Group Life Insurance: Its Legal Aspects

Background: Female representation on boards is an ongoing debate. The European Parliament voted in November 2013 in favour of a proposal that it should be at least 40 percent women on company boards by 2020. Jens Spendrup, president of the Confederation of Swedish Enterprise, was interviewed on Swedish Radio in February 2014 and stated that there are not enough qualified women to recruit to the company boards. The listed companies in Sweden only have 22 percent of women in their company boards and given Jens Spendrup’s statement should they have difficulty reaching up to 40 percent by 2020. Nevertheless, the state-owned companies has shown that it is possible with a female representation of 50 percent. The question then is what the private companies are doing wrong? Aim: The study aims to investigate the recruitment process within listed companies and state-owned companies in Sweden to see if it affects the representation of women on corporate boards. This study intends to explain why female representation is so low in the private sector relative to the state sector. Methodology: The study is qualitative in nature where empirical data is primarily collected through interviews with representatives that have insight in the recruitment process in each sector. Theory and empirical data were alternately collected which implies an iterative approach. Conclusion: We have distinguished organizational differences in the recruitment process, which is crucial for female representation. Time and resources have been identified as key parameters and age as well as experience affects the selection of candidates. We also discovered that normative regulations do not work in the private sector and therefor there is a need for a mandatory regulation.Bakgrund: Den kvinnliga representationen i bolagsstyrelser är en aktuell debatt. Europaparlamentet röstade i november 2013 ja till ett förslag att det ska vara minst 40 procent kvinnor i bolagsstyrelser senast år 2020. Jens Spendrup, ordförande i Svenskt Näringsliv, uttalade sig i Sveriges Radio i februari 2014 om att det inte finns tillräckligt med kompetenta kvinnor att rekrytera till bolagsstyrelser, vilket blev väldigt uppmärksammat i media. Som synes pågår debatten både nationellt och internationellt. Börsbolagen i Sverige har 22 procent kvinnor i sina bolagsstyrelser och med tanke på Jens Spendrups uttalande borde de ha svårt att nå 40 procent till år 2020. Dock har de statligt helägda bolagen visat att det är möjligt och har en kvinnlig representation på 50 procent. Frågan är då vad de privata bolagen gör för fel? Syfte: Studiens syfte är att undersöka rekryteringsprocessen inom börsbolag och statligt helägda bolag i Sverige för att se om den påverkar den kvinnliga representationen i bolagsstyrelserna. Denna studie ämnar att förklara varför kvinnlig representation är så låg inom den privata sektorn i förhållande till den statliga sektorn. Metod: Studien är av kvalitativ karaktär där empirin främst är insamlad genom intervjuer med representanter som har insyn i rekryteringsprocessen inom respektive sektor. Teori och empiri insamlades växelvis vilket innebär en iterativ ansats. Slutsats: Vi har i studien urskilt organisatoriska skillnader i rekryteringsprocessen vilket är avgörande för den kvinnliga representationen. Tid och resurser har identifierats som viktiga parametrar samt att ålder och erfarenhet spelar en viktig roll vid urvalet av kandidater. Vi konstaterar även att normativa regelverk inte fungerar på privat sektor och därmed finns behov av tvingande regelverk

Negation detection in Swedish clinical text: An adaption of NegEx to Swedish

<p>Abstract</p> <p>Background</p> <p>Most methods for negation detection in clinical text have been developed for English text, and there is a need for evaluating the feasibility of adapting these methods to other languages. A Swedish adaption of the English rule-based negation detection system NegEx, which detects negations through the use of trigger phrases, was therefore evaluated.</p> <p>Results</p> <p>The Swedish adaption of NegEx showed a precision of 75.2% and a recall of 81.9%, when evaluated on 558 manually classified sentences containing negation triggers, and a negative predictive value of 96.5% when evaluated on 342 sentences not containing negation triggers.</p> <p>Conclusions</p> <p>The precision was significantly lower for the Swedish adaptation than published results for the English version, but since many negated propositions were identified through a limited set of trigger phrases, it could nevertheless be concluded that the same trigger phrase approach is possible in a Swedish context, even though it needs to be further developed.</p> <p>Availability</p> <p>The triggers used for the evaluation of the Swedish adaption of NegEx are available at <url>http://people.dsv.su.se/~mariask/resources/triggers.txt</url> and can be used together with the original NegEx program for negation detection in Swedish clinical text.</p

The Impact of Part-of-Speech Filtering on Generation of a Swedish-Japanese Dictionary Using English as Pivot Language

