Semantic Tagging at the Sense Level
نویسندگان
چکیده
This paper summarizes our research in the area of semantic tagging at the word and sense levels and sets the ground for a new approach to text-level sentiment annotation using a combination of machine learning and linguisticallymotivated techniques. We describe a system for sentiment tagging of words and senses based on WordNet glosses and advance the treatment of sentiment as a fuzzy category.
منابع مشابه
Combining Independent Knowledge Sources for Word Sense Disambiguation
Disambiguation Yorick Wilks and Mark Stevenson Department of Computer Science, University of She eld, Regent Court, 211 Portobello Street, She eld S1 4DP, UK fyorick, [email protected] Abstract Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of word sense disambiguation. We di...
متن کاملSense Tagging: Semantic Tagging with a Lexicon
Sense tagging, the automatic assignment of the appropriate sense from some lexicon to each of the words in a text, is a specialised instance of the general problem of semantic tagging by category or type. We discuss which recent word sense disambiguation algorithms are appropriate for sense tagging. It is our belief that sense tagging can be carried out effectively by combining several simple, ...
متن کاملManaging Uncertainty in Semantic Tagging
Low interannotator agreement (IAA) is a well-known issue in manual semantic tagging (sense tagging). IAA correlates with the granularity of word senses and they both correlate with the amount of information they give as well as with its reliability. We compare different approaches to semantic tagging in WordNet, FrameNet, PropBank and OntoNotes with a small tagged data sample based on the Corpu...
متن کاملCorpus-Based Approaches to Semantic Interpretation in NLP
into empirical, corpus-based learning approaches to natural language processing (NLP). Most empirical NLP work to date has focused on relatively low-level language processing such as part-ofspeech tagging, text segmentation, and syntactic parsing. The success of these approaches has stimulated research in using empirical learning techniques in other facets of NLP, including semantic analysis—un...
متن کاملFAQFinder with sense tagging FAQFinder without sense tagging
Rejection Recall FAQFinder with sense tagging FAQFinder without sense tagging Figure 4: Recall vs. Rejection for FAQFinder with and without WordNet Sense Tagging search. In FAQFinder, sense tagging and calculation of semantic similarity are much more computationally intensive than term vector processing. However, since FAQFinder matches single questions rather than entire documents, the computa...
متن کامل