Optimising Selective Sampling for Bootstrapping Named Entity Recognition

Training a statistical named entity recognition system in a new domain requires costly manual annotation of large quantities of in-domain data. Active learning promises to reduce the annotation cost by selecting only highly informative data points. This paper is concerned with a real active learning experiment to bootstrap a named entity recognition system for a new domain of radio astronomical abstracts. We evaluate several committee-based metrics for quantifying the disagreement between classifiers built using multiple views, and demonstrate that the choice of metric can be optimised in simulation experiments with existing annotated data from different domains. A final evaluation shows that we gained substantial savings compared to a randomly sampled baseline. 1

A Survey of Biological Entity Recognition Approaches

There has been growing interest in the task of Named Entity Recognition (NER) and a lot of research has been done in this direction in last two decades. Particularly, a lot of progress has been made in the biomedical domain with emphasis on identifying domain-specific entities and often the task being known as Biological Named Entity Recognition (BER). The task of biological entity recognition (BER) has been proved to be a challenging task due to several reasons as identified by many researchers. The recognition of biological entities in text and the extraction of relationships between them have paved the way for doing more complex text-mining tasks and building further applications. This paper looks at the challenges perceived by the researchers in BER task and investigates the works done in the domain of BER by using the multiple approaches available for the task

Bootstrapping Named Entity Annotation by Means of Active Machine Learning: A Method for Creating Corpora

This thesis describes the development and in-depth empirical investigation of a method, called BootMark, for bootstrapping the marking up of named entities in textual documents. The reason for working with documents, as opposed to for instance sentences or phrases, is that the BootMark method is concerned with the creation of corpora. The claim made in the thesis is that BootMark requires a human annotator to manually annotate fewer documents in order to produce a named entity recognizer with a given performance, than would be needed if the documents forming the basis for the recognizer were randomly drawn from the same corpus. The intention is then to use the created named en- tity recognizer as a pre-tagger and thus eventually turn the manual annotation process into one in which the annotator reviews system-suggested annotations rather than creating new ones from scratch. The BootMark method consists of three phases: (1) Manual annotation of a set of documents; (2) Bootstrapping – active machine learning for the purpose of selecting which document to an- notate next; (3) The remaining unannotated documents of the original corpus are marked up using pre-tagging with revision. Five emerging issues are identified, described and empirically investigated in the thesis. Their common denominator is that they all depend on the real- ization of the named entity recognition task, and as such, require the context of a practical setting in order to be properly addressed. The emerging issues are related to: (1) the characteristics of the named entity recognition task and the base learners used in conjunction with it; (2) the constitution of the set of documents annotated by the human annotator in phase one in order to start the bootstrapping process; (3) the active selection of the documents to annotate in phase two; (4) the monitoring and termination of the active learning carried out in phase two, including a new intrinsic stopping criterion for committee-based active learning; and (5) the applicability of the named entity recognizer created during phase two as a pre-tagger in phase three. The outcomes of the empirical investigations concerning the emerging is- sues support the claim made in the thesis. The results also suggest that while the recognizer produced in phases one and two is as useful for pre-tagging as a recognizer created from randomly selected documents, the applicability of the recognizer as a pre-tagger is best investigated by conducting a user study involving real annotators working on a real named entity recognition task

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

Boosting Drug Named Entity Recognition using an Aggregate Classifier

AbstractObjectiveDrug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations.MethodsWe perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition.MaterialsOur approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers.ResultsAggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names.ConclusionWe conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations

Named Entity Recognition for the Estonian Language

Käesoleva töö raames uuriti eestikeelsetes tekstides nimega üksuste tuvastamise probleemi (NÜT) kasutades masinõppemeetodeid. NÜT süsteemi väljatöötamisel käsitleti kahte põhiaspekti: nimede tuvastamise algoritmi valikut ja nimede esitusviisi. Selleks võrreldi maksimaalse entroopia (MaxEnt) ja lineaarse ahela tinglike juhuslike väljade (CRF) masinõppemeetodeid. Uuriti, kuidas mõjutavad masinõppe tulemusi kolme liiki tunnused: 1) lokaalsed tunnused (sõnast saadud informatsioon), 2) globaalsed tunnused (sõna kõikide esinemiskontekstide tunnused) ja 3) väline teadmus (veebist saadud nimede nimekirjad). Masinõppe algoritmide treenimiseks ja võrdlemiseks annoteeriti käsitsi ajakirjanduse artiklitest koosnev tekstikorpus, milles märgendati asukohtade, inimeste, organisatsioonide ja ehitise-laadsete objektide nimed. Eksperimentide tulemusena ilmnes, et CRF ületab oluliselt MaxEnt meetodit kõikide vaadeldud nimeliikide tuvastamisel. Parim tulemus, 0.86 F1 skoor, saavutati annoteeritud korpusel CRF meetodiga, kasutades kombinatsiooni kõigist kolmest nime esitusvariandist. Vaadeldi ka süsteemi kohanemisvõimet teiste tekstižanridega spordi domeeni näitel ja uuriti võimalusi süsteemi kasutamiseks teistes keeltes nimede tuvastamisel.In this thesis we study the applicability of recent statistical methods to extraction of named entities from Estonian texts. In particular, we explore two fundamental design challenges: choice of inference algorithm and text representation. We compare two state-of-the-art supervised learning methods, Linear Chain Conditional Random Fields (CRF) and Maximum Entropy Model (MaxEnt). In representing named entities, we consider three sources of information: 1) local features, which are based on the word itself, 2) global features extracted from other occurrences of the same word in the whole document and 3) external knowledge represented by lists of entities extracted from the Web. To train and evaluate our NER systems, we assembled a text corpus of Estonian newspaper articles in which we manually annotated names of locations, persons, organisations and facilities. In the process of comparing several solutions we achieved F1 score of 0.86 by the CRF system using combination of local and global features and external knowledge

