PAMPO: using pattern matching and pos-tagging for effective Named Entities recognition in Portuguese
This work addresses the problem of extracting named entities from Portuguese text, offering a domain-specific solution that is incremental in nature.
The paper tackled named entity recognition in Portuguese by introducing PAMPO, a pattern matching and POS tagging algorithm, which achieved considerable improvements in recall and F1 scores compared to existing alternatives.
This paper deals with the entity extraction task (named entity recognition) of a text mining process that aims at unveiling non-trivial semantic structures, such as relationships and interaction between entities or communities. In this paper we present a simple and efficient named entity extraction algorithm. The method, named PAMPO (PAttern Matching and POs tagging based algorithm for NER), relies on flexible pattern matching, part-of-speech tagging and lexical-based rules. It was developed to process texts written in Portuguese, however it is potentially applicable to other languages as well. We compare our approach with current alternatives that support Named Entity Recognition (NER) for content written in Portuguese. These are Alchemy, Zemanta and Rembrandt. Evaluation of the efficacy of the entity extraction method on several texts written in Portuguese indicates a considerable improvement on $recall$ and $F_1$ measures.