Despite significant advances in named entity extraction technologies, state-of-the-art extraction tools achieve insufficient accuracy rates for practical use in many operational settings. However, they are not all prone to the same types of error, suggesting that substantial improvements may be achieved via appropriate combinations of existing tools, provided their behavior can be accurately ch...

This work studies Named Entity Classification (NEC) for Catalan without making use of large annotated resources of this language. Two views are explored and compared, namely exploiting solely the Catalan resources, and a direct training of bilingual classification models (Spanish and Catalan), given that a large collection of annotated examples is available for Spanish. The empirical results ob...

As the number of published scienti c papers grows everyday, there is also an increasing necessity for automated named entity recognition (NER) systems capable of identifying relevant entities mentioned in a given text, such as chemical entities. Since high precision values are crucial to deliver useful results, we developed a NER method, Identifying Chemical Entities (ICE), which was tuned for ...

We present the Global Health Monitor, an online Web-based system for detecting and mapping infectious disease outbreaks that appear in news stories. The system analyzes English news stories from news feed providers, classifies them for topical relevance and plots them onto a Google map using geo-coding information, helping public health workers to monitor the spread of diseases in a geo-tempora...

In this paper we describe the first evaluation contest (track) for Portuguese whose goal was to detect and classify relations between named entities in running text, called ReRelEM. Given a collection annotated with named entities belonging to ten different semantic categories, we marked all relationships between them within each document. We used the following fourfold relationship classificat...

This paper presents a collection of texts manually annotated with named entities in context, which was used for HAREM, the first evaluation contest for named entity recognizers for Portuguese. We discuss the options taken and the originality of our approach compared with previous evaluation initiatives in the area. We document the choice of categories, their quantitative weight in the overall c...

This paper presents an approach to mining information relating people, places, organizations and events extracted from Wikipedia and linking them on a time scale. The approach consists of two phases: (1) identifying relevant pages categorizing the articles as containing people, places or organizations; (2) generating timeline linking named entities and extracting events and their time frame. We...

In this paper, we present Second HAREM, the second edition of an evaluation campaign for Portuguese, addressing named entity recognition (NER). This second edition also included two new tracks: the recognition and normalization of temporal entities (proposed by a group of participants, and hence not covered on this paper) and ReRelEM, the detection of semantic relations between named entities. ...

This paper describes our system used in the ACL 2015 Workshop on Noisy Usergenerated Text Shared Task for Named Entity Recognition (NER) in Twitter. Our system uses Conditional Random Fields to train two separate classifiers for the two evaluations: predicting 10 fine-grained types, and segmenting named entities. We focus our efforts on generating word representations from large amount of unlab...

In this paper, we propose a multi-criteria based active learning approach and effectively apply it to named entity recognition. Active learning targets to minimize the human annotation efforts by selecting examples for labeling. To maximize the contribution of the selected examples, we consider the multiple criteria: informativeness, representativeness and diversity and propose measures to quan...