[16] introduce a novel supervised learning model for mapping verb instances to VN classes, using rich syntactic features and class membership constraints. The above two methods are based on supervised learning methods with rich features based on part-of-speech tags, word stems, blog post surrounding and cooccurring words, and dependency relationships.3. Word Sense Disambiguation Based on Topical and Semantic AssociationIn this section, we introduce the description of word sense disambiguation in detail, which includes three core components, namely firstly, mapping a WordNet Sense to an ODP’s Category Label for generating the term’s topic semantic profile; secondly, extracting topic discriminative term through position feature, statistical feature, semantic feature, and topic span distribution feature, and leveraging topic discriminative terms for topic identification; finally, determining the unique sense of ambiguous term using topical-semantic association graph.
3.1. Mapping a WordNet Sense to an ODP’s Category LabelWe aim to construct a mapping relation from a WordNet sense to an ODP’s category label. Our proposed approach effectively fuses the semantic knowledge with hierarchical topic category to generate topic semantic knowledge profile for expediently handling a series of research hot issues, such as information extraction, topic identification, and word sense disambiguation. For conveniences in describing follow-up contents, we give some basic terminologies.Definition 1 (topic chain) ��A topic chain (TC) is a branch of topic hierarchy and represents a sequence of ordered topic category label terms in ODP.
It represents a notation of tm > >t2 > t1, where t1 is a top topic term and tm is a terminal topic term.Definition 2 (disambiguation context) ��A disambiguation context (DC) is a set of glosses, synonyms semantics, and hypernyms semantics for a term which may exist several senses in WordNet. DC represents the horizontal synonyms relation and the vertical hypernyms relation from lower-level concept to upper-level concept. Simultaneously, the glosses can also be available to calculate the semantic similarity. Definition 3 (topic semantic profile) ��A topic semantic profile (TSP), which characterizes term’s semantic and its hierarchical topic category, is a sequence of 3-tuple and represents a notation of w, DC, TC, where DC denotes the term w’s disambiguation context; TC denotes the term w’s topic chain label name.
Due to a variety of senses or the vague sense for a given term, the determination of its topic category label is the most difficult problem. In order to solve this problem, there are two significant aspects to be handled, one is to determinate a particular topic branch of term which is Anacetrapib associated with multiple topics; the other is to assign the term’s proper topic level, just in case too fine-grained hierarchical category to match the concept of user interests or information needs.