In the field of naturallanguageprocessing, Knowledge Base Question Answering (KBQA) is a challenging task that involves accurately retrieving answers from structured knowledge. Existing methods often face issues whe...
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The proceedings contain 23 papers. The topics discussed include: graph-based and graph-supported machine learning and deep learning methods;graph-based and graph-supported deep learning (e.g., graph-based recurrent an...
ISBN:
(纸本)9781950737864
The proceedings contain 23 papers. The topics discussed include: graph-based and graph-supported machine learning and deep learning methods;graph-based and graph-supported deep learning (e.g., graph-based recurrent and recursive networks);exploration of capabilities and limitations of graph-basedmethods being applied to neural networks;graph-based techniques for text summarization, simplification, and paraphrasing;graph-based techniques for document navigation and visualization;and using graphs-basedmethods to populate ontologies using textual data.
The proceedings contain 11 papers. The topics discussed include: event-centered information retrieval using kernels on event graphs;reconstructing big semantic similarity networks;graph-based unsupervised learning of ...
ISBN:
(纸本)9781937284978
The proceedings contain 11 papers. The topics discussed include: event-centered information retrieval using kernels on event graphs;reconstructing big semantic similarity networks;graph-based unsupervised learning of word similarities using heterogeneous feature types;understanding seed selection in bootstrapping;graph-structures matching for review relevance identi?cation;automatic extraction of reasoning chains from textual reports;graph-based Approaches for organization entity resolution in MapReduce;and a graph-based approach to skill extraction from text.
To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient inform...
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To address the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have become a critical tool for integrating large volumes of heterogeneous data to enable efficient information retrieval and automated knowledge discovery. However, transforming unstructured scientific literature into KGs remains a significant challenge, with previous methods unable to achieve human-level accuracy. Here we used an information extraction pipeline that won first place in the LitCoin naturallanguageprocessing Challenge (2022) to construct a large-scale KG named iKraph using all PubMed abstracts. The extracted information matches human expert annotations and significantly exceeds the content of manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. This KG facilitates rigorous performance evaluation of automated knowledge discovery, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and applied it to real-time COVID-19 drug repurposing from March 2020 to May 2023. Our method identified around 1,200 candidate drugs in the first 4 months, with one-third of those discovered in the first 2 months later supported by clinical trials or PubMed publications. These outcomes are very challenging to attain through alternative approaches that lack a thorough understanding of the existing literature. A cloud-based platform (https://***) was developed for academic users to access this rich structured data and associated tools.
The proceedings contain 8 papers. The topics discussed include: normalized entity graph for computing local coherence;exploiting timegraphs in temporal relation classification;multi-document summarization using bipart...
ISBN:
(纸本)9781937284961
The proceedings contain 8 papers. The topics discussed include: normalized entity graph for computing local coherence;exploiting timegraphs in temporal relation classification;multi-document summarization using bipartite graphs;a novel two-stage framework for extracting opinionated sentences from news articles;constructing coherent event hierarchies from news stories;semi-supervised graph-based genre classification for web pages;the modular community structure of linguistic predication networks;and from visualization to hypothesis construction for second language acquisition.
Objective: Osteosarcoma is a prevalent primary malignant bone tumor in children and adolescents, accounting for approximately 5 % of childhood malignancies. Because of its rarity and biological complexity, treatment b...
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Zero-shot stance detection (ZSSD) is a challenging task that requires classifying stances towards unseen targets without large, well-curated training datasets. Existing data augmentation methods for ZSSD often suffer ...
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The proceedings contain 8 papers. The topics discussed include: a new parametric estimation method for graph-based clustering;extracting signed social networks from text;using link analysis to discover interesting mes...
ISBN:
(纸本)9781937284374
The proceedings contain 8 papers. The topics discussed include: a new parametric estimation method for graph-based clustering;extracting signed social networks from text;using link analysis to discover interesting messages spread across twitter;graphbased similarity measures for synonym extraction from parsed text;semantic relatedness for biomedical word sense disambiguation;identifying untyped relation mentions in a corpus given an ontology;cause-effect relation learning;and bringing the associative ability to social tag recommendation.
The proceedings contain 17 papers. The topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investig...
ISBN:
(纸本)1932432779
The proceedings contain 17 papers. The topics discussed include: graph-based clustering for computational linguistics: a survey;towards the automatic creation of a wordnet from a term-based lexical network;an investigation on the influence of frequency on the lexical organization of verbs;robust and efficient page rank for word sense disambiguation;hierarchical spectral partitioning of bipartite graphs to cluster dialects and identify distinguishing features;a character-based intersection graph approach to linguistic phylogeny;spectral approaches to learning in the graph domain;cross-lingual comparison between distributionally determined word similarity networks;co-occurrence cluster features for lexical substitutions in context;contextually-mediated semantic similarity graphs for topic segmentation;and experiments with CST-based multidocument summarization.
The proceedings contain 15 papers. The topics discussed include: a graphical framework for contextual search and name disambiguation in email;graphbased semi-supervised approach for information extraction;graph-based...
The proceedings contain 15 papers. The topics discussed include: a graphical framework for contextual search and name disambiguation in email;graphbased semi-supervised approach for information extraction;graph-based text representation for novelty detection;measuring aboutness of an entity in a text;a study of two graph algorithms in topic-driven summarization;similarity between pairs of co-indexed trees for textual entailment recognition;learning of graph-based question answering rules;seeing stars when there aren’t many stars: graph-based semi-supervised learning for sentiment categorization;and random-walk term weighting for improved text classification.
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