This paper presents a Named Entity Extraction (NEE) system for the CoNLL-2003 shared task competition. As in the past year edition (Carreras et al., 2002a), we have approached the task by treating the two main sub–tasks of the problem, recognition (NER) and classification (NEC), sequentially and independently with separate modules. Both modules are machine learning based systems, which make use...

A lightweight fact extractor is presented that utilizes XML tools, such as XPath and XSLT, to extract static information from C++ source code programs. The source code is first converted into an XML representation, srcML, to facilitate the use of a wide variety of XML tools. The method is deemed lightweight because only a partial parsing of the source is done. Additionally, the technique is qui...

This letter presents an algorithm, CrySSMEx, for extracting minimal finite state machine descriptions of dynamic systems such as recurrent neural networks. Unlike previous algorithms, CrySSMEx is parameter free and deterministic, and it efficiently generates a series of increasingly refined models. A novel finite stochastic model of dynamic systems and a novel vector quantization function have ...

This paper presents a system which creates a KWIC index of WWW texts in Japanese by automatic summarization. The system consists of three modules: a WWW spider, an extractor of important sentences, and a sentence summarizer. The most effective module is the last one which employs a robust and fairly accurate Japanese parser: KNP. It segments an input sentence into phrases or simple sentences an...

A fuzzy extractor is a security primitive that allows for reproducible extraction of an almost uniform key from a non-uniform noisy source. We analyze a fuzzy extractor scheme that uses universal hash functions for both information reconciliation and privacy amplification. This is a useful scheme when the number of error patterns likely to occur is limited, regardless of the error probabilities...

Studies of different term extractors on a corpus of the biomedical domain revealed decreasing performances when applied to highly technical texts. Facing the difficulty or impossibility to customize existing tools, we developed a tunable term extractor. It exploits linguistic-based rules in combination with the reuse of existing terminologies, i.e. exogenous disambiguation. Experiments reported...