Matches in SemOpenAlex for { <https://semopenalex.org/work/W3088286051> ?p ?o ?g. }
Showing items 1 to 81 of
81
with 100 items per page.
- W3088286051 abstract "Graphs or networks have been widely used as modeling tools in Natural Language Processing (NLP), Text Mining (TM) and Information Retrieval (IR). Traditionally, the unigram bag-of-words representation is applied; that way, a document is represented as a multiset of its terms, disregarding dependencies between the terms. Although several variants and extensions of this modeling approach have been proposed (e.g., the n-gram model), the main weakness comes from the underlying term independence assumption. The order of the terms within a document is completely disregarded and any relationship between terms is not taken into account in the final task (e.g., text categorization). Nevertheless, as the heterogeneity of text collections is increasing (especially with respect to document length and vocabulary), the research community has started exploring different document representations aiming to capture more fine-grained contexts of co-occurrence between different terms, challenging the well-established unigram bag-of-words model. To this direction, graphs constitute a well-developed model that has been adopted for text representation. The goal of this tutorial is to offer a comprehensive presentation of recent methods that rely on graph-based text representations to deal with various tasks in NLP and IR. We will describe basic as well as novel graph theoretic concepts and we will examine how they can be applied in a wide range of text-related application domains.All the material associated to the tutorial will be available at: http://fragkiskosm.github.io/projects/graph_text_tutorial" @default.
- W3088286051 created "2020-10-01" @default.
- W3088286051 creator A5057695979 @default.
- W3088286051 creator A5087251228 @default.
- W3088286051 date "2017-09-01" @default.
- W3088286051 modified "2023-09-23" @default.
- W3088286051 title "Graph-based Text Representations: Boosting Text Mining, NLP and Information Retrieval with Graphs" @default.
- W3088286051 hasPublicationYear "2017" @default.
- W3088286051 type Work @default.
- W3088286051 sameAs 3088286051 @default.
- W3088286051 citedByCount "3" @default.
- W3088286051 countsByYear W30882860512019 @default.
- W3088286051 countsByYear W30882860512020 @default.
- W3088286051 crossrefType "proceedings-article" @default.
- W3088286051 hasAuthorship W3088286051A5057695979 @default.
- W3088286051 hasAuthorship W3088286051A5087251228 @default.
- W3088286051 hasConcept C110484373 @default.
- W3088286051 hasConcept C11413529 @default.
- W3088286051 hasConcept C114614502 @default.
- W3088286051 hasConcept C132525143 @default.
- W3088286051 hasConcept C137293760 @default.
- W3088286051 hasConcept C138885662 @default.
- W3088286051 hasConcept C154945302 @default.
- W3088286051 hasConcept C204321447 @default.
- W3088286051 hasConcept C23123220 @default.
- W3088286051 hasConcept C2777601683 @default.
- W3088286051 hasConcept C2779623528 @default.
- W3088286051 hasConcept C2985684807 @default.
- W3088286051 hasConcept C33923547 @default.
- W3088286051 hasConcept C41008148 @default.
- W3088286051 hasConcept C41895202 @default.
- W3088286051 hasConcept C44291984 @default.
- W3088286051 hasConcept C66945725 @default.
- W3088286051 hasConcept C71472368 @default.
- W3088286051 hasConcept C80444323 @default.
- W3088286051 hasConceptScore W3088286051C110484373 @default.
- W3088286051 hasConceptScore W3088286051C11413529 @default.
- W3088286051 hasConceptScore W3088286051C114614502 @default.
- W3088286051 hasConceptScore W3088286051C132525143 @default.
- W3088286051 hasConceptScore W3088286051C137293760 @default.
- W3088286051 hasConceptScore W3088286051C138885662 @default.
- W3088286051 hasConceptScore W3088286051C154945302 @default.
- W3088286051 hasConceptScore W3088286051C204321447 @default.
- W3088286051 hasConceptScore W3088286051C23123220 @default.
- W3088286051 hasConceptScore W3088286051C2777601683 @default.
- W3088286051 hasConceptScore W3088286051C2779623528 @default.
- W3088286051 hasConceptScore W3088286051C2985684807 @default.
- W3088286051 hasConceptScore W3088286051C33923547 @default.
- W3088286051 hasConceptScore W3088286051C41008148 @default.
- W3088286051 hasConceptScore W3088286051C41895202 @default.
- W3088286051 hasConceptScore W3088286051C44291984 @default.
- W3088286051 hasConceptScore W3088286051C66945725 @default.
- W3088286051 hasConceptScore W3088286051C71472368 @default.
- W3088286051 hasConceptScore W3088286051C80444323 @default.
- W3088286051 hasLocation W30882860511 @default.
- W3088286051 hasOpenAccess W3088286051 @default.
- W3088286051 hasPrimaryLocation W30882860511 @default.
- W3088286051 hasRelatedWork W1991290777 @default.
- W3088286051 hasRelatedWork W2094205336 @default.
- W3088286051 hasRelatedWork W2280871258 @default.
- W3088286051 hasRelatedWork W2511641841 @default.
- W3088286051 hasRelatedWork W2604227417 @default.
- W3088286051 hasRelatedWork W2758006462 @default.
- W3088286051 hasRelatedWork W2807739445 @default.
- W3088286051 hasRelatedWork W2895892112 @default.
- W3088286051 hasRelatedWork W2908621254 @default.
- W3088286051 hasRelatedWork W2912726230 @default.
- W3088286051 hasRelatedWork W2927634267 @default.
- W3088286051 hasRelatedWork W2981916418 @default.
- W3088286051 hasRelatedWork W3022049376 @default.
- W3088286051 hasRelatedWork W3044289590 @default.
- W3088286051 hasRelatedWork W3118780954 @default.
- W3088286051 hasRelatedWork W3133621124 @default.
- W3088286051 hasRelatedWork W3138839922 @default.
- W3088286051 hasRelatedWork W3154000428 @default.
- W3088286051 hasRelatedWork W9516802 @default.
- W3088286051 hasRelatedWork W9753090 @default.
- W3088286051 isParatext "false" @default.
- W3088286051 isRetracted "false" @default.
- W3088286051 magId "3088286051" @default.
- W3088286051 workType "article" @default.