Memory Based Learning

AbstractCurrent approaches to computational lexicology in language technology are knowledge-based (competence-oriented) and try to abstract away from specific formalisms, domains, and applications. This results in severe complexity, acquisition and reusability …...

This paper analyses the relation between the use of similarity in Memory-Based Learning and the notion of backed-off smoothing in statistical language model- ing. We show that the two approaches …...

In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disam- biguation. We compare Memory-Based Learning, which stores examples in mem- ory and …...

Recognizing shallow linguistic patterns, such as ba- sic syntactic relationships between words, is a com- mon task in applied natural language and text pro- cessing. The common practice for approaching …...

Memory-based learning, keeping full memory ofleaxning material, appeaxs a viable approach to learning N-~ tasks, and is often superior in genera~sation accuracy to eager learning approaches that abstract from learning …...

We present a general architecture for efficient and deterministic morphological analysis based on memory-based learning, and apply it to morphological analysis of Dutch. The system makes direct mappings from letters …...

We present a memory-based learning (MBL) approach to shallow parsing in which POS tagging, chunking, and identification of syntactic relations are formulated as memory-based modules. The experiments reported in this …...

Memory-based learning (MBL) has enjoyed considerable success corpus-based natural language process-in ing (NLP) tasks and is thus a reliable method of getting a high-level of per- formance when building corpus-based …...

We address the issue of on-line detec- tion of communication problems in spoken dialogue systems. The useful- ness is investigated of the sequence of system question types and the word …...

We describe the Dutch word sense disambigua- tion data submitted to SENSEVAL-2, and give preliminary results on the data using a WSD system based on memory-based learning and statistical keyword …...

This paper discusses the supervised learn- ing of morphology using stochastic trans- ducers, trained using the Expectation- Maximization (EM) algorithm. Two ap- proaches are presented: first, using the transducers directly …...

We describe a case study in which a memory-based learning algorithm is trained to simultaneously chunk sentences and assign grammatical function tags to these chunks. We compare the algo- rithm’s …...

Previous work has argued that memory-based learning is better than abstraction-based learn- ing for a set of language learning tasks. In this paper, we first attempt to generalize these re- …...

This paper proposes a hybrid of hand- crafted rules and a machine learning method for chunking Korean. In the par- tially free word-order languages such as Korean and Japanese, a …...

This paper presents a deterministic dependency parser based on memory-based learning, which parses English text in linear time. When trained and evaluated on the Wall Street Journal sec- tion of …...

We describe a method for enriching the output of a parser with information available in a corpus. The method is based on graph rewriting using memory- based learning, applied to …...

This paper reports the results of experiments using memory-based learning to guide a de- terministic dependency parser for unrestricted natural language text. Using data from a small treebank of Swedish, …...

To facilitate the use of syntactic infor- mation in the study of child language acquisition, a coding scheme for Gram- matical Relations (GRs) in transcripts of parent-child dialogs has been …...

Most studies in statistical or machine learning based authorship attribution focus on two or a few authors. This leads to an overestimation of the importance of the features extracted from …...

Instance sampling is a method to balance ex- tremely skewed training sets as they occur, for example, in machine learning settings for anaphora resolution. Here, the number of nega- tive …...

In this paper, we discuss the importance of the quality against the quantity of automatically ex- tracted examples for word sense disambiguation (WSD). We first show that we can build …...