Proceedings of the 18th Nordic Conference of Computational Linguistics NODALIDA 2011. Editors: Bolette Sandford Pedersen, Gunta Nešpore and Inguna Skadiņa. NEALT Proceedings Series, Vol. 11 (2011), 98-105. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio.no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle.net/10062/16955

Detection of stance and sentiment modifiers in political blogs

The automatic detection of seven types of modifiers was studied: Certainty, Uncertainty, Hypotheticality, Prediction, Recommendation, Concession/Contrast and Source. A classifier aimed at detecting local cue words that signal the categories was the most successful method for five of the categories. For Prediction and Hypotheticality, however, better results were obtained with a classifier trained on tokens and bigrams present in the entire sentence. Unsupervised cluster features were shown useful for the categories Source and Uncertainty, when a subset of the training data available was used. However, when all of the 2,095 sentences that had been actively selected and manually annotated were used as training data, the cluster features had a very limited effect. Some of the classification errors made by the models would be possible to avoid by extending the training data set, while other features and feature representations, as well as the incorporation of pragmatic knowledge, would be required for other error types

Negation Scope Delimitation in Clinical Text Using Three Approaches: NegEx, PyConTextNLP and SynNeg

ABSTRACT Negation detection is a key component in clinical information extraction systems, as health record text contains reasonings in which the physician excludes different diagnoses by negating them. Many systems for negation detection rely on negation cues (e.g. not), but only few studies have investigated if the syntactic structure of the sentences can be used for determining the scope of these cues. We have in this paper compared three different systems for negation detection in Swedish clinical text (NegEx, PyConTextNLP and SynNeg), which have different approaches for determining the scope of negation cues. NegEx uses the distance between the cue and the disease, PyConTextNLP relies on a list of conjunctions limiting the scope of a cue, and in SynNeg the boundaries of the sentence units, provided by a syntactic parser, limit the scope of the cues. The three systems produced similar results, detecting negation with an F-score of around 80%, but using a parser had advantages when handling longer, complex sentences or short sentences with contradictory statements

Annotating speaker stance in discourse:the Brexit Blog Corpus

The aim of this study is to explore the possibility of identifying speaker stance in discourse, provide an analytical resource for it and an evaluation of the level of agreement across speakers. We also explore to what extent language users agree about what kind of stances are expressed in natural language use or whether their interpretations diverge. In order to perform this task, a comprehensive cognitive-functional framework of ten stance categories was developed based on previous work on speaker stance in the literature. A corpus of opinionated texts was compiled, the Brexit Blog Corpus (BBC). An analytical protocol and interface (Active Learning and Visual Analytics) for the annotations was set up and the data were independently annotated by two annotators. The annotation procedure, the annotation agreements and the co-occurrence of more than one stance in the utterances are described and discussed. The careful, analytical annotation process has returned satisfactory inter- and intra-annotation agreement scores, resulting in a gold standard corpus, the final version of the BBC

Extracting Clinical Findings from Swedish Health Record Text

Information contained in the free text of health records is useful for the immediate care of patients as well as for medical knowledge creation. Advances in clinical language processing have made it possible to automatically extract this information, but most research has, until recently, been conducted on clinical text written in English. In this thesis, however, information extraction from Swedish clinical corpora is explored, particularly focusing on the extraction of clinical findings. Unlike most previous studies, Clinical Finding was divided into the two more granular sub-categories Finding (symptom/result of a medical examination) and Disorder (condition with an underlying pathological process). For detecting clinical findings mentioned in Swedish health record text, a machine learning model, trained on a corpus of manually annotated text, achieved results in line with the obtained inter-annotator agreement figures. The machine learning approach clearly outperformed an approach based on vocabulary mapping, showing that Swedish medical vocabularies are not extensive enough for the purpose of high-quality information extraction from clinical text. A rule and cue vocabulary-based approach was, however, successful for negation and uncertainty classification of detected clinical findings. Methods for facilitating expansion of medical vocabulary resources are particularly important for Swedish and other languages with less extensive vocabulary resources. The possibility of using distributional semantics, in the form of Random indexing, for semi-automatic vocabulary expansion of medical vocabularies was, therefore, evaluated. Distributional semantics does not require that terms or abbreviations are explicitly defined in the text, and it is, thereby, a method suitable for clinical corpora. Random indexing was shown useful for extending vocabularies with medical terms, as well as for extracting medical synonyms and abbreviation dictionaries

