Measuring praise and criticism: Inference of semantic orientation from association

The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This paper introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words

Yleiskäyttöinen tekstinluokittelija suomenkielisille potilaskertomusteksteille

Medical texts are an underused source of data in clinical analytics. Extracting the relevant information from unstructured texts is difficult and while there are some tools available, they are often targeted for English texts. The situation is worse for smaller languages, such as Finnish. In this work, we reviewed literature in text mining and natural language processing fields in the scope of analyzing medical texts. Using the results of our literature review, we created an algorithm for information extraction from patient record texts. During this thesis work we created a decent text mining tool that works through text classification. This algorithm can be used detect medical conditions solely from medical texts. The usage of the algorithm is limited through the availability of large training data.Potilaskertomustekstejä käytetään kliinisessä analytiikassa huomattavan vähäisessä määrin. Olennaisen tiedon poimiminen tekstin joukosta on vaikeaa, ja vaikka siihen on työkaluja saatavilla, ovat ne useimmiten tehty englanninkielisille teksteille. Pienempien kielten, kuten suomen kohdalla tilanne on heikompi. Tässä työssä tehtiin kirjallisuuskatsaus tekstinlouhintaan ja luonnollisen kielen käsittelyyn liittyvään kirjallisuuteen, keskittyen erityisesti menetelmiin jotka soveltuvat lääketieteellisten tekstien analysointiin. Kirjallisuuskatsauksen pohjalta loimme algoritmin, joka soveltuu yleisesti lääketieteellisten tekstien luokitteluun. Tämän diplomityön osana luotiin tekstinlouhintatyökalu suomenkielisille lääketieteellisille teksteille. Kehitettyä algoritmia voidaan käyttää erilaisten tilojen tunnistamiseen potilaskertomusteksteistä. Algoritmin käyttöä kuitenkin rajoittaa tarve suurehkolle määrälle opetusdataa

Simple tricks for improving pattern-based information extraction from the biomedical literature

<p>Abstract</p> <p>Background</p> <p>Pattern-based approaches to relation extraction have shown very good results in many areas of biomedical text mining. However, defining the right set of patterns is difficult; approaches are either manual, incurring high cost, or automatic, often resulting in large sets of noisy patterns.</p> <p>Results</p> <p>We propose several techniques for filtering sets of automatically generated patterns and analyze their effectiveness for different extraction tasks, as defined in the recent BioNLP 2009 shared task. We focus on simple methods that only take into account the complexity of the pattern and the complexity of the texts the patterns are applied to. We show that our techniques, despite their simplicity, yield large improvements in all tasks we analyzed. For instance, they raise the F-score for the task of extraction gene expression events from 24.8% to 51.9%.</p> <p>Conclusions</p> <p>Already very simple filtering techniques may improve the F-score of an information extraction method based on automatically generated patterns significantly. Furthermore, the application of such methods yields a considerable speed-up, as fewer matches need to be analysed. Due to their simplicity, the proposed filtering techniques also should be applicable to other methods using linguistic patterns for information extraction.</p

IEST: WASSA-2018 Implicit Emotions Shared Task

Past shared tasks on emotions use data with both overt expressions of emotions (I am so happy to see you!) as well as subtle expressions where the emotions have to be inferred, for instance from event descriptions. Further, most datasets do not focus on the cause or the stimulus of the emotion. Here, for the first time, we propose a shared task where systems have to predict the emotions in a large automatically labeled dataset of tweets without access to words denoting emotions. Based on this intention, we call this the Implicit Emotion Shared Task (IEST) because the systems have to infer the emotion mostly from the context. Every tweet has an occurrence of an explicit emotion word that is masked. The tweets are collected in a manner such that they are likely to include a description of the cause of the emotion - the stimulus. Altogether, 30 teams submitted results which range from macro F1 scores of 21 % to 71 %. The baseline (MaxEnt bag of words and bigrams) obtains an F1 score of 60 % which was available to the participants during the development phase. A study with human annotators suggests that automatic methods outperform human predictions, possibly by honing into subtle textual clues not used by humans. Corpora, resources, and results are available at the shared task website at http://implicitemotions.wassa2018.com.Comment: Accepted at Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysi