Hercules Dalianis


2024

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Deidentifying a Norwegian Clinical Corpus - an Effort to Create a Privacy-preserving Norwegian Large Clinical Language Model
Phuong Ngo | Miguel Tejedor | Therese Olsen Svenning | Taridzo Chomutare | Andrius Budrionis | Hercules Dalianis
Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)

The study discusses the methods and challenges of deidentifying and pseudonymizing Norwegian clinical text for research purposes. The results of the NorDeid tool for deidentification and pseudonymization on different types of protected health information were evaluated and discussed, as well as the extension of its functionality with regular expressions to identify specific types of sensitive information. The research used a clinical corpus of adult patients treated in a gastro-surgical department in Norway, which contains approximately nine million clinical notes. The study also highlights the challenges posed by the unique language and clinical terminology of Norway and emphasizes the importance of protecting privacy and the need for customized approaches to meet legal and research requirements.

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When Is a Name Sensitive? Eponyms in Clinical Text and Implications for De-Identification
Thomas Vakili | Tyr Hullmann | Aron Henriksson | Hercules Dalianis
Proceedings of the Workshop on Computational Approaches to Language Data Pseudonymization (CALD-pseudo 2024)

Clinical data, in the form of electronic health records, are rich resources that can be tapped using natural language processing. At the same time, they contain very sensitive information that must be protected. One strategy is to remove or obscure data using automatic de-identification. However, the detection of sensitive data can yield false positives. This is especially true for tokens that are similar in form to sensitive entities, such as eponyms. These names tend to refer to medical procedures or diagnoses rather than specific persons. Previous research has shown that automatic de-identification systems often misclassify eponyms as names, leading to a loss of valuable medical information. In this study, we estimate the prevalence of eponyms in a real Swedish clinical corpus. Furthermore, we demonstrate that modern transformer-based de-identification systems are more accurate in distinguishing between names and eponyms than previous approaches.

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Using BART to Automatically Generate Discharge Summaries from Swedish Clinical Text
Nils Berg | Hercules Dalianis
Proceedings of the First Workshop on Patient-Oriented Language Processing (CL4Health) @ LREC-COLING 2024

Documentation is a regular part of contemporary healthcare practices and one such documentation task is the creation of a discharge summary, which summarizes a care episode. However, to manually write discharge summaries is a time-consuming task, and research has shown that discharge summaries are often lacking quality in various respects. To alleviate this problem, text summarization methods could be applied on text from electronic health records, such as patient notes, to automatically create a discharge summary. Previous research has been conducted on this topic on text in various languages and with various methods, but no such research has been conducted on Swedish text. In this paper, four datasets extracted from a Swedish clinical corpora were used to fine-tune four BART language models to perform the task of summarizing Swedish patient notes into a discharge summary. Out of these models, the best performing model was manually evaluated by a senior, now retired, nurse and clinical coder. The evaluation results show that the best performing model produces discharge summaries of overall low quality. This is possibly due to issues in the data extracted from the Health Bank research infrastructure, which warrants further work on this topic.

2023

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Using Membership Inference Attacks to Evaluate Privacy-Preserving Language Modeling Fails for Pseudonymizing Data
Thomas Vakili | Hercules Dalianis
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

Large pre-trained language models dominate the current state-of-the-art for many natural language processing applications, including the field of clinical NLP. Several studies have found that these can be susceptible to privacy attacks that are unacceptable in the clinical domain where personally identifiable information (PII) must not be exposed. However, there is no consensus regarding how to quantify the privacy risks of different models. One prominent suggestion is to quantify these risks using membership inference attacks. In this study, we show that a state-of-the-art membership inference attack on a clinical BERT model fails to detect the privacy benefits from pseudonymizing data. This suggests that such attacks may be inadequate for evaluating token-level privacy preservation of PIIs.

