2 Search Results for "Cocea, Mihaela"


Document
Towards Context-Aware Syntax Parsing and Tagging

Authors: Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea

Published in: OASIcs, Volume 66, 2018 Imperial College Computing Student Workshop (ICCSW 2018)


Abstract
Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains.

Cite as

Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea. Towards Context-Aware Syntax Parsing and Tagging. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 5:1-5:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Copy BibTex To Clipboard

@InProceedings{mohasseb_et_al:OASIcs.ICCSW.2018.5,
  author =	{Mohasseb, Alaa and Bader-El-Den, Mohamed and Cocea, Mihaela},
  title =	{{Towards Context-Aware Syntax Parsing and Tagging}},
  booktitle =	{2018 Imperial College Computing Student Workshop (ICCSW 2018)},
  pages =	{5:1--5:9},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-097-2},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{66},
  editor =	{Pirovano, Edoardo and Graversen, Eva},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.5},
  URN =		{urn:nbn:de:0030-drops-101866},
  doi =		{10.4230/OASIcs.ICCSW.2018.5},
  annote =	{Keywords: Information Retrieval, Natural Language Processing, PoS Tagging, PoS Parsing, Machine Learning}
}
Document
Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification

Authors: Fatima Chiroma, Mihaela Cocea, and Han Liu

Published in: OASIcs, Volume 66, 2018 Imperial College Computing Student Workshop (ICCSW 2018)


Abstract
Feature selection is typically employed before or in conjunction with classification algorithms to reduce the feature dimensionality and improve the classification performance, as well as reduce processing time. While particular approaches have been developed for feature selection, such as filter and wrapper approaches, some algorithms perform feature selection through their learning strategy. In this paper, we are investigating the effect of the implicit feature selection of the PRISM algorithm, which is rule-based, when compared with the wrapper feature selection approach employing four popular algorithms: decision trees, naïve bayes, k-nearest neighbors and support vector machine. Moreover, we investigate the performance of the algorithms on target classes, i.e. where the aim is to identify one or more phenomena and distinguish them from their absence (i.e. non-target classes), such as when identifying benign and malign cancer (two target classes) vs. non-cancer (the non-target class).

Cite as

Fatima Chiroma, Mihaela Cocea, and Han Liu. Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 6:1-6:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Copy BibTex To Clipboard

@InProceedings{chiroma_et_al:OASIcs.ICCSW.2018.6,
  author =	{Chiroma, Fatima and Cocea, Mihaela and Liu, Han},
  title =	{{Evaluation of Rule-Based Learning and Feature Selection Approaches For Classification}},
  booktitle =	{2018 Imperial College Computing Student Workshop (ICCSW 2018)},
  pages =	{6:1--6:6},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-097-2},
  ISSN =	{2190-6807},
  year =	{2019},
  volume =	{66},
  editor =	{Pirovano, Edoardo and Graversen, Eva},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.6},
  URN =		{urn:nbn:de:0030-drops-101872},
  doi =		{10.4230/OASIcs.ICCSW.2018.6},
  annote =	{Keywords: Feature Selection, Prism, Rule-based Learning, Wrapper Approach}
}
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