Naivno terminološko označivanje zakonskih tekstova u slovačkom – može li biti korisno?

Correct automatic terminological annotation of texts in a corpus can be sometimes a challenging task, especially for moderately or heavily inflected languages with relatively free word order. We explore the possibility of simple annotation based on sequence matching of lemmatized texts to annotate Slovak language corpus with IATE terminological entries. The accuracy of annotating legal language is very good when annotating multiword terms, while accuracy of single-word terms can be increased by applying simple filters based on word lengths and blacklisting most frequent false positives.Ispravna automatska terminološka anotacija tekstova u korpusu ponekad može biti izazovan zadatak, posebno za iznimno flektivne jezike s razmjerno slobodnim redoslijedom riječi. U članku istražujemo mogućnost jednostavne anotacije na temelju podudarnosti lematiziranih tekstova kako bi korpus slovačkoga jezika bio anotiran terminološkim zapisima IATE. Točnost anotacije višerječnih termina vrlo je dobra, dok se točnost jednorječnih termina može povisiti primjenom jednostavnih filtara na temelju duljine riječi i stavljanja na crnu listu najčešćih lažnih pozitivnih rezultata

Distribution of Terms Across Genres in the Annotated Lithuanian Cybersecurity Corpus

The paper provides results of the frequential distribution analysis of cybersecurity terms used in the Lithuanian cybersecurity corpus composed of texts of different genres. The research focuses on the following aspects: overall distribution of cybersecurity terms (their density and diversity) across genres, distribution of English and English-Lithuanian terms and their usage patterns in Lithuanian sentences, and, finally, the most frequent cybersecurity terms and their thematic groups in each genre. The research was performed in several stages: compilation of a cybersecurity corpus and its subdivision into genre-specific subcorpora, manual annotation of cybersecurity terms, automatic lemmatisation of annotated terms and, finally, quantitative analysis of the distribution of the terms across the subcorpora. The results reveal the similarities and differences of the use of cybersecurity terminology across genres which are important to consider to get a complete picture of terminology usage trends in this domain

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

Band gap information extraction from materials science literature – a pilot study

Purpose The purpose of this paper is to present a preliminary work on extracting band gap information of materials from academic papers. With increasing demand for renewable energy, band gap information will help material scientists design and implement novel photovoltaic (PV) cells. Design/methodology/approach The authors collected 1.44 million titles and abstracts of scholarly articles related to materials science, and then filtered the collection to 11,939 articles that potentially contain relevant information about materials and their band gap values. ChemDataExtractor was extended to extract information about PV materials and their band gap information. Evaluation was performed on randomly sampled information records of 415 papers. Findings The findings of this study show that the current system is able to correctly extract information for 51.32% articles, with partially correct extraction for 36.62% articles and incorrect for 12.04%. The authors have also identified the errors belonging to three main categories pertaining to chemical entity identification, band gap information and interdependency resolution. Future work will focus on addressing these errors to improve the performance of the system. Originality/value The authors did not find any literature to date on band gap information extraction from academic text using automated methods. This work is unique and original. Band gap information is of importance to materials scientists in applications such as solar cells, light emitting diodes and laser diodes

Distribution of Terms Across Genres in the Annotated Lithuanian Cybersecurity Corpus

CC BYThe paper provides results of the frequential distribution analysis of cybersecurity terms used in the Lithuanian cybersecurity corpus composed of texts of different genres. The research focuses on the following aspects: overall distribution of cybersecurity terms (their density and diversity) across genres, distribution of English and English-Lithuanian terms and their usage patterns in Lithuanian sentences, and, finally, the most frequent cybersecurity terms and their thematic groups in each genre. The research was performed in several stages: compilation of a cybersecurity corpus and its subdivision into genre-specific subcorpora, manual annotation of cybersecurity terms, automatic lemmatisation of annotated terms and, finally, quantitative analysis of the distribution of the terms across the subcorpora. The results reveal the similarities and differences of the use of cybersecurity terminology across genres which are important to consider to get a complete picture of terminology usage trends in this domain

Constructing a semantic predication gold standard from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Semantic relations increasingly underpin biomedical text mining and knowledge discovery applications. The success of such practical applications crucially depends on the quality of extracted relations, which can be assessed against a gold standard reference. Most such references in biomedical text mining focus on narrow subdomains and adopt different semantic representations, rendering them difficult to use for benchmarking independently developed relation extraction systems. In this article, we present a multi-phase gold standard annotation study, in which we annotated 500 sentences randomly selected from MEDLINE abstracts on a wide range of biomedical topics with 1371 semantic predications. The UMLS Metathesaurus served as the main source for conceptual information and the UMLS Semantic Network for relational information. We measured interannotator agreement and analyzed the annotations closely to identify some of the challenges in annotating biomedical text with relations based on an ontology or a terminology.</p> <p>Results</p> <p>We obtain fair to moderate interannotator agreement in the practice phase (0.378-0.475). With improved guidelines and additional semantic equivalence criteria, the agreement increases by 12% (0.415 to 0.536) in the main annotation phase. In addition, we find that agreement increases to 0.688 when the agreement calculation is limited to those predications that are based only on the explicitly provided UMLS concepts and relations.</p> <p>Conclusions</p> <p>While interannotator agreement in the practice phase confirms that conceptual annotation is a challenging task, the increasing agreement in the main annotation phase points out that an acceptable level of agreement can be achieved in multiple iterations, by setting stricter guidelines and establishing semantic equivalence criteria. Mapping text to ontological concepts emerges as the main challenge in conceptual annotation. Annotating predications involving biomolecular entities and processes is particularly challenging. While the resulting gold standard is mainly intended to serve as a test collection for our semantic interpreter, we believe that the lessons learned are applicable generally.</p

Using rule-based natural language processing to improve disease normalization in biomedical text

Background and objective: In order for computers to extract useful information from unstructured text, a concept normalization system is needed to link relevant concepts in a text to sources that contain further information about the concept. Popular concept normalization tools in the biomedical field are dictionarybased. In this study we investigate the usefulness of natural language processing (NLP) as an adjunct to dictionary-based concept normalization. Methods: We compared the performance of two biomedical concept normalization systems, MetaMap and Peregrine, on the Arizona Disease Corpus, with and without the use of a rule-based NLP module. Performance was assessed for exact and inexact boundary matching of the system annotations with those of the gold standard and for concept identifier matching. Results: Without the NLP module, MetaMap and Peregrine attained F-scores of 61.0% and 63.9%, respectively, for exact boundary matching, and 55.1% and 56.9% for concept identifier matching. With the aid of the NLP module, the F-scores of MetaMap and Peregrine improved to 73.3% and 78.0% for boundary matching, and to 66.2% and 69.8% for concept identifier matching. For inexact boundary matching, performances further increased to 85.5% and 85.4%, and to 73.6% and 73.3% for concept identifier matching. Conclusions: We have shown the added value of NLP for the recognition and normalization of diseases with MetaMap and Peregrine. The NLP module is general and can be applied in combination with any concept normalization system. Whether its use for concept types other than disease is equally advantageous remains to be investigated

Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events

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