2022

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Utility Preservation of Clinical Text After De-Identification
Thomas Vakili | Hercules Dalianis
Proceedings of the 21st Workshop on Biomedical Language Processing

Electronic health records contain valuable information about symptoms, diagnosis, treatment and outcomes of the treatments of individual patients. However, the records may also contain information that can reveal the identity of the patients. Removing these identifiers - the Protected Health Information (PHI) - can protect the identity of the patient. Automatic de-identification is a process which employs machine learning techniques to detect and remove PHI. However, automatic techniques are imperfect in their precision and introduce noise into the data. This study examines the impact of this noise on the utility of Swedish de-identified clinical data by using human evaluators and by training and testing BERT models. Our results indicate that de-identification does not harm the utility for clinical NLP and that human evaluators are less sensitive to noise from de-identification than expected.

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Evaluating Pretraining Strategies for Clinical BERT Models
Anastasios Lamproudis | Aron Henriksson | Hercules Dalianis
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Research suggests that using generic language models in specialized domains may be sub-optimal due to significant domain differences. As a result, various strategies for developing domain-specific language models have been proposed, including techniques for adapting an existing generic language model to the target domain, e.g. through various forms of vocabulary modifications and continued domain-adaptive pretraining with in-domain data. Here, an empirical investigation is carried out in which various strategies for adapting a generic language model to the clinical domain are compared to pretraining a pure clinical language model. Three clinical language models for Swedish, pretrained for up to ten epochs, are fine-tuned and evaluated on several downstream tasks in the clinical domain. A comparison of the language models’ downstream performance over the training epochs is conducted. The results show that the domain-specific language models outperform a general-domain language model; however, there is little difference in performance of the various clinical language models. However, compared to pretraining a pure clinical language model with only in-domain data, leveraging and adapting an existing general-domain language model requires fewer epochs of pretraining with in-domain data.

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Downstream Task Performance of BERT Models Pre-Trained Using Automatically De-Identified Clinical Data
Thomas Vakili | Anastasios Lamproudis | Aron Henriksson | Hercules Dalianis
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Automatic de-identification is a cost-effective and straightforward way of removing large amounts of personally identifiable information from large and sensitive corpora. However, these systems also introduce errors into datasets due to their imperfect precision. These corruptions of the data may negatively impact the utility of the de-identified dataset. This paper de-identifies a very large clinical corpus in Swedish either by removing entire sentences containing sensitive data or by replacing sensitive words with realistic surrogates. These two datasets are used to perform domain adaptation of a general Swedish BERT model. The impact of the de-identification techniques is assessed by training and evaluating the models using six clinical downstream tasks. The results are then compared to a similar BERT model domain-adapted using an unaltered version of the clinical corpus. The results show that using an automatically de-identified corpus for domain adaptation does not negatively impact downstream performance. We argue that automatic de-identification is an efficient way of reducing the privacy risks of domain-adapted models and that the models created in this paper should be safe to distribute to other academic researchers.

2021

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Creating and Evaluating a Synthetic Norwegian Clinical Corpus for De-Identification
Synnøve Bråthen | Wilhelm Wie | Hercules Dalianis
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

Building tools to remove sensitive information such as personal names, addresses, and telephone numbers - so called Protected Health Information (PHI) - from clinical free text is an important task to make clinical texts available for research. These de-identification tools must be assessed regarding their quality in the form of the measurements precision and re- call. To assess such tools, gold standards - annotated clinical text - must be available. Such gold standards exist for larger languages. For Norwegian, how- ever, there are no such resources. Therefore, an already existing Norwegian synthetic clinical corpus, NorSynthClinical, has been extended with PHIs and annotated by two annotators, obtaining an inter-annotator agreement of 0.94 F1-measure. In total, the corpus has 409 annotated PHI instances and is called NorSynthClinical PHI. A de-identification hybrid tool (machine learning and rule-based meth- ods) for Norwegian was developed and trained with open available resources, and obtained an overall F1-measure of 0.73 and a recall of 0.62, when tested using NorSynthClinical PHI. NorSynthClinical PHI is made open and available at Github to be used by the research community.

