Web Information Extraction

Chiticariu, Laura; Danilevsky, Marina; Ho, Howard; Krishnamurthy, Rajasekar; Li, Yunyao; Raghavan, S. V.; Reiss, Frederick; Vaithyanathan, Shivakumar; Zhu, Huaiyu

doi:10.1007/978-1-4614-8265-9_459

Cited by 5 publications

(7 citation statements)

References 18 publications

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“…NegEx and ConText use “pseudo” triggers to deal with these situations. Goryachev et al [8] compared four different negation detection methods to process discharge summaries, including NegEx, NegExpander, and two other simple machine learning (ML) approaches. The rule-based NegEx (F1-score: 0.89) and NegExpander (F1-score: 0.91) outperformed the two ML approaches (F1-scores: 0.78 and 0.86, respectively).…”

Section: Introductionmentioning

confidence: 99%

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable

Shi

Hurdle

2018

Journal of Biomedical Informatics

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Section: Introductionmentioning

confidence: 99%

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable

Shi

Hurdle

2018

Journal of Biomedical Informatics

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“…Based on their approaches, they can be classified into three categories: rulebased, machine learning, and hybrid (Chiticariu, Krishnamurthy, Li, Reiss, & Vaithyanathan, 2010;Nadeau & Sekine, 2007). Notwithstanding, the performance of that algorithm can deteriorate rapidly when noise (perturbation) is introduced in the entity list or pattern list.…”

Section: Nerc: Techniquesmentioning

confidence: 99%

“…To make these rules effective, each word is represented by a features vector that contains Boolean, numeric and nominal values. These values describe specifics features of the words, for example, its length and if the word is capitalized (Carreras, Màrquez, & Padró, 2003;Chiticariu et al, 2010;Nadeau & Sekine, 2007).…”

Section: Nerc: Features Detectedmentioning

confidence: 99%

“…or domain factor (gardening, sports, business, etc.) can be reasonably supported, however porting a system to a new domain or textual genre and produce accurate results remains a major challenge (Chiticariu et al, 2010;Nadeau & Sekine, 2007). Finally, another limiting factor is the Entity type since based on the application requirements, a NERC system must classify in the most studied types (ENAMEX and NUMEX) or subtypes and subcategories of them (city, country, "politician", "entertainer", etc.…”

Section: Nerc: General Limitationsmentioning

confidence: 99%

“…A rule-based NERC system often consists of a combination of four categories of rules (Chiticariu et al, 2010): o Consolidation rules (CO) resolve overlapping candidates generated by multiple CD and CR rules. For instance, a CD rule may identify 'Dr.…”

Section: • Rules Categoriesmentioning

confidence: 99%

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End-to-end system for extracting and interpreting testual information of interest from identify documents images.

Menéndez¹,

Carlos²

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Identity documents (ID) are one of the primary sources for obtaining information about a citizen. The center of many applications within the administrative and service sectors is the extraction of the data contained in ID cards. Therefore, in this research is proposed the implementation of an automated system able to extract and interpret the textual information from identity documents images. The proposed end-to-end system allows the automation of a registration or verification process that requires the acquisition of information about a citizen using his identity documents. The system obtained through this research is considered as an end-to-end system since it covers every stage of the information of interest extraction process from IDs images.Different to the template-based systems, the proposed system uses a semantic attribution algorithm that allows to classify and attribute meaning to the information from IDs according to its semantics. This research is the first comprehensive description of a complete information extraction system to process IDs that describes from image processing to named entity recognition.To evaluate the performance of the research were proposed different metrics based on the internal functions of the system. The final evaluation shows satisfactory results showing that the end-to-end system is capable of extracting and interpreting textual information from identity documents images without prior knowledge of their layouts.

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Web Mining

Ristoski

2023

Machine Learning for Data Science Handbook

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Web Information Extraction

Cited by 5 publications

References 18 publications

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable

Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable

End-to-end system for extracting and interpreting testual information of interest from identify documents images.

Web Mining

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