Recognition of medication information from discharge summaries using ensembles of classifiers

BACKGROUND: Extraction of clinical information such as medications or problems from clinical text is an important task of clinical natural language processing (NLP). Rule-based methods are often used in clinical NLP systems because they are easy to adapt and customize. Recently, supervised machine learning methods have proven to be effective in clinical NLP as well. However, combining different classifiers to further improve the performance of clinical entity recognition systems has not been investigated extensively. Combining classifiers into an ensemble classifier presents both challenges and opportunities to improve performance in such NLP tasks. METHODS: We investigated ensemble classifiers that used different voting strategies to combine outputs from three individual classifiers: a rule-based system, a support vector machine (SVM) based system, and a conditional random field (CRF) based system. Three voting methods were proposed and evaluated using the annotated data sets from the 2009 i2b2 NLP challenge: simple majority, local SVM-based voting, and local CRF-based voting. RESULTS: Evaluation on 268 manually annotated discharge summaries from the i2b2 challenge showed that the local CRF-based voting method achieved the best F-score of 90.84% (94.11% Precision, 87.81% Recall) for 10-fold cross-validation. We then compared our systems with the first-ranked system in the challenge by using the same training and test sets. Our system based on majority voting achieved a better F-score of 89.65% (93.91% Precision, 85.76% Recall) than the previously reported F-score of 89.19% (93.78% Precision, 85.03% Recall) by the first-ranked system in the challenge. CONCLUSIONS: Our experimental results using the 2009 i2b2 challenge datasets showed that ensemble classifiers that combine individual classifiers into a voting system could achieve better performance than a single classifier in recognizing medication information from clinical text. It suggests that simple strategies that can be easily implemented such as majority voting could have the potential to significantly improve clinical entity recognition

PhagePro: prophage finding tool

Dissertação de mestrado em BioinformáticaBacteriophages are viruses that infect bacteria and use them to reproduce. Their reproductive cycle can be lytic or lysogenic. The lytic cycle leads to the bacteria death, given that the bacteriophage hijacks hosts machinery to produce phage parts necessary to assemble a new complete bacteriophage, until cell wall lyse occurs. On the other hand, the lysogenic reproductive cycle comprises the bacteriophage genetic material in the bacterial genome, becoming a prophage. Sometimes, due to external stimuli, these prophages can be induced to perform a lytic cycle. Moreover, the lysogenic cycle can lead to significant modifications in bacteria, for example, antibiotic resistance. To that end, PhagePro was created. This tool finds and characterises prophages inserted in the bacterial genome. Using 42 features, three datasets were created and five machine learning algorithms were tested. All models were evaluated in two phases, during testing and with real bacterial cases. During testing, all three datasets reached the 98 % F1 score mark in their best result. In the second phase, the results of the models were used to predict real bacterial cases and the results compared to the results of two tools, Prophage Hunter and PHASTER. The best model found 110 zones out of 154 and the model with the best result in dataset 3 had 94 in common. As a final test, Agrobacterium fabrum strC68 was extensively analysed. The results show that PhagePro was capable of detecting more regions with proteins associated with phages than the other two tools. In the ligth of the results obtained, PhagePro has shown great potential in the discovery and characterisation of bacterial alterations caused by prophages.Bacteriófagos são vírus que infetam bactérias usando-as para garantir a manutenção do seu genoma. Este processo pode ser realizado por ciclo lítico ou lipogénico. O ciclo lítico consiste em usar a célula para seu proveito, criar bacteriófagos e lisar a célula. Por outro lado, no ciclo lipogénico o bacteriófago insere o seu código genético no genoma da bactéria, o que pode levar à transferência de genes de interesse, tornando-se importante uma monitorização dos profagos. Assim foi desenvolvido o PhagePro, uma ferramenta capaz de encontrar e caracterizar bacteriófagos em genomas bactérias. Foram criadas features para distinguir profagos de bactérias, criando três datasets e usando algoritmos de aprendizagem de máquina. Os modelos foram avaliados durante duas fases, a fase de teste e a fase de casos reais. Na primeira fase de testes, o melhor modelo do dataset 1 teve 98% de F1 score, dataset 2 teve 98% e do dataset 3 também teve 98%. Todos os modelos, para teste em casos reais, foram comparados com previsões de duas ferramentas Prophage Hunter e PHASTER. O modelo com os melhores resultados obteve 110 de 154 zonas em comum com as duas ferramentas e o modelo do dataset 3 teve 94 zonas. Por fim, foi feita a análise dos resultados da bactéria Agrobacterium fabrum strC68. Os resultados obtidos mostram resultados diferentes, mas válidos, as ferramentas comparadas, visto que o PhagePro consegue detectar zonas com proteínas associadas a fagos que as outras tools não conseguem. Em virtude dos resultados obtidos, PhagePro mostrou que é capaz de encontrar e caracterizar profagos em bactérias.Este estudo contou com o apoio da Fundação para a Ciência e Tecnologia (FCT) portuguesa no âmbito do financiamento estratégico da unidade UIDB/04469/2020. A obra também foi parcialmente financiada pelo Projeto PTDC/SAU-PUB/29182/2017 [POCI-01-0145-FEDER-029182]