Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

Mohamed, Muhidin; Oussalah, Mourad

doi:10.14569/ijacsa.2014.050725

Cited by 4 publications

(5 citation statements)

References 14 publications

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“…In this way, texts can be represented by their named entities. It is emerged in the Sixth Message Understanding Conference in 1995 [11].…”

Section: Named Entitiesmentioning

confidence: 99%

“…In the task of named entity classification, Mohamed and Oussalah [11] presented an approach by using the Wikipedia article info boxes where it has significantly reduced the classifier's processing time since the information inside the info box is structured. The proposed approach achieved a classification accuracy of above 97% with 3600 named entities and CoNLL-2003 shared task NER dataset used to validate the classifier's performance.…”

Section: Introductionmentioning

confidence: 99%

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A Web-based Tagger for Named Entities Detection

Muñoz¹,

Pérez-Téllez²,

Pinto³

2019

RCS

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Information Retrieval constitutes an important task for extracting meaningful data in huge amounts of texts since the majority of information in the world exist electronically, and in unstructured and plain text form. Most datamining applications assimilate only structured information, this means before any predictive model can be applied the data must be prepared in a special way. This paper presents a web-based tagger system for named entities detection in plain text to help users tagging texts resources. The advantage of using this tool is the possibility of tagging any plain text file and with any type of class for the entities, allowing to treat any domain and thus obtaining a wider recognition of named entities. In addition, a small application shows the facility in the training of NER classifier models as an advantage of corpora constructed with the web-based tagger.

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“…In this way, texts can be represented by their named entities. It is emerged in the Sixth Message Understanding Conference in 1995 [11].…”

Section: Named Entitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Web-based Tagger for Named Entities Detection

Muñoz¹,

Pérez-Téllez²,

Pinto³

2019

RCS

View full text Add to dashboard Cite

show abstract

“…In particular, several studies related to social media textual data have described the particular way of expressing named entities in natural language from social network data. Among these works, a first family has focused on the identification and the categorization of named entities (person, place et organization) using Wikipedia [12] [13]. In these works, the authors seek to take into account only toponym names as places (for examples Rome, Paris, London, etc.).…”

Section: State-of-the-artmentioning

confidence: 99%

Social media and European cultural routes

Bellens

Banc

Éloire

et al. 2016

Proceedings of the 8th International Conference on Management of Digital EcoSystems

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In the last few years, European Cultural Routes have become important tourist destinations. What makes this kind of tourism interesting is both the complexity of values and significances that motivate it and the variety of social actors (walkers, inhabitants, institutions, commercial actors, etc.) that are involved. Empirical studies on this phenomenon are still limited because they are really time-consuming and resource-demanding. Yet, social media data seem to provide new perspectives on this field by generating digital traces of the tourists' behavior. This paper aims at exploring how social media data can be employed in order to study tourism on European Cultural Routes. We will consider the case study of the platform Instagram on the route "Via Francigena".

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“…Utilizing Wikipedia infobox for ETR was presented in [26]. The proposed model classifies entities by matching entity attributes extracted from the relevant article infobox with core entity attributes built from Wikipedia infobox templates.…”

Section: Related Workmentioning

confidence: 99%

Entity Type Recognition Using an Ensemble of Distributional Semantic Models to Enhance Query Understanding

Shalaby

Jadda²,

Korayem³

et al. 2016

2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)

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We present an ensemble approach for categorizing search query entities in the recruitment domain. Understanding the types of entities expressed in a search query (Company, Skill, Job Title, etc.) enables more intelligent information retrieval based upon those entities compared to a traditional keyword-based search. Because search queries are typically very short, leveraging a traditional bag-of-words model to identify entity types would be inappropriate due to the lack of contextual information. Our approach instead combines clues from different sources of varying complexity in order to collect real-world knowledge about query entities. We employ distributional semantic representations of query entities through two models: 1) contextual vectors generated from encyclopedic corpora like Wikipedia, and 2) high dimensional word embedding vectors generated from millions of job postings using word2vec. Additionally, our approach utilizes both entity linguistic properties obtained from WordNet and ontological properties extracted from DBpedia. We evaluate our approach on a data set created at CareerBuilder; the largest job board in the US. The data set contains entities extracted from millions of job seekers/recruiters search queries, job postings, and resume documents. After constructing the distributional vectors of search entities, we use supervised machine learning to infer search entity types. Empirical results show that our approach outperforms the state-of-the-art word2vec distributional semantics model trained on Wikipedia. Moreover, we achieve microaveraged F 1 score of 97% using the proposed distributional representations ensemble.

show abstract

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

Cited by 4 publications

References 14 publications

A Web-based Tagger for Named Entities Detection

A Web-based Tagger for Named Entities Detection

Social media and European cultural routes

Entity Type Recognition Using an Ensemble of Distributional Semantic Models to Enhance Query Understanding

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