoperability problems by enabling various healthcare standards from various healthcare entities (doctors, clinics, hospitals etc.) to …
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A Study on Leveraging Generative Artificial Intelligence and Text Clustering to Support Vendors
… and K-Means for grouping text feedback based on semantic similarity and on the employment of retrieval augmented generation (RAG) for extracting actionable insights. Our findings indicate a relative effectiveness of K-Means over DBSCAN in clustering feedback, but the overall effectiveness is …
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Leveraging Intermediate Artifacts to Improve Automated Trace Link Retrieval
… combinations of techniques for computing semantic similarity, scaling scores across multiple paths, and aggregating results from multiple paths. We report results from five projects, including one large industrial project. We find that leverag- ing intermediate artifacts improves the …
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