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Applying and Sharing pre-trained BERT-models for Named Entity Recognition and Classification in Swedish Electronic Patient Records
Mila Grancharova | Hercules Dalianis
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

To be able to share the valuable information in electronic patient records (EPR) they first need to be de-identified in order to protect the privacy of their subjects. Named entity recognition and classification (NERC) is an important part of this process. In recent years, general-purpose language models pre-trained on large amounts of data, in particular BERT, have achieved state of the art results in NERC, among other NLP tasks. So far, however, no attempts have been made at applying BERT for NERC on Swedish EPR data. This study attempts to fine-tune one Swedish BERT-model and one multilingual BERT-model for NERC on a Swedish EPR corpus. The aim is to assess the applicability of BERT-models for this task as well as to compare the two models in a domain-specific Swedish language task. With the Swedish model, recall of 0.9220 and precision of 0.9226 is achieved. This is an improvement to previous results on the same corpus since the high recall does not sacrifice precision. As the models also perform relatively well when fine-tuned with pseudonymised data, it is concluded that there is good potential in using this method in a shareable de-identification system for Swedish clinical text.

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HB Deid - HB De-identification tool demonstrator
Hanna Berg | Hercules Dalianis
Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)

This paper describes a freely available web-based demonstrator called HB Deid. HB Deid identifies so-called protected health information, PHI, in a text written in Swedish and removes, masks, or replaces them with surrogates or pseudonyms. PHIs are named entities such as personal names, locations, ages, phone numbers, dates. HB Deid uses a CRF model trained on non-sensitive annotated text in Swedish, as well as a rule-based post-processing step for finding PHI. The final step in obscuring the PHI is then to either mask it, show only the class name or use a rule-based pseudonymisation system to replace it.

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On the Contribution of Per-ICD Attention Mechanisms to Classify Health Records in Languages with Fewer Resources than English
Alberto Blanco | Sonja Remmer | Alicia Pérez | Hercules Dalianis | Arantza Casillas
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

We introduce a multi-label text classifier with per-label attention for the classification of Electronic Health Records according to the International Classification of Diseases. We apply the model on two Electronic Health Records datasets with Discharge Summaries in two languages with fewer resources than English, Spanish and Swedish. Our model leverages the BERT Multilingual model (specifically the Wikipedia, as the model have been trained with 104 languages, including Spanish and Swedish, with the largest Wikipedia dumps) to share the language modelling capabilities across the languages. With the per-label attention, the model can compute the relevance of each word from the EHR towards the prediction of each label. For the experimental framework, we apply 157 labels from Chapter XI – Diseases of the Digestive System of the ICD, which makes the attention especially important as the model has to discriminate between similar diseases. 1 https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages

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Developing a Clinical Language Model for Swedish: Continued Pretraining of Generic BERT with In-Domain Data
Anastasios Lamproudis | Aron Henriksson | Hercules Dalianis
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The use of pretrained language models, fine-tuned to perform a specific downstream task, has become widespread in NLP. Using a generic language model in specialized domains may, however, be sub-optimal due to differences in language use and vocabulary. In this paper, it is investigated whether an existing, generic language model for Swedish can be improved for the clinical domain through continued pretraining with clinical text. The generic and domain-specific language models are fine-tuned and evaluated on three representative clinical NLP tasks: (i) identifying protected health information, (ii) assigning ICD-10 diagnosis codes to discharge summaries, and (iii) sentence-level uncertainty prediction. The results show that continued pretraining on in-domain data leads to improved performance on all three downstream tasks, indicating that there is a potential added value of domain-specific language models for clinical NLP.

