Isabel Moreno

MaNER: A MedicAl Named Entity Recogniser

This paper describes a medicinal products and active ingredients named entity recogniser (MaNER) for Spanish technical documents. This rule-based system uses high quality and low-maintenance lexicons. Our results (F-measure 90 %) proves that dictionary-based approaches, without any deep natural language processing (e.g. POS tagging), can achieve a high performance in this task. Our system obtains better results when compared to similar systems.

An Active Ingredients Entity Recogniser System Based on Profiles

This paper describes an active ingredients named entity recogniser. Our machine learning system, which is language and domain independent, employs unsupervised feature generation and weighting from the training data. The proposed automatic feature extraction process is based on generating a profile for the given entity without traditional knowledge resources (such as dictionaries). Our results (F1 87.3 % [95 %CI: 82.07–92.53]) proves that unsupervised feature generation can achieve a high performance for this task.

Named Entity Classification Based on Profiles: A Domain Independent Approach

This paper presents a Named Entity Classification system, which uses profiles and machine learning based on [6]. Aiming at confirming its domain independence, it is tested on two domains: general - CONLL2002 corpus, and medical - DrugSemantics gold standard. Given our overall results (CONLL2002, F1 = 67.06; DrugSemantics, F1 = 71.49), our methodology has proven to be domain independent.

DrugSemantics: A corpus for Named Entity Recognition in Spanish Summaries of Product Characteristics

For the healthcare sector, it is critical to exploit the vast amount of textual health-related information. Nevertheless, healthcare providers have difficulties to benefit from such quantity of data during pharmacotherapeutic care. The problem is that such information is stored in different sources and their consultation time is limited. In this context, Natural Language Processing techniques can be applied to efficiently transform textual data into structured information so that it could be used in critical healthcare applications, being of help for physicians in their daily workload, such as: decision support systems, cohort identification, patient management, etc. Any development of these techniques requires annotated corpora. However, there is a lack of such resources in this domain and, in most cases, the few ones available concern English. This paper presents the definition and creation of DrugSemantics corpus, a collection of Summaries of Product Characteristics in Spanish. It was manually annotated with pharmacotherapeutic named entities, detailed in DrugSemantics annotation scheme. Annotators were a Registered Nurse (RN) and two students from the Degree in Nursing. The quality of DrugSemantics corpus has been assessed by measuring its annotation reliability (overall F=79.33% [95%CI: 78.35-80.31]), as well as its annotation precision (overall P=94.65% [95%CI: 94.11-95.19]). Besides, the gold-standard construction process is described in detail. In total, our corpus contains more than 2000 named entities, 780 sentences and 226,729 tokens. Last, a Named Entity Classification module trained on DrugSemantics is presented aiming at showing the quality of our corpus, as well as an example of how to use it.

From Sentences to Documents: Extending Abstract Meaning Representation for Understanding Documents

Research partially supported by the Spanish Government (grants TIN2015-65100-R; TIN2015-65136-C02-2-R).

A Domain and Language Independent Named Entity Classification Approach Based on Profiles and Local Information.

This paper presents a Named Entity Classification system, which employs machine learning. Our methodology employs local entity information and profiles as feature set. All features are generated in an unsupervised manner. It is tested on two different data sets: (i) DrugSemantics Spanish corpus (Overall F1 = 74.92), whose results are in-line with the state of the art without employing external domain-specific resources. And, (ii) English CONLL2003 dataset (Overall F1 = 81.40), although our results are lower than previous work, these are reached without external knowledge or complex linguistic analysis. Last, using the same configuration for the two corpora, the difference of overall F1 is only 6.48 points (DrugSemantics = 74.92 versus CoNLL2003 = 81.40). Thus, this result supports our hypothesis that our approach is language and domain independent and does not require any external knowledge or complex linguistic analysis.

Person name discrimination in the dossier-GPLSI at the university of Alicante

We present the Dossier-GPLSI, a system for the automatic generation of press dossiers for organizations. News are downloaded from online newspapers and are automatically classified. We describe specifically a module for the discrimination of person names. Three different approaches are analyzed and evaluated, each one using different kind of information, as semantic information, domain information and statistical evidence. We demonstrate that this module reaches a very good performance, and can be integrated in the Dossier-GPLSI system.