Classifying Functional Relations in Factotum via WordNet Hypernym Associations
نویسندگان
چکیده
This paper describes how to automatically classify the functional relations from the Factotum knowledge base via a statistical machine learning algorithm. This incorporates a method for inferring prepositional relation indicators from corpus data. It also uses lexical collocations (i.e., word associations) and class-based collocations based on the WordNet hypernym relations (i.e., is-subset-of). The result shows substantial improvement over a baseline approach.
منابع مشابه
Resolving and Generating Definite Anaphora by Modeling Hypernymy using Unlabeled Corpora
We demonstrate an original and successful approach for both resolving and generating definite anaphora. We propose and evaluate unsupervised models for extracting hypernym relations by mining cooccurrence data of definite NPs and potential antecedents in an unlabeled corpus. The algorithm outperforms a standard WordNet-based approach to resolving and generating definite anaphora. It also substa...
متن کاملMulti-term Web Query Expansion Using WordNet
In this paper, we propose a method for multi-term query expansions based on WordNet. In our approach, Hypernym/Hyponymy and Synonym relations in WordNet is used as the basic expansion rules. Then we use WordNet Lexical Chains and WordNet semantic similarity to assign terms in the same query into different groups with respect to their semantic similarities. For each group, we expand the highest ...
متن کاملUnsupervised Entity Classification with Wikipedia and Wordnet
The task of classifying entities appearing in textual annotations to an arbitrary set of classes has not been extensively researched, yet it is useful in multimedia retrieval. We proposed an unsupervised algorithm, which expresses entities and classes as Wordnet synsets and uses Lin measure to classify them. Real-time hypernym discovery from Wikipedia is used to map uncommon entities to Wordnet...
متن کاملLearning Syntactic Patterns for Automatic Hypernym Discovery
Semantic taxonomies such as WordNet provide a rich source of knowledge for natural language processing applications, but are expensive to build, maintain, and extend. Motivated by the problem of automatically constructing and extending such taxonomies, in this paper we present a new algorithm for automatically learning hypernym (is-a) relations from text. Our method generalizes earlier work tha...
متن کاملUCD-FC: Deducing semantic relations using WordNet senses that occur frequently in a database of noun-noun compounds
This paper describes a system for classifying semantic relations among nominals, as in SemEval task 4. This system uses a corpus of 2,500 compounds annotated with WordNet senses and covering 139 different semantic relations. Given a set of nominal pairs for training, as provided in the SemEval task 4 training data, this system constructs for each training pair a set of features made up of relat...
متن کامل