Refine
Document Type
Language
- English (2)
Has Fulltext
- yes (2)
Is part of the Bibliography
- no (2)
Keywords
- Computerlinguistik (2)
- Maschinelles Lernen (2)
- Text Mining (2)
- Korpus <Linguistik> (1)
- Lebensmittel (1)
- Natürliche Sprache (1)
- Negation (1)
- document management and text processing (1)
- language resources (1)
- natural language processing (1)
Publicationstate
- Veröffentlichungsversion (2) (remove)
Reviewstate
- Peer-Review (2)
Publisher
- Association for Computational Linguistics (2) (remove)
This paper presents a survey on the role of negation in sentiment analysis. Negation is a very common linguistic construction that affects polarity and, therefore, needs to be taken into consideration in sentiment analysis.
We will present various computational approaches modeling negation in sentiment analysis. We will, in particular, focus on aspects such as level of representation used for sentiment analysis, negation word detection and scope of negation. We will also discuss limits and challenges of negation modeling on that task.
Automatic Food Categorization from Large Unlabeled Corpora and Its Impact on Relation Extraction
(2014)
We present a weakly-supervised induction method to assign semantic information to food items. We consider two tasks of categorizations being food-type classification and the distinction of whether a food item is composite or not. The categorizations are induced by a graph-based algorithm applied on a large unlabeled domain-specific corpus. We show that the usage of a domain-specific corpus is vital. We do not only outperform a manually designed open-domain ontology but also prove the usefulness of these categorizations in relation extraction, outperforming state-of-the-art features that include syntactic information and Brown clustering.