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Semantic extraction
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Semantic extraction from images is a topical problem and is applied in many different semantic retrieval systems. In this paper, a method of image semantic retrieval is proposed based on a set of images similar to the input image. Since then, the semantics of the image are queried on the ontology by the visual word vector.
20p
vimurdoch
18-09-2023
5
3
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The aim of this work is to leverage relational information extracted from biomedical literature using a novel synthesis of unsupervised pretraining, representational composition, and supervised machine learning for drug safety monitoring.
12p
visteverogers
24-06-2023
6
2
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We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents.
10p
visteverogers
24-06-2023
3
2
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Unlocking the data contained within both structured and unstructured components of electronic health records (EHRs) has the potential to provide a step change in data available for secondary research use, generation of actionable medical insights, hospital management, and trial recruitment. To achieve this, we implemented SemEHR, an open source semantic search and analytics tool for EHRs.
8p
visteverogers
24-06-2023
3
2
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This article describes an ensembling system to automatically extract adverse drug events and drug related entities from clinical narratives, which was developed for the 2018 n2c2 Shared Task Track 2. Materials and Methods: We designed a neural model to tackle both nested (entities embedded in other entities) and polysemous entities (entities annotated with multiple semantic types) based on MIMIC III discharge summaries.
9p
vighostrider
25-05-2023
3
2
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Lecture Introduction to Machine learning and Data mining: Lesson 1. This lesson provides students with content about: data collection and currency processing; recovery time; reporting data collection system; extract semantic symbols; convert data text;... Please refer to the detailed content of the lecture!
30p
hanlamcoman
26-11-2022
17
5
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Part 2 of book "Speech and Language Processing: An introduction to natural language processing" provide with knowledge about: statistical parsing; language and complexity; features and unification; representing meaning; computational semantics; lexical semantics; computational lexical semantics; computational discourse; information extraction; question answering and summarization; dialogue and conversational agents;...
535p
britaikridanik
06-07-2022
27
2
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Part 2 of the document "Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition" provide with knowledge about: dependency parsing; logical representations of sentence meaning; computational semantics and semantic parsing; information extraction; word senses and WordNet; semantic role labeling; lexicons for sentiment, affect, and connotation;...
336p
britaikridanik
05-07-2022
18
5
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There are many applications related to semantic web, information retrieval, information extraction, and question answering applying ontologies in recent years. To avoid the conceptual and terminological confusion, an ontology is built as a taxonomy ontology which identifies and distinguishes concepts as well as terminology.
8p
vivelvet2711
06-09-2021
9
1
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The proliferation of the scientific literature in the field of biomedicine makes it difficult to keep abreast of current knowledge, even for domain experts. While general Web search engines and specialized information retrieval (IR) systems have made important strides in recent decades, the problem of accurate knowledge extraction from the biomedical literature is far from solved.
14p
vikentucky2711
26-11-2020
14
1
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Controlled vocabularies such as the Unified Medical Language System (UMLS®) and Medical Subject Headings (MeSH®) are widely used for biomedical natural language processing (NLP) tasks. However, the standard terminology in such collections suffers from low usage in biomedical literature, e.g. only 13% of UMLS terms appear in MEDLINE®.
10p
vikentucky2711
26-11-2020
8
0
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Entity coreference is common in biomedical literature and it can affect text understanding systems that rely on accurate identification of named entities, such as relation extraction and automatic summarization.
16p
vioklahoma2711
19-11-2020
11
1
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In this paper, we present a novel method to measure the semantic similarity between elements in different RDF ontologies. This measure is designed so as to enable extraction of information encoded in RDF element descriptions and to take into account the element relationships with its ancestors and children. We evaluate the proposed measures in the context of matching two RDF ontologies to determine the number of matches between them and then compare with human estimation and the related methods.
9p
quenchua9
20-11-2020
21
3
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Relation extraction is an essential procedure in literature mining. It focuses on extracting semantic relations between parts of text, called mentions. Biomedical literature includes an enormous amount of textual descriptions of biological entities, their interactions and results of related experiments.
16p
vioklahoma2711
19-11-2020
13
1
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This paper has presented LOD-ABOG framework which shows that current LOD sources and technologies are a promising solution to automate the process of biomedical ontology generation and extract relations to a greater extent. In addition, unlike existing frameworks which require domain experts in ontology development process, the proposed approach requires involvement of them only for improvement purpose at the end of ontology life cycle.
13p
viconnecticut2711
28-10-2020
14
0
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Both intra- and inter-sentential semantic relations in biomedical texts provide valuable information for biomedical research. However, most existing methods either focus on extracting intra-sentential relations and ignore intersentential ones or fail to extract inter-sentential relations accurately and regard the instances containing entity relations as being independent, which neglects the interactions between relations.
14p
vicolorado2711
22-10-2020
5
0
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In the era of information overload, natural language processing (NLP) techniques are increasingly needed to support advanced biomedical information management and discovery applications. In this paper, we present an in-depth description of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguistic principles and UMLS domain knowledge.
28p
vicolorado2711
22-10-2020
9
0
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Semantic resources such as knowledge bases contains high-qualitystructured knowledge and therefore require significant effort from domain experts. Using the resources to reinforce the information retrieval from the unstructured text may further exploit the potentials of such unstructured text resources and their curated knowledge.
18p
vicolorado2711
22-10-2020
32
0
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Furthermore, experiments on the task of RE proved that data representation is one of the most influential factors to the model’s performance but still has many limitations. We propose a compositional embedding that combines several dominant linguistic as well as architectural features and dependency tree normalization techniques for generating rich representations for both words and dependency relations in the SDP
82p
tamynhan1
13-06-2020
14
2
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The image retrieval and semantic extraction play an important role in the multimedia systems such as geographic information system, hospital information system, digital library system, etc. Therefore, the research and development of semantic-based image retrieval (SBIR) systems have become extremely important and urgent. Major recent publications are included covering different aspects of the research in this area, including building data models, low-level image feature extraction, and deriving high-level semantic features.
19p
12120609
23-03-2020
23
1
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