Using SNOMED CT for High Precision Entity Recognition in Swedish Clinical text

An evaluation was performed of retrieval of findings in Swedish clinical text through exact string matching against SNOMED CT. The aim was to create a system for retrieving clinical findings with high precision that for example can be used as training data for machine learning. The evaluation was performed on previously manually annotated findings, and the best approach showed a precision of 93 percent

Worlds Apart – The Gentlemen’s Club versus The Role Model : A study of the recruitment process to company boards in listed companies and state-owned companies

Background: Female representation on boards is an ongoing debate. The European Parliament voted in November 2013 in favour of a proposal that it should be at least 40 percent women on company boards by 2020. Jens Spendrup, president of the Confederation of Swedish Enterprise, was interviewed on Swedish Radio in February 2014 and stated that there are not enough qualified women to recruit to the company boards. The listed companies in Sweden only have 22 percent of women in their company boards and given Jens Spendrup’s statement should they have difficulty reaching up to 40 percent by 2020. Nevertheless, the state-owned companies has shown that it is possible with a female representation of 50 percent. The question then is what the private companies are doing wrong? Aim: The study aims to investigate the recruitment process within listed companies and state-owned companies in Sweden to see if it affects the representation of women on corporate boards. This study intends to explain why female representation is so low in the private sector relative to the state sector. Methodology: The study is qualitative in nature where empirical data is primarily collected through interviews with representatives that have insight in the recruitment process in each sector. Theory and empirical data were alternately collected which implies an iterative approach. Conclusion: We have distinguished organizational differences in the recruitment process, which is crucial for female representation. Time and resources have been identified as key parameters and age as well as experience affects the selection of candidates. We also discovered that normative regulations do not work in the private sector and therefor there is a need for a mandatory regulation.Bakgrund: Den kvinnliga representationen i bolagsstyrelser är en aktuell debatt. Europaparlamentet röstade i november 2013 ja till ett förslag att det ska vara minst 40 procent kvinnor i bolagsstyrelser senast år 2020. Jens Spendrup, ordförande i Svenskt Näringsliv, uttalade sig i Sveriges Radio i februari 2014 om att det inte finns tillräckligt med kompetenta kvinnor att rekrytera till bolagsstyrelser, vilket blev väldigt uppmärksammat i media. Som synes pågår debatten både nationellt och internationellt. Börsbolagen i Sverige har 22 procent kvinnor i sina bolagsstyrelser och med tanke på Jens Spendrups uttalande borde de ha svårt att nå 40 procent till år 2020. Dock har de statligt helägda bolagen visat att det är möjligt och har en kvinnlig representation på 50 procent. Frågan är då vad de privata bolagen gör för fel? Syfte: Studiens syfte är att undersöka rekryteringsprocessen inom börsbolag och statligt helägda bolag i Sverige för att se om den påverkar den kvinnliga representationen i bolagsstyrelserna. Denna studie ämnar att förklara varför kvinnlig representation är så låg inom den privata sektorn i förhållande till den statliga sektorn. Metod: Studien är av kvalitativ karaktär där empirin främst är insamlad genom intervjuer med representanter som har insyn i rekryteringsprocessen inom respektive sektor. Teori och empiri insamlades växelvis vilket innebär en iterativ ansats. Slutsats: Vi har i studien urskilt organisatoriska skillnader i rekryteringsprocessen vilket är avgörande för den kvinnliga representationen. Tid och resurser har identifierats som viktiga parametrar samt att ålder och erfarenhet spelar en viktig roll vid urvalet av kandidater. Vi konstaterar även att normativa regelverk inte fungerar på privat sektor och därmed finns behov av tvingande regelverk

Creating and Evaluating a Consensus for Negated and Speculative Words in a Swedish Clinical Corpus

In this paper we describe the creation of a consensus corpus that was obtained through combining three individual annotations of the same clinical corpus in Swedish. We used a few basic rules that were executed automatically to create the consensus. The corpus contains negation words, speculative words, uncertain expressions and certain expressions. We evaluated the consensus using it for negation and speculation cue detection. We used Stanford NER, which is based on the machine learning algorithm Conditional Random Fields for the training and detection. For comparison we also used the clinical part of the BioScope Corpus and trained it with Stanford NER. For our clinical consensus corpus in Swedish we obtained a precision of 87.9 percent and a recall of 91.7 percent for negation cues, and for English with the Bioscope Corpus we obtained a precision of 97.6 percent and a recall of 96.7 percent for negation cues