Analysis of Twitter data for postmarketing surveillance in pharmacovigilance

Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs af- ter release for use by the general pop- ulation, but suffers from under-reporting and limited coverage. Automatic meth- ods for detecting drug effect reports, es- pecially for social media, could vastly in- crease the scope of PMS. Very few auto- matic PMS methods are currently avail- able, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We de- scribe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools per- form well for tweet-level language iden- tification and tweet-level sentiment anal- ysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse- vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap seman- tic types provide a very promising ba- sis for identifying drug effect mentions in tweets

LORE: a model for the detection of fine-grained locative references in tweets

[EN] Extracting geospatially rich knowledge from tweets is of utmost importance for location-based systems in emergency services to raise situational awareness about a given crisis-related incident, such as earthquakes, floods, car accidents, terrorist attacks, shooting attacks, etc. The problem is that the majority of tweets are not geotagged, so we need to resort to the messages in the search of geospatial evidence. In this context, we present LORE, a location-detection system for tweets that leverages the geographic database GeoNames together with linguistic knowledge through NLP techniques. One of the main contributions of this model is to capture fine-grained complex locative references, ranging from geopolitical entities and natural geographic references to points of interest and traffic ways. LORE outperforms state-of-the-art open-source location-extraction systems (i.e. Stanford NER, spaCy, NLTK and OpenNLP), achieving an unprecedented trade-off between precision and recall. Therefore, our model provides not only a quantitative advantage over other well-known systems in terms of performance but also a qualitative advantage in terms of the diversity and semantic granularity of the locative references extracted from the tweets.Financial support for this research has been provided by the Spanish Ministry of Science, Innovation and Universities [grant number RTC 2017-6389-5], and the European Union's Horizon 2020 research and innovation program [grant number 101017861: project SMARTLAGOON]. We also thank Universidad de Granada for their financial support to the first author through the Becas de Iniciacion para estudiantes de Master 2018 del Plan Propio de la UGR.Fernández-Martínez, NJ.; Periñán-Pascual, C. (2021). LORE: a model for the detection of fine-grained locative references in tweets. Onomázein. (52):195-225. https://doi.org/10.7764/onomazein.52.111952255

COPIOUS: A gold standard corpus of named entities towards extracting species occurrence from biodiversity literature

Background Species occurrence records are very important in the biodiversity domain. While several available corpora contain only annotations of species names or habitats and geographical locations, there is no consolidated corpus that covers all types of entities necessary for extracting species occurrence from biodiversity literature. In order to alleviate this issue, we have constructed the COPIOUS corpus—a gold standard corpus that covers a wide range of biodiversity entities. Results Two annotators manually annotated the corpus with five categories of entities, i.e. taxon names, geographical locations, habitats, temporal expressions and person names. The overall inter-annotator agreement on 200 doubly-annotated documents is approximately 81.86% F-score. Amongst the five categories, the agreement on habitat entities was the lowest, indicating that this type of entity is complex. The COPIOUS corpus consists of 668 documents downloaded from the Biodiversity Heritage Library with over 26K sentences and more than 28K entities. Named entity recognisers trained on the corpus could achieve an F-score of 74.58%. Moreover, in recognising taxon names, our model performed better than two available tools in the biodiversity domain, namely the SPECIES tagger and the Global Name Recognition and Discovery. More than 1,600 binary relations of Taxon-Habitat, Taxon-Person, Taxon-Geographical locations and Taxon-Temporal expressions were identified by applying a pattern-based relation extraction system to the gold standard. Based on the extracted relations, we can produce a knowledge repository of species occurrences. Conclusion The paper describes in detail the construction of a gold standard named entity corpus for the biodiversity domain. An investigation of the performance of named entity recognition (NER) tools trained on the gold standard revealed that the corpus is sufficiently reliable and sizeable for both training and evaluation purposes. The corpus can be further used for relation extraction to locate species occurrences in literature—a useful task for monitoring species distribution and preserving the biodiversity