Common Sense and Maximum Entropy

Reasoning with Probabilities and Maximum Entropy : The System PIT and its Application in LEXMED 1

We present a theory, a system and an application for common sense reasoning based on propositional logic, the probability calculus and the concept of maximum entropy. The task of the system Pit (Probability Induction Tool) is to provide decisions under incomplete knowledge, while keeping the necessary additional assumptions as minimal and clear as possible. We therefore enrich the probability c...

Maximum Entropy Models for Word Sense Disambiguation

A Maximum Entropy Approach To HowNet-Based Chinese Word Sense Disambiguation

This paper presents a maximum entropy method for the disambiguation of word senses as defined in HowNet. With the release of this bilingual (Chinese and English) knowledge base in 1999, a corpus of 30,000 words was sense tagged and released in January 2002. Concepts meanings in HowNet are constructed by a closed set of sememes, the smallest meaning units, which can be treated as semantic tags. ...

A Maximum Entropy-based Word Sense Disambiguation System

In this paper, a supervised learning system of word sense disambiguation is presented. It is based on conditional maximum entropy models. This system acquires the linguistic knowledge from an annotated corpus and this knowledge is represented in the form of features. Several types of features have been analyzed using the SENSEVAL-2 data for the Spanish lexical sample task. Such analysis shows t...

Refined Lexikon Models for Statistical Machine Translation Using a Maximum Entropy Approach

Typically, the lexicon models used in statistical machine translation systems do not include any kind of linguistic or contextual information, which often leads to problems in performing a correct word-sense disambiguation. One way to deal with this problem within the statistical framework is using maximum entropy methods. In this paper, we present how to use this information within a statistic...