Suchergebnisse

Dipterocarps, belonging to the family Dipterocarpaceae, are economically and ecologically important in the Philippines due to their timber value as well as contribution to wildlife habitat, climatic balance and stronghold on water releases. The supra-annual mass flowering of dipterocarps occurs in irregular intervals of two to ten years, possibly synchronously across Asia. Predicting the likelihood of their regeneration, to subsequently make plans regarding species for reforestation, can be aided by providing access to a knowledge base of dipterocarps, including information on the factors that affect their flowering and fruiting patterns. The content of the knowledge base could be enriched with literature-derived data on dipterocarp occurrence, reproductive condition and habitat. We aim to develop information extraction methods to automatically extract concepts relevant to the reproductive cycle of dipterocarps to enable searching for more descriptive information of mass flowering from the literature. In previous work, we created a corpus of text selected from the Biodiversity Heritage Library (BHL), scholarly articles, books and government agency reports with manually labelled taxon names, geographic locations, dates, habitat descriptions, authorities, and names of herbaria (in the case of collected specimens) to aid in determining the distribution of dipterocarps. Importantly, the species' reproductive condition, e.g., whether it is in fruit, in flower or sterile, was also annotated to enable the derivation of phenological patterns and the identification of factors that trigger mass flowering. In this work, we focus our efforts on the automatic annotation of information on a species' reproductive condition, which we cast as a named entity recognition (NER) task. To this end, we have developed machine learning-based models building upon conditional random fields (Lafferty et al. 2001). The resulting new NER tool has been integrated as a new component in Argo (Rak et al. 2012) to allow for the linking of ...

Dipterocarps, belonging to the family Dipterocarpaceae, are economically and ecologically important in the Philippines due to their timber value as well as contribution to wildlife habitat, climatic balance and stronghold on water releases. The supra-annual mass flowering of dipterocarps occurs in irregular intervals of two to ten years, possibly synchronously across Asia. Predicting the likelihood of their regeneration, to subsequently make plans regarding species for reforestation, can be aided by providing access to a knowledge base of dipterocarps, including information on the factors that affect their flowering and fruiting patterns. The content of the knowledge base could be enriched with literature-derived data on dipterocarp occurrence, reproductive condition and habitat. We aim to develop information extraction methods to automatically extract concepts relevant to the reproductive cycle of dipterocarps to enable searching for more descriptive information of mass flowering from the literature. In previous work, we created a corpus of text selected from the Biodiversity Heritage Library (BHL), scholarly articles, books and government agency reports with manually labelled taxon names, geographic locations, dates, habitat descriptions, authorities, and names of herbaria (in the case of collected specimens) to aid in determining the distribution of dipterocarps. Importantly, the species' reproductive condition, e.g., whether it is in fruit, in flower or sterile, was also annotated to enable the derivation of phenological patterns and the identification of factors that trigger mass flowering. In this work, we focus our efforts on the automatic annotation of information on a species' reproductive condition, which we cast as a named entity recognition (NER) task. To this end, we have developed machine learning-based models building upon conditional random fields (Lafferty et al. 2001). The resulting new NER tool has been integrated as a new component in Argo (Rak et al. 2012) to allow for the linking of information on reproductive condition, with species names and habitat descriptions. This will eventually enable the generation of more descriptive occurrence data that includes information on reproductive and habitat conditions of dipterocarps. This serves as a step towards a more comprehensive basis of restoration efforts for dipterocarp forests.

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative's work throughout Europe in order to boost progress and innovation in our field. ; Postprint (published version)

This article provides an overview of the dissemination work carried out in META-NET from 2010 until early 2014; we describe its impact on the regional, national and international level, mainly with regard to politics and the situation of funding for LT topics. This paper documents the initiative's work throughout Europe in order to boost progress and innovation in our field. ; Postprint (published version)

Sometimes the normal course of events is disrupted by a particularly swift and profound change. Historians have often referred to such changes as "revolutions", and, though they have identified many of them, they have rarely supported their claims with statistical evidence. Here, we present a method to identify revolutions based on a measure of multivariate rate of change called Foote novelty. We define revolutions as those periods of time when the value of this measure is, by a non-parametric test, shown to significantly exceed the background rate. Our method also identifies conservative periods when the rate of change is unusually low. We apply it to several quantitative data sets that capture long-term political, social and cultural changes and, in some of them, identify revolutions - both well known and not. Our method is general and can be applied to any phenomenon captured by multivariate time series data of sufficient quality.

This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative's work throughout Europe in order to boost progress and innovation in our field. ; Peer Reviewed ; Postprint (author's final draft)

This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative's work throughout Europe in order to boost progress and innovation in our field. ; Peer Reviewed ; Postprint (author's final draft)

This article provides an overview of the dissemination work carried out in META-NET from 2010 until 2015; we describe its impact on the regional, national and international level, mainly with regard to politics and the funding situation for LT topics. The article documents the initiative's work throughout Europe in order to boost progress and innovation in our field. ; Peer reviewed