-Prot/TrEMBL protein records, for incorporation into a specialized biological database of transport proteins named TCDB. We show that both learning approaches outperform rules created by hand by a human expert. As one of the first case studies involving two different approaches to updating a deployed database, both

better than reported by any other learning algorithm. IdentiFinder's performance is competitive with approaches based on handcrafted rules on mixed case text and superior on text where case information is not available. We also present a controlled experiment showing the effect of training set size

This paper proposes a hybrid of handcrafted rules and a machine learning method for chunking Korean. In the partially free word-order languages such as Korean and Japanese, a small number of rules dominate the performance due to their well-developed postpositions and endings. Thus

and was originally developed as a research prototype to create lemmatisation rules from training data. In this paper we compare the performance of the stemmer that uses handcrafted rules for Swedish, Danish and Norwegian as well one stemmer for Greek with CST’s lemmatiser that uses training data to extract

This paper presents a constraint-based morphological disambiguation approach that is applicable languages with complex morphology-specifically agglutinative lan-

application. This paper describes CRYSTAL, a system that learns text analysis rules automatically from examples. Rules induced byCRYSTAL achieve performance approaching that of hand-crafted rules. CRYSTAL has a particularly e#cient learning algorithm that is not improved by more extensive search

One method for detecting fraud is to check for suspicious changes in user behavior. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining techniques. Specifically, we use a rule-learning program to uncover indicators

We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of., ?, and ! as either a valid or invalid sentence boundary. The training procedure requires no hand-crafted rules

XML. We describe a new framework for inferring SciXML from a presentational level of description, such as PDF, using general purpose components such as Optical Character Recognition and expert hand-coded rules and then using supervised machine learning to provide the per-journal adaptation required

This paper describes our submission to Se-mEval 2014 Task 41 (aspect based senti-ment analysis). The current work is based on the assumption that it could be advan-tageous to connect the subtasks into one workflow, not necessarily following their given order. We took part in all four sub-tasks (aspect term extraction, aspect term