Automated recognition and post-coordination of complex clinical terms

One of the key tasks in integrating guideline-based decision support systems with the electronic patient record is the mapping of clinical terms contained in both guidelines and patient notes to a common, controlled terminology. However, a vocabulary of pre-coordinated terms cannot cover every possible variation - clinical terms are often highly compositional and complex. We present a rule-based approach for automated recognition and post-coordination of clinical terms using minimal, morpheme-based thesauri, neoclassical combining forms and part-of-speech analysis. The process integrates MetaMap with the open-source GATE framework

LEAP4FNSSA lexicon: Towards a new dataset of keywords dealing with food security

The main objective of the project LEAP4FNSSA (Long-term EU-AU Research and Innovation Partnership for Food and Nutrition Security and Sustainable Agriculture) is to provide a tool for European and African institutions to engage in a sustainable partnership platform for research and innovation on Food and Nutrition Security, and Sustainable Agriculture (FNSSA). The FNSSA roadmap facilitates the involvement of stakeholders for addressing and linking research to innovation dealing with food security issues. In this context, the LEAP4FNSSA project supports the driving of the roadmap. Research and innovation activities were captured in different data, i.e. LEAP4FNSSA database and heterogeneous textual data including project reports, websites, scientific publications, workshop reports and student theses. The Knowledge Extractor Pipeline System (KEOPS) was implemented to support the processing and analysis of textual data associated with FNSSA activities. KEOPS is based on the LEAP4FNSSA lexicon presented in this data paper. The LEAP4FNSSA lexicon composed of 331 keywords associated with 12 concepts of the food security domain is the result of 3 steps of work and brainstorming. The lexicon enables the capturing of research and innovation topics dealing with food security and conducted by African and European partners. This data paper presents the obtained lexicon and a summary of the method to build it. (c) 2022 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/

In no uncertain terms : a dataset for monolingual and multilingual automatic term extraction from comparable corpora

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in low inter-annotator agreement. There is a large need for well-documented, manually validated datasets, especially in the rising field of multilingual term extraction from comparable corpora, which presents a unique new set of challenges. In this paper, a new approach is presented for both monolingual and multilingual term annotation in comparable corpora. The detailed guidelines with different term labels, the domain- and language-independent methodology and the large volumes annotated in three different languages and four different domains make this a rich resource. The resulting datasets are not just suited for evaluation purposes but can also serve as a general source of information about terms and even as training data for supervised methods. Moreover, the gold standard for multilingual term extraction from comparable corpora contains information about term variants and translation equivalents, which allows an in-depth, nuanced evaluation

Mining protein function from text using term-based support vector machines

<p>Abstract</p> <p>Background</p> <p>Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents.</p> <p>Results</p> <p>The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.</p> <p>Conclusion</p> <p>A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2.</p

Named Entity Recognition for Bacterial Type IV Secretion Systems

Research on specialized biological systems is often hampered by a lack of consistent terminology, especially across species. In bacterial Type IV secretion systems genes within one set of orthologs may have over a dozen different names. Classifying research publications based on biological processes, cellular components, molecular functions, and microorganism species should improve the precision and recall of literature searches allowing researchers to keep up with the exponentially growing literature, through resources such as the Pathosystems Resource Integration Center (PATRIC, patricbrc.org). We developed named entity recognition (NER) tools for four entities related to Type IV secretion systems: 1) bacteria names, 2) biological processes, 3) molecular functions, and 4) cellular components. These four entities are important to pathogenesis and virulence research but have received less attention than other entities, e.g., genes and proteins. Based on an annotated corpus, large domain terminological resources, and machine learning techniques, we developed recognizers for these entities. High accuracy rates (>80%) are achieved for bacteria, biological processes, and molecular function. Contrastive experiments highlighted the effectiveness of alternate recognition strategies; results of term extraction on contrasting document sets demonstrated the utility of these classes for identifying T4SS-related documents

Semi-automated Ontology Generation for Biocuration and Semantic Search

Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org

D-TERMINE : data-driven term extraction methodologies investigated

Automatic term extraction is a task in the field of natural language processing that aims to automatically identify terminology in collections of specialised, domain-specific texts. Terminology is defined as domain-specific vocabulary and consists of both single-word terms (e.g., corpus in the field of linguistics, referring to a large collection of texts) and multi-word terms (e.g., automatic term extraction). Terminology is a crucial part of specialised communication since terms can concisely express very specific and essential information. Therefore, quickly and automatically identifying terms is useful in a wide range of contexts. Automatic term extraction can be used by language professionals to find which terms are used in a domain and how, based on a relevant corpus. It is also useful for other tasks in natural language processing, including machine translation. One of the main difficulties with term extraction, both manual and automatic, is the vague boundary between general language and terminology. When different people identify terms in the same text, it will invariably produce different results. Consequently, creating manually annotated datasets for term extraction is a costly, time- and effort- consuming task. This can hinder research on automatic term extraction, which requires gold standard data for evaluation, preferably even in multiple languages and domains, since terms are language- and domain-dependent. Moreover, supervised machine learning methodologies rely on annotated training data to automatically deduce the characteristics of terms, so this knowledge can be used to detect terms in other corpora as well. Consequently, the first part of this PhD project was dedicated to the construction and validation of a new dataset for automatic term extraction, called ACTER – Annotated Corpora for Term Extraction Research. Terms and Named Entities were manually identified with four different labels in twelve specialised corpora. The dataset contains corpora in three languages and four domains, leading to a total of more than 100k annotations, made over almost 600k tokens. It was made publicly available during a shared task we organised, in which five international teams competed to automatically extract terms from the same test data. This illustrated how ACTER can contribute towards advancing the state-of-the-art. It also revealed that there is still a lot of room for improvement, with moderate scores even for the best teams. Therefore, the second part of this dissertation was devoted to researching how supervised machine learning techniques might contribute. The traditional, hybrid approach to automatic term extraction relies on a combination of linguistic and statistical clues to detect terms. An initial list of unique candidate terms is extracted based on linguistic information (e.g., part-of-speech patterns) and this list is filtered based on statistical metrics that use frequencies to measure whether a candidate term might be relevant. The result is a ranked list of candidate terms. HAMLET – Hybrid, Adaptable Machine Learning Approach to Extract Terminology – was developed based on this traditional approach and applies machine learning to efficiently combine more information than could be used with a rule-based approach. This makes HAMLET less susceptible to typical issues like low recall on rare terms. While domain and language have a large impact on results, robust performance was reached even without domain- specific training data, and HAMLET compared favourably to a state-of-the-art rule-based system. Building on these findings, the third and final part of the project was dedicated to investigating methodologies that are even further removed from the traditional approach. Instead of starting from an initial list of unique candidate terms, potential terms were labelled immediately in the running text, in their original context. Two sequential labelling approaches were developed, evaluated and compared: a feature- based conditional random fields classifier, and a recurrent neural network with word embeddings. The latter outperformed the feature-based approach and was compared to HAMLET as well, obtaining comparable and even better results. In conclusion, this research resulted in an extensive, reusable dataset and three distinct new methodologies for automatic term extraction. The elaborate evaluations went beyond reporting scores and revealed the strengths and weaknesses of the different approaches. This identified challenges for future research, since some terms, especially ambiguous ones, remain problematic for all systems. However, overall, results were promising and the approaches were complementary, revealing great potential for new methodologies that combine multiple strategies