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Multi-label Diagnosis Classification of Swedish Discharge Summaries – ICD-10 Code Assignment Using KB-BERT
Sonja Remmer | Anastasios Lamproudis | Hercules Dalianis
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)

The International Classification of Diseases (ICD) is a system for systematically recording patients’ diagnoses. Clinicians or professional coders assign ICD codes to patients’ medical records to facilitate funding, research, and administration. In most health facilities, clinical coding is a manual, time-demanding task that is prone to errors. A tool that automatically assigns ICD codes to free-text clinical notes could save time and reduce erroneous coding. While many previous studies have focused on ICD coding, research on Swedish patient records is scarce. This study explored different approaches to pairing Swedish clinical notes with ICD codes. KB-BERT, a BERT model pre-trained on Swedish text, was compared to the traditional supervised learning models Support Vector Machines, Decision Trees, and K-nearest Neighbours used as the baseline. When considering ICD codes grouped into ten blocks, the KB-BERT was superior to the baseline models, obtaining an F1-micro of 0.80 and an F1-macro of 0.58. When considering the 263 full ICD codes, the KB-BERT was outperformed by all baseline models at an F1-micro and F1-macro of zero. Wilcoxon signed-rank tests showed that the performance differences between the KB-BERT and the baseline models were statistically significant.

2020

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The Impact of De-identification on Downstream Named Entity Recognition in Clinical Text
Hanna Berg | Aron Henriksson | Hercules Dalianis
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

The impact of de-identification on data quality and, in particular, utility for developing models for downstream tasks has been more thoroughly studied for structured data than for unstructured text. While previous studies indicate that text de-identification has a limited impact on models for downstream tasks, it remains unclear what the impact is with various levels and forms of de-identification, in particular concerning the trade-off between precision and recall. In this paper, the impact of de-identification is studied on downstream named entity recognition in Swedish clinical text. The results indicate that de-identification models with moderate to high precision lead to similar downstream performance, while low precision has a substantial negative impact. Furthermore, different strategies for concealing sensitive information affect performance to different degrees, ranging from pseudonymisation having a low impact to the removal of entire sentences with sensitive information having a high impact. This study indicates that it is possible to increase the recall of models for identifying sensitive information without negatively affecting the use of de-identified text data for training models for clinical named entity recognition; however, there is ultimately a trade-off between the level of de-identification and the subsequent utility of the data.

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A Semi-supervised Approach for De-identification of Swedish Clinical Text
Hanna Berg | Hercules Dalianis
Proceedings of the Twelfth Language Resources and Evaluation Conference

An abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done to protect the integrity of patients while making electronic health records usable for research by removing personally identifiable information in patient records. Supervised machine learning approaches for de-identification of EHRs need annotated data for training, annotations that are costly in time and human resources. The annotation costs for clinical text is even more costly as the process must be carried out in a protected environment with a limited number of annotators who must have signed confidentiality agreements. In this paper is therefore, a semi-supervised method proposed, for automatically creating high-quality training data. The study shows that the method can be used to improve recall from 84.75% to 89.20% without sacrificing precision to the same extent, dropping from 95.73% to 94.20%. The model’s recall is arguably more important for de-identification than precision.

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Detecting Adverse Drug Events from Swedish Electronic Health Records using Text Mining
Maria Bampa | Hercules Dalianis
Proceedings of the LREC 2020 Workshop on Multilingual Biomedical Text Processing (MultilingualBIO 2020)

Electronic Health Records are a valuable source of patient information which can be leveraged to detect Adverse Drug Events (ADEs) and aid post-mark drug-surveillance. The overall aim of this study is to scrutinize text written by clinicians in the EHRs and build a model for ADE detection that produces medically relevant predictions. Natural Language Processing techniques will be exploited to create important predictors and incorporate them into the learning process. The study focuses on the 5 most frequent ADE cases found ina Swedish electronic patient record corpus. The results indicate that considering textual features, rather than the structured, can improve the classification performance by 15% in some ADE cases. Additionally, variable patient history lengths are incorporated in the models, demonstrating the importance of the above decision rather than using an arbitrary number for a history length. The experimental findings suggest that the clinical text in EHRs includes information that can capture data beyond the ones that are found in a structured format.

