Wrapper Induction

Goebel, Max; Ceresna, Michal

doi:10.1007/978-0-387-39940-9_1160

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The reasons for this seem to be expectation, convenience and heritage. HTML in natural language is not expected to be of any benefit: "Free text exhibits no implicit structure at all" (Goebel and Ceresna, 2009). Any information to be extracted from free text is assumed to be held entirely in natural language grammar and semantics.…”

Section: Introductionmentioning

confidence: 99%

Leveraging HTML in Free Text Web Named Entity Recognition

Ashby¹,

Weir²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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HTML tags are typically discarded in free text Named Entity Recognition from Web pages. We investigate whether these discarded tags might be used to improve NER performance. We compare Text+Tags sentences with their Text-Only equivalents, over five datasets, two free text segmentation granularities and two NER models. We find an increased F1 performance for Text+Tags of between 0.9% and 13.2% over all datasets, variants and models. This performance increase, over datasets of varying entity types, HTML density and construction quality, indicates our method is flexible and adaptable. These findings imply that a similar technique might be of use in other Web-aware NLP tasks, including the enrichment of deep language models.

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Section: Introductionmentioning

confidence: 99%

Leveraging HTML in Free Text Web Named Entity Recognition

Ashby¹,

Weir²

2020

Proceedings of the 28th International Conference on Computational Linguistics

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A Characterization of Data Exchange between Visual Analytics Tools

Nonnemann

Schumann

Urban

et al. 2020

2020 24th International Conference Information Visualisation (IV)

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Learning twig and path queries

Staworko

Wieczorek

2012

Proceedings of the 15th International Conference on Database Theory

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We investigate the problem of learning XML queries, path queries and twig queries, from examples given by the user. A learning algorithm takes on the input a set of XML documents with nodes annotated by the user and returns a query that selects the nodes in a manner consistent with the annotation. We study two learning settings that differ with the types of annotations. In the first setting the user may only indicate required nodes that the query must select (i.e., positive examples). In the second, more general, setting, the user may also indicate forbidden nodes that the query must not select (i.e., negative examples). The query may or may not select any node with no annotation.We formalize what it means for a class of queries to be learnable. One requirement is the existence of a learning algorithm that is sound i.e., always returning a query consistent with the examples given by the user. Furthermore, the learning algorithm should be complete i.e., able to produce every query with sufficiently rich examples. Other requirements involve tractability of the learning algorithm and its robustness to nonessential examples. We identify practical classes of Boolean and unary, path and twig queries that are learnable from positive examples. We also show that adding negative examples to the picture renders learning unfeasible.

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Wrapper Induction

Cited by 3 publications

References 5 publications

Leveraging HTML in Free Text Web Named Entity Recognition

Leveraging HTML in Free Text Web Named Entity Recognition

A Characterization of Data Exchange between Visual Analytics Tools

Learning twig and path queries

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