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Text Analytics in ITS 2.0: Annotation of Named Entities

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Presentation on theme: "Text Analytics in ITS 2.0: Annotation of Named Entities"— Presentation transcript:

1 Text Analytics in ITS 2.0: Annotation of Named Entities
Tadej Štajner Jožef Stefan Institute

2 Motivation Translating proper names
… can be problematic for statistical MT systems

3 Motivation (2) Translation depends on source and target language:
There are specific rules to translate (or transliterate) particular proper names or concepts Sometimes, they should not even be translated Solution: figure out what is actually being mentioned and see if any existing translated expression exists for that entity

4 Motivation (3) Localization of proper names:
personal names, product names, or geographic names, chemical compounds, protein names Names can appear without sufficient context: we can use ITS2.0 Text Analysis annotations to provide context for ambiguous content.

5 ITS2.0 Text Analysis Support text analysis agents that enhance content by suggesting or identifying concepts, identities, identified by IRIs. The data category provides three pieces of information: confidence entity type/concept class entity/concept identifier

6 ITS2.0 Text Analysis <!DOCTYPE html> <div its-annotators-ref="text-analysis| <span its-ta-ident-ref=" its-ta-class-ref=" is the <span its-ta-ident-ref=" of <span its-ta-ident-ref=" its-ta-class-ref=" </div>

7 Producing these annotations
NLP Techniques Named entity extraction & disambiguation Word sense disambiguation Manual annotation

8 Named entity disambiguation
Document Entity Mention Label

9 Use cases Informing a human agent (i.e. translator) that a certain fragment of text is subject to follow specific translation rules: [this is taken up in OKAPI and the XLIFF generation] proper names officially regulated translations. Informing software agent (i.e. CMS) about the conceptual type of a textual entity in order to enable special processing or indexing; geographic names personal names product names chemical compounds

10 Open issues Text Analysis data category can’t represent stand-off annotations, so only one layer can be done Support for the domain data category via text analysis tools

11 Business case By itself, it’s infrastructure that indirectly supports business cases by supporting other technical scenarios [Clemens:] Having metadata saves time, producing it automatically can compound the savings [XLIFF roundtrip:] Human as well as machine consumption of this metadata

12 Demo

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