what dataset was used?
CoNLL_X bosque data, News data by Lusa agency, Sports news data
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.
Nowadays, a large amount of information is produced and shared in unstructured form, mostly unstructured text BIBREF0 , BIBREF1 . This information can be exploited in decision making processes but, to be useful, it should be transformed and presented in ways that make its intrinsic knowledge more readily intelligible. For that, we need efficient methods and tools that quickly extract useful information from unstructured text collections. Such demand can be observed, for instance, in Biology, where researchers, in order to be abreast of all developments, need to analyse new biomedical literature on a daily basis BIBREF2 . Another application is on fraud and corruption studies where the network information — the set of actors and their relationships — is implicitly stored in unstructured natural-language documents BIBREF3 . Hence, text mining and information extraction are required to pre-process the texts in order to extract the entities and the relations between them. Information extraction is a challenging task mainly due to the ambiguous features of natural-language. Moreover, most tools need to be adapted to different human languages and to different domains BIBREF4 . In fact, the language of the processed texts is still the decisive factor when choosing among existing information extraction technologies. This is also true for the task of entity extraction (Named Entity Recognition - NER). For several reasons, text mining tools are typically first developed for English and only afterwards extended to other languages. Thus, there are still relatively few text mining tools for Portuguese and even less that are freely accessible. In particular, for the named entities recognition task in Portuguese texts, we find three extractors available: Alchemy, Zemanta and Rembrandt BIBREF5 . We also find some studies where the measures ( INLINEFORM0 , INLINEFORM1 and INLINEFORM2 ) for those extractors are computed and compared BIBREF6 , but their comparative effectiveness remains domain and final purpose dependent. In this work, we present PAMPO (PAttern Matching and POs tagging based algorithm for NER), a new method to automatically extract named entities from unstructured texts, applicable to the Portuguese language but potentially adaptable to other languages as well. The method relies on flexible pattern matching, part-of-speech tagging and lexical-based rules. All steps are implemented using free software and taking advantage of various existing packages. The process has been developed using as case-study a specific book written in Portuguese, but it has since been used in other applications and successfully tested in different text collections. In this paper, we describe the evaluation procedures on independent textual collections, and produce a comparative study of PAMPO with other existing tools for NER.