2019

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Building a De-identification System for Real Swedish Clinical Text Using Pseudonymised Clinical Text
Hanna Berg | Taridzo Chomutare | Hercules Dalianis
Proceedings of the Tenth International Workshop on Health Text Mining and Information Analysis (LOUHI 2019)

This article presents experiments with pseudonymised Swedish clinical text used as training data to de-identify real clinical text with the future aim to transfer non-sensitive training data to other hospitals. Conditional Random Fields (CFR) and Long Short-Term Memory (LSTM) machine learning algorithms were used to train de-identification models. The two models were trained on pseudonymised data and evaluated on real data. For benchmarking, models were also trained on real data, and evaluated on real data as well as trained on pseudonymised data and evaluated on pseudonymised data. CRF showed better performance for some PHI information like Date Part, First Name and Last Name; consistent with some reports in the literature. In contrast, poor performances on Location and Health Care Unit information were noted, partially due to the constrained vocabulary in the pseudonymised training data. It is concluded that it is possible to train transferable models based on pseudonymised Swedish clinical data, but even small narrative and distributional variation could negatively impact performance.

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Augmenting a De-identification System for Swedish Clinical Text Using Open Resources and Deep Learning
Hanna Berg | Hercules Dalianis
Proceedings of the Workshop on NLP and Pseudonymisation

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Pseudonymisation of Swedish Electronic Patient Records Using a Rule-Based Approach
Hercules Dalianis
Proceedings of the Workshop on NLP and Pseudonymisation

2017

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Efficient Encoding of Pathology Reports Using Natural Language Processing
Rebecka Weegar | Jan F Nygård | Hercules Dalianis
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017

In this article we present a system that extracts information from pathology reports. The reports are written in Norwegian and contain free text describing prostate biopsies. Currently, these reports are manually coded for research and statistical purposes by trained experts at the Cancer Registry of Norway where the coders extract values for a set of predefined fields that are specific for prostate cancer. The presented system is rule based and achieves an average F-score of 0.91 for the fields Gleason grade, Gleason score, the number of biopsies that contain tumor tissue, and the orientation of the biopsies. The system also identifies reports that contain ambiguity or other content that should be reviewed by an expert. The system shows potential to encode the reports considerably faster, with less resources, and similar high quality to the manual encoding.

2016

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Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections
Olof Jacobson | Hercules Dalianis
Proceedings of the 15th Workshop on Biomedical Natural Language Processing

2015

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Creating a rule based system for text mining of Norwegian breast cancer pathology reports
Rebecka Weegar | Hercules Dalianis
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

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Adverse Drug Event classification of health records using dictionary based pre-processing and machine learning
Stefanie Friedrich | Hercules Dalianis
Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis

2014

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Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)
Sumithra Velupillai | Martin Duneld | Maria Kvist | Hercules Dalianis | Maria Skeppstedt | Aron Henriksson
Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi)

2013

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Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg
Hideyuki Tanushi | Hercules Dalianis | Martin Duneld | Maria Kvist | Maria Skeppstedt | Sumithra Velupillai
Proceedings of the 19th Nordic Conference of Computational Linguistics (NODALIDA 2013)

2012

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Rule-based Entity Recognition and Coverage of SNOMED CT in Swedish Clinical Text
Maria Skeppstedt | Maria Kvist | Hercules Dalianis
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Named entity recognition of the clinical entities disorders, findings and body structures is needed for information extraction from unstructured text in health records. Clinical notes from a Swedish emergency unit were annotated and used for evaluating a rule- and terminology-based entity recognition system. This system used different preprocessing techniques for matching terms to SNOMED CT, and, one by one, four other terminologies were added. For the class body structure, the results improved with preprocessing, whereas only small improvements were shown for the classes disorder and finding. The best average results were achieved when all terminologies were used together. The entity body structure was recognised with a precision of 0.74 and a recall of 0.80, whereas lower results were achieved for disorder (precision: 0.75, recall: 0.55) and for finding (precision: 0.57, recall: 0.30). The proportion of entities containing abbreviations were higher for false negatives than for correctly recognised entities, and no entities containing more than two tokens were recognised by the system. Low recall for disorders and findings shows both that additional methods are needed for entity recognition and that there are many expressions in clinical text that are not included in SNOMED CT.

