Lexical knowledge is knowledge about the morphology gram mar and semantics of words This knowledge is increasingly important in language engineering and more generally in information retrieval in formation ltering intelligent agents and knowledge management Here we present a framework based on default logic called Lexica for captur ing lexical knowledge We show how we can use contextual informa...

We present UWN, a large multilingual lexical knowledge base that describes the meanings and relationships of words in over 200 languages. This paper explains how link prediction, information integration and taxonomy induction methods have been used to build UWN based on WordNet and extend it with millions of named entities from Wikipedia. We additionally introduce extensions to cover lexical re...

From the viewpoint of multilingual generation, the common underlying knowledge base should be kept clear of language-speciic concepts. This goal presupposes that lexical items of various languages cannot map one-to-one onto concepts all the time. We propose a more exi-ble way of attaching lexical items to conngurations of concepts and roles, and a lexical option nder that determines the set of ...

This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.

We participated in the SENSEVAL-3 English lexical sample task and multilingual lexical sample task. We adopted a supervised learning approach with Support Vector Machines, using only the official training data provided. No other external resources were used. The knowledge sources used were partof-speech of neighboring words, single words in the surrounding context, local collocations, and synta...

A large ontology such as lexical ontology is useful as the basic knowledge base in artificial intelligence and computational linguistics application. However, it is insufficient to recognize only existing instances for each concept. Adding new instances into the lexical ontology will expand knowledge in the system. In this paper, we propose an efficient unsupervised ontology population system t...

Children learn new words via their everyday reading experience but little is known about how this learning happens. We addressed this by focusing on the conditions needed for new words to become familiar to children, drawing a distinction between lexical configuration (the acquisition of word knowledge) and lexical engagement (the emergence of interactive processes between newly learned words a...