Abstract
| Original language | Undefined |
|---|---|
| Title of host publication | Uncertainty Reasoning for the Semantic Web III |
| Place of Publication | Berlin |
| Publisher | Springer |
| Pages | 309-328 |
| Number of pages | 20 |
| ISBN (Print) | 978-3-319-13412-3 |
| DOIs | |
| Publication status | Published - Nov 2014 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer Verlag |
| Number | 8816 |
| Volume | 8816 |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Keywords
- Named entity recognitionNamed entity linkingNamed entity extractionNamed entity disambiguationInformal textUncertainty handling
- EWI-25421
- Uncertainty handling
- IR-93592
- Named Entity Disambiguation
- Named Entity Extraction
- METIS-309724
- Informal text
Cite this
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Uncertainty Handling in Named Entity Extraction and Disambiguation for Informal Text. / van Keulen, Maurice; Habib, Mena Badieh.
Uncertainty Reasoning for the Semantic Web III. Berlin : Springer, 2014. p. 309-328 (Lecture Notes in Computer Science; Vol. 8816, No. 8816).Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review
TY - CHAP
T1 - Uncertainty Handling in Named Entity Extraction and Disambiguation for Informal Text
AU - van Keulen, Maurice
AU - Habib, Mena Badieh
N1 - 10.1007/978-3-319-13413-0_16
PY - 2014/11
Y1 - 2014/11
N2 - Social media content represents a large portion of all textual content appearing on the Internet. These streams of user generated content (UGC) provide an opportunity and challenge for media analysts to analyze huge amount of new data and use them to infer and reason with new information. A main challenge of natural language is its ambiguity and vagueness. To automatically resolve ambiguity, the grammatical structure of sentences is used. However, when we move to informal language widely used in social media, the language becomes more ambiguous and thus more challenging for automatic understanding. Information Extraction (IE) is the research field that enables the use of unstructured text in a structured way. Named Entity Extraction (NEE) is a sub task of IE that aims to locate phrases (mentions) in the text that represent names of entities such as persons, organizations or locations regardless of their type. Named Entity Disambiguation (NED) is the task of determining which correct person, place, event, etc. is referred to by a mention. The goal of this paper is to provide an overview on some approaches that mimic the human way of recognition and disambiguation of named entities especially for domains that lack formal sentence structure. The proposed methods open the doors for more sophisticated applications based on users’ contributions on social media. We propose a robust combined framework for NEE and NED in semi-formal and informal text. The achieved robustness has been proven to be valid across languages and domains and to be independent of the selected extraction and disambiguation techniques. It is also shown to be robust against the informality of the used language. We have discovered a reinforcement effect and exploited it a technique that improves extraction quality by feeding back disambiguation results. We present a method of handling the uncertainty involved in extraction to improve the disambiguation results.
AB - Social media content represents a large portion of all textual content appearing on the Internet. These streams of user generated content (UGC) provide an opportunity and challenge for media analysts to analyze huge amount of new data and use them to infer and reason with new information. A main challenge of natural language is its ambiguity and vagueness. To automatically resolve ambiguity, the grammatical structure of sentences is used. However, when we move to informal language widely used in social media, the language becomes more ambiguous and thus more challenging for automatic understanding. Information Extraction (IE) is the research field that enables the use of unstructured text in a structured way. Named Entity Extraction (NEE) is a sub task of IE that aims to locate phrases (mentions) in the text that represent names of entities such as persons, organizations or locations regardless of their type. Named Entity Disambiguation (NED) is the task of determining which correct person, place, event, etc. is referred to by a mention. The goal of this paper is to provide an overview on some approaches that mimic the human way of recognition and disambiguation of named entities especially for domains that lack formal sentence structure. The proposed methods open the doors for more sophisticated applications based on users’ contributions on social media. We propose a robust combined framework for NEE and NED in semi-formal and informal text. The achieved robustness has been proven to be valid across languages and domains and to be independent of the selected extraction and disambiguation techniques. It is also shown to be robust against the informality of the used language. We have discovered a reinforcement effect and exploited it a technique that improves extraction quality by feeding back disambiguation results. We present a method of handling the uncertainty involved in extraction to improve the disambiguation results.
KW - Named entity recognitionNamed entity linkingNamed entity extractionNamed entity disambiguationInformal textUncertainty handling
KW - EWI-25421
KW - Uncertainty handling
KW - IR-93592
KW - Named Entity Disambiguation
KW - Named Entity Extraction
KW - METIS-309724
KW - Informal text
U2 - 10.1007/978-3-319-13413-0_16
DO - 10.1007/978-3-319-13413-0_16
M3 - Chapter
SN - 978-3-319-13412-3
T3 - Lecture Notes in Computer Science
SP - 309
EP - 328
BT - Uncertainty Reasoning for the Semantic Web III
PB - Springer
CY - Berlin
ER -