Synonymy and semantic classification
Synonymy and semantic classification
Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
C4.5: programs for machine learning
C4.5: programs for machine learning
Inheritance, defaults and the lexicon
Inheritance, defaults and the lexicon
Lexical knowledge representation and natural language processing
Artificial Intelligence - Special volume on natural language processing
Automated learning of decision rules for text categorization
ACM Transactions on Information Systems (TOIS)
Query expansion using lexical-semantic relations
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
WordNet: a lexical database for English
Communications of the ACM
Electric words: dictionaries, computers, and meanings
Electric words: dictionaries, computers, and meanings
DATR: a language for lexical knowledge representation
Computational Linguistics
The role of lexicons in natural language processing
Communications of the ACM
Introduction to Default Logic
Information Retrieval
Machine Learning
Lemma Handling in Default Logic Theorem Provers
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Using Default Logic for Lexical Knowledge
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Presuppositions and Default Reasoning: A Study in Lexical Pragmatics
Proceedings of the First SIGLEX Workshop on Lexical Semantics and Knowledge Representation
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Using Prior Probabilities and Density Estimation for Relational Classification
ILP '98 Proceedings of the 8th International Workshop on Inductive Logic Programming
Intelligent Text Handling Using Default Logic
ICTAI '96 Proceedings of the 8th International Conference on Tools with Artificial Intelligence
Semantic Lexicon Acquisition for Learning Natural Language Interfaces
Semantic Lexicon Acquisition for Learning Natural Language Interfaces
EACL '89 Proceedings of the fourth conference on European chapter of the Association for Computational Linguistics
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Merging structured text using temporal knowledge
Data & Knowledge Engineering
Logical fusion rules for merging structured news reports
Data & Knowledge Engineering
Context-aware pervasive service composition and its implementation
Personal and Ubiquitous Computing
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Lexical knowledge is increasingly important in information systems—for example in indexing documents using keywords, or disambiguating words in a query to an information retrieval system, or a natural language interface. However, it is a difficult kind of knowledge to represent and reason with. Existing approaches to formalizing lexical knowledge have used languages with limited expressibility, such as those based on inheritance hierarchies, and in particular, they have not adequately addressed the context-dependent nature of lexical knowledge. Here we present a framework, based on default logic, called the dex framework, for capturing context-dependent reasoning with lexical knowledge. Default logic is a first-order logic offering a more expressive formalisation than inheritance hierarchies: (1) First-order formulae capturing lexical knowledge about words can be inferred; (2) Preferences over formulae can be based on specificity, reasoning about exceptions, or explicit priorities; (3) Information about contexts can be reasoned with as first-order formulae formulae; and (4) Information about contexts can be derived as default inferences. In the dex framework, a word for which lexical knowledge is sought is called a query word. The context for a query word is derived from further words, such as words in the same sentence as the query word. These further words are used with a form of decision tree called a context classification tree to identify which contexts hold for the query word. We show how we can use these contexts in default logic to identify lexical knowledge about the query word such as synonyms, antonyms, specializations, meronyms, and more sophisticated first-order semantic knowledge. We also show how we can use a standard machine learning algorithm to generate context classification trees.