2010

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Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents
Hercules Dalianis | Martin Hassel | Gunnar Nilsson
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents

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Characteristics and Analysis of Finnish and Swedish Clinical Intensive Care Nursing Narratives
Helen Allvin | Elin Carlsson | Hercules Dalianis | Riitta Danielsson-Ojala | Vidas Daudaravicius | Martin Hassel | Dimitrios Kokkinakis | Heljä Lundgren-Laine | Gunnar Nilsson | Øystein Nytrø | Sanna Salanterä | Maria Skeppstedt | Hanna Suominen | Sumithra Velupillai
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents

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Uncertainty Detection as Approximate Max-Margin Sequence Labelling
Oscar Täckström | Sumithra Velupillai | Martin Hassel | Gunnar Eriksson | Hercules Dalianis | Jussi Karlgren
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

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Creating and evaluating a consensus for negated and speculative words in a Swedish clinical corpus
Hercules Dalianis | Maria Skeppstedt
Proceedings of the Workshop on Negation and Speculation in Natural Language Processing

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Creating a Reusable English-Chinese Parallel Corpus for Bilingual Dictionary Construction
Hercules Dalianis | Hao-chun Xing | Xin Zhang
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

This paper first describes an experiment to construct an English-Chinese parallel corpus, then applying the Uplug word alignment tool on the corpus and finally produce and evaluate an English-Chinese word list. The Stockholm English-Chinese Parallel Corpus (SEC) was created by downloading English-Chinese parallel corpora from a Chinese web site containing law texts that have been manually translated from Chinese to English. The parallel corpus contains 104 563 Chinese characters equivalent to 59 918 Chinese words, and the corresponding English corpus contains 75 766 English words. However Chinese writing does not utilize any delimiters to mark word boundaries so we had to carry out word segmentation as a preprocessing step on the Chinese corpus. Moreover since the parallel corpus is downloaded from Internet the corpus is noisy regarding to alignment between corresponding translated sentences. Therefore we used 60 hours of manually work to align the sentences in the English and Chinese parallel corpus before performing automatic word alignment using Uplug. The word alignment with Uplug was carried out from English to Chinese. Nine respondents evaluated the resulting English-Chinese word list with frequency equal to or above three and we obtained an accuracy of 73.1 percent.

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Influence of Module Order on Rule-Based De-identification of Personal Names in Electronic Patient Records Written in Swedish
Elin Carlsson | Hercules Dalianis
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Electronic patient records (EPRs) are a valuable resource for research but for confidentiality reasons they cannot be used freely. In order to make EPRs available to a wider group of researchers, sensitive information such as personal names has to be removed. De-identification is a process that makes this possible. Both rule-based as well as statistical and machine learning based methods exist to perform de-identification, but the second method requires annotated training material which exists only very sparsely for patient names. It is therefore necessary to use rule-based methods for de-identification of EPRs. Not much is known, however, about the order in which the various rules should be applied and how the different rules influence precision and recall. This paper aims to answer this research question by implementing and evaluating four common rules for de-identification of personal names in EPRs written in Swedish: (1) dictionary name matching, (2) title matching, (3) common words filtering and (4) learning from previous modules. The results show that to obtain the highest recall and precision, the rules should be applied in the following order: title matching, common words filtering and dictionary name matching.

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How Certain are Clinical Assessments? Annotating Swedish Clinical Text for (Un)certainties, Speculations and Negations
Hercules Dalianis | Sumithra Velupillai
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Clinical texts contain a large amount of information. Some of this information is embedded in contexts where e.g. a patient status is reasoned about, which may lead to a considerable amount of statements that indicate uncertainty and speculation. We believe that distinguishing such instances from factual statements will be very beneficial for automatic information extraction. We have annotated a subset of the Stockholm Electronic Patient Record Corpus for certain and uncertain expressions as well as speculative and negation keywords, with the purpose of creating a resource for the development of automatic detection of speculative language in Swedish clinical text. We have analyzed the results from the initial annotation trial by means of pairwise Inter-Annotator Agreement (IAA) measured with F-score. Our main findings are that IAA results for certain expressions and negations are very high, but for uncertain expressions and speculative keywords results are less encouraging. These instances need to be defined in more detail. With this annotation trial, we have created an important resource that can be used to further analyze the properties of speculative language in Swedish clinical text. Our intention is to release this subset to other research groups in the future after removing identifiable information.

