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Documents authored by Lopes Cardoso, Henrique

Document
DOI: 10.4230/OASIcs.LDK.2021.15
Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval

Authors: Álvaro Mendes Samagaio, Henrique Lopes Cardoso, and David Ribeiro

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
Smart assistants and recommender systems must deal with lots of information coming from different sources and having different formats. This is more frequent in text data, which presents increased variability and complexity, and is rather common for conversational assistants or chatbots. Moreover, this issue is very evident in the food and nutrition lexicon, where the semantics present increased variability, namely due to hypernyms and hyponyms. This work describes the creation of a set of word embeddings based on the incorporation of information from a food thesaurus - LanguaL - through retrofitting. The ingredients were classified according to three different facet label groups. Retrofitted embeddings seem to properly encode food-specific knowledge, as shown by an increase on accuracy as compared to generic embeddings (+23%, +10% and +31% per group). Moreover, a weighing mechanism based on TF-IDF was applied to embedding creation before retrofitting, also bringing an increase on accuracy (+5%, +9% and +5% per group). Finally, the approach has been tested with human users in an ingredient retrieval exercise, showing very positive evaluation (77.3% of the volunteer testers preferred this method over a string-based matching algorithm).
Cite as

Álvaro Mendes Samagaio, Henrique Lopes Cardoso, and David Ribeiro. Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 15:1-15:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{samagaio_et_al:OASIcs.LDK.2021.15,
  author =	{Samagaio, \'{A}lvaro Mendes and Lopes Cardoso, Henrique and Ribeiro, David},
  title =	{{Enriching Word Embeddings with Food Knowledge for Ingredient Retrieval}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{15:1--15:15},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.15},
  URN =		{urn:nbn:de:0030-drops-145510},
  doi =		{10.4230/OASIcs.LDK.2021.15},
  annote =	{Keywords: Word embeddings, Retrofitting, LanguaL, Food Embeddings, Knowledge Graph}
}
Document
DOI: 10.4230/OASIcs.LDK.2021.25
Inconsistency Detection in Job Postings

Authors: Joana Urbano, Miguel Couto, Gil Rocha, and Henrique Lopes Cardoso

Published in: OASIcs, Volume 93, 3rd Conference on Language, Data and Knowledge (LDK 2021)

Abstract
The use of AI in recruitment is growing and there is AI software that reads jobs' descriptions in order to select the best candidates for these jobs. However, it is not uncommon for these descriptions to contain inconsistencies such as contradictions and ambiguities, which confuses job candidates and fools the AI algorithm. In this paper, we present a model based on natural language processing (NLP), machine learning (ML), and rules to detect these inconsistencies in the description of language requirements and to alert the recruiter to them, before the job posting is published. We show that the use of an hybrid model based on ML techniques and a set of domain-specific rules to extract the language details from sentences achieves high performance in the detection of inconsistencies.
Cite as

Joana Urbano, Miguel Couto, Gil Rocha, and Henrique Lopes Cardoso. Inconsistency Detection in Job Postings. In 3rd Conference on Language, Data and Knowledge (LDK 2021). Open Access Series in Informatics (OASIcs), Volume 93, pp. 25:1-25:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{urbano_et_al:OASIcs.LDK.2021.25,
  author =	{Urbano, Joana and Couto, Miguel and Rocha, Gil and Lopes Cardoso, Henrique},
  title =	{{Inconsistency Detection in Job Postings}},
  booktitle =	{3rd Conference on Language, Data and Knowledge (LDK 2021)},
  pages =	{25:1--25:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-199-3},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{93},
  editor =	{Gromann, Dagmar and S\'{e}rasset, Gilles and Declerck, Thierry and McCrae, John P. and Gracia, Jorge and Bosque-Gil, Julia and Bobillo, Fernando and Heinisch, Barbara},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.LDK.2021.25},
  URN =		{urn:nbn:de:0030-drops-145612},
  doi =		{10.4230/OASIcs.LDK.2021.25},
  annote =	{Keywords: NLP, Ambiguities, Contradictions, Recruitment software}
}
Document
DOI: 10.4230/DFU.Vol4.12111.93
Regulated MAS: Social Perspective

Authors: Pablo Noriega, Amit K. Chopra, Nicoletta Fornara, Henrique Lopes Cardoso, and Munindar P. Singh

Published in: Dagstuhl Follow-Ups, Volume 4, Normative Multi-Agent Systems (2013)

Abstract
This chapter addresses the problem of building normative multiagent systems in terms of regulatory mechanisms. It describes a static conceptual model through which one can specify normative multiagent systems along with a dynamic model to capture their operation and evolution. The chapter proposes a typology of applications and presents some open problems. In the last section, the authors express their individual views on these matters.
Cite as

Pablo Noriega, Amit K. Chopra, Nicoletta Fornara, Henrique Lopes Cardoso, and Munindar P. Singh. Regulated MAS: Social Perspective. In Normative Multi-Agent Systems. Dagstuhl Follow-Ups, Volume 4, pp. 93-133, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2013)

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@InCollection{noriega_et_al:DFU.Vol4.12111.93,
  author =	{Noriega, Pablo and Chopra, Amit K. and Fornara, Nicoletta and Lopes Cardoso, Henrique and Singh, Munindar P.},
  title =	{{Regulated MAS: Social Perspective}},
  booktitle =	{Normative Multi-Agent Systems},
  pages =	{93--133},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-51-4},
  ISSN =	{1868-8977},
  year =	{2013},
  volume =	{4},
  editor =	{Andrighetto, Giulia and Governatori, Guido and Noriega, Pablo and van der Torre, Leendert W. N.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DFU.Vol4.12111.93},
  URN =		{urn:nbn:de:0030-drops-40017},
  doi =		{10.4230/DFU.Vol4.12111.93},
  annote =	{Keywords: NormMAS, Norms, Open Interaction}
}
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