Refine
Year of publication
- 2010 (3) (remove)
Document Type
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- Computerlinguistik (2)
- Maschinelles Lernen (2)
- Natürliche Sprache (2)
- Automatische Sprachanalyse (1)
- Frame-Semantik (1)
- Information Extraction (1)
- Labeling approach (1)
- Meinung (1)
- Natural Language Processing (1)
- Negation (1)
Publicationstate
- Veröffentlichungsversion (3) (remove)
Reviewstate
- Peer-Review (3)
Publisher
- Association for Computational Linguistics (3) (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.
Opinion holder extraction is one of the important subtasks in sentiment analysis. The effective detection of an opinion holder depends on the consideration of various cues on various levels of representation, though they are hard to formulate explicitly as features. In this work, we propose to use convolution kernels for that task which identify meaningful fragments of sequences or trees by themselves. We not only investigate how different levels of information can be effectively combined in different kernels but also examine how the scope of these kernels should be chosen. In general relation extraction, the two candidate entities thought to be involved in a relation are commonly chosen to be the boundaries of sequences and trees. The definition of boundaries in opinion holder extraction, however, is less straightforward since there might be several expressions beside the candidate opinion holder to be eligible for being a boundary.
We describe the SemEval-2010 shared task on “Linking Events and Their Participants in Discourse”. This task is an extension to the classical semantic role labeling task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in a larger context, e.g., by sharing references to specific discourse entities or events. In the shared task we looked at one particular aspect of cross-sentence links between argument structures, namely linking locally uninstantiated roles to their co-referents in the wider discourse context (if such co-referents exist). This task is potentially beneficial for a number of NLP applications, such as information extraction, question answering or text summarization.