2009

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Identification of Parallel Text Pairs Using Fingerprints
Martin Hassel | Hercules Dalianis
Proceedings of the International Conference RANLP-2009

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Using Uplug and SiteSeeker to construct a cross language search engine for Scandinavian languages
Hercules Dalianis | Martin Rimka | Viggo Kann
Proceedings of the 17th Nordic Conference of Computational Linguistics (NODALIDA 2009)

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Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike
Bart Jongejan | Hercules Dalianis
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

2008

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Automatic Construction of Domain-specific Dictionaries on Sparse Parallel Corpora in the Nordic languages
Sumithra Velupillai | Hercules Dalianis
Coling 2008: Proceedings of the workshop Multi-source Multilingual Information Extraction and Summarization

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Experiments to Investigate the Connection between Case Distribution and Topical Relevance of Search Terms in an Information Retrieval Setting
Jussi Karlgren | Hercules Dalianis | Bart Jongejan
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

We have performed a set of experiments made to investigate the utility of morphological analysis to improve retrieval of documents written in languages with relatively large morphological variation in a practical commercial setting, using the SiteSeeker search system developed and marketed by Euroling Ab. The objective of the experiments was to evaluate different lemmatisers and stemmers to determine which would be the most practical for the task at hand: highly interactive, relatively high precision web searches in commercial customer-oriented document collections. This paper gives an overview of some of the results for Finnish and German, and describes specifically one experiment designed to investigate the case distribution of nouns in a highly inflectional language (Finnish) and the topicality of the nouns in target texts. We find that topical nouns taken from queries are distributed differently over relevant and non-relevant documents depending on their grammatical case.

2006

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Hand-crafted versus Machine-learned Inflectional Rules: The Euroling-SiteSeeker Stemmer and CST’s Lemmatiser
Hercules Dalianis | Bart Jongejan
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

The Euroling stemmer is developed for a commercial web site and intranet search engine called SiteSeeker. SiteSeeker is basically used in the Swedish domain but to some extent also for the English domain. CST's lemmatiser comes from the Center for Language Technology, University of Copenhagen and was originally developed as a research prototype to create lemmatisation rules from training data. In this paper we compare the performance of the stemmer that uses handcrafted rules for Swedish, Danish and Norwegian as well one stemmer for Greek with CST's lemmatiser that uses training data to extract lemmatisation rules for Swedish, Danish, Norwegian and Greek. The performances of the two approaches are about the same with around 10 percent errors. The handcrafted rule based stemmer techniques are easy to get started with if the programmer has the proper linguistic knowledge. The machine trained sets of lemmatisation rules are very easy to produce without having linguistic knowledge given that one has correct training data.

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Improving search engine retrieval using a compound splitter for Swedish
Hercules Dalianis
Proceedings of the 15th Nordic Conference of Computational Linguistics (NODALIDA 2005)

2001

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Improving Precision in Information Retrieval for Swedish using Stemming
Johan Carlberger | Hercules Dalianis | Martin Duneld | Ola Knutsson
Proceedings of the 13th Nordic Conference of Computational Linguistics (NODALIDA 2001)

1996

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On Lexical Aggregation and Ordering
Hercules Dalianis | Eduard Hovy
Eighth International Natural Language Generation Workshop (Posters and Demonstrations)

1995

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Aggregation in the NL-generator of the Visual and Natural language Specification Tool
Hercules Dalianis
Seventh Conference of the European Chapter of the Association for Computational Linguistics