Unigram Features Research Articles

Discovering Fine-grained Sentiment in Suicide Notes

This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.

Community QA Question Classification: Is the Asker Looking for Subjective Answers or Not?

Community question answering (CQA) sites such as Yahoo! Chiebukuro are known to be very useful resources for automatic question answering (QA) systems. However, CQA users often post questions expecting not some general truths but rather opinions of different people. We believe that a QA system should act according to these different question types. We therefore define two question types based on whether the questioner expects subjective or objective answers, and report on an automatic question classification experiment. We achieve over 80% weighted accuracy using uni-gram and bi-gram features learned by Naive Bayes with smoothing. We also discuss the inter-annotator agreement and its impact on automatic classification accuracy, as well as what kind of questions tend to be misclassified.

Expediting medical literature coding with query-building

Manual sorting of published journal articles into several pre-defined subsets for the purpose of qualitative analysis is common practice in social science research. Unfortunately, this can be a time-consuming process which requires the attention of a subject specialist, and relies on various measures of inter-rater reliability to ensure that the results are valid and reproducible to serve as a basis for further study. We describe a system we have implemented, steelir, to help determine features common to one set of PubMed® articles in order to distinguish them from another. The system provides users with wordlevel unigram and bigram features from the article title and abstract, as well as MeSH® indexing terms, and suggests robust sample queries to find similar articles. We apply the system to the task of distinguishing original research articles on functional magnetic resonance imaging (fMRI) of sensorimotor function from fMRI studies of higher cognitive functions.

Formulating MEDLINE queries for article retrieval based on PubMed exemplars

AbstractBibliographic search engines allow endless possibilities for building queries based on specific words or phrases in article titles and abstracts, indexing terms, and other attributes. Unfortunately, deciding which attributes to use in a methodologically sound query is a non-trivial process. In this paper, we describe a system to help with this task, given an example set of PubMed articles to retrieve and a corresponding set of articles to exclude. The system provides the users with unigram and bigram features from the title, abstract, MeSH terms, and MeSH qualifier terms in decreasing order of precision, given a recall threshold. From this information and their knowledge of the domain, users can formulate a query and evaluate its performance. We apply the system to the task of distinguishing original research articles of functional magnetic resonance imaging (fMRI) of sensorimotor function from fMRI studies of higher cognitive functions.

Formulating MEDLINE queries for article retrieval based on PubMed exemplars

AbstractBibliographic search engines allow endless possibilities for building queries based on specific words or phrases in article titles and abstracts, indexing terms, and other attributes. Unfortunately, deciding which attributes to use in a methodologically sound query is a non-trivial process. In this paper, we describe a system to help with this task, given an example set of PubMed articles to retrieve and a corresponding set of articles to exclude. The system provides the users with unigram and bigram features from the title, abstract, MeSH terms, and MeSH qualifier terms in decreasing order of precision, given a recall threshold. From this information and their knowledge of the domain, users can formulate a query and evaluate its performance. We apply the system to the task of distinguishing original research articles of functional magnetic resonance imaging (fMRI) of sensorimotor function from fMRI studies of higher cognitive functions.

Using open access literature to guide full-text query formulation

Abstract Background Much scientific knowledge is contained in the details of the full-text biomedical literature. Most research in automated retrieval presupposes that the target literature can be downloaded and preprocessed prior to query. Unfortunately, this is not a practical or maintainable option for most users due to licensing restrictions, website terms of use, and sheer volume. Scientific article full-text is increasingly queriable through portals such as PubMed Central, Highwire Press, Scirus, and Google Scholar. However, because these portals only support very basic Boolean queries and full text is so expressive, formulating an effective query is a difficult task for users. We propose improving the formulation of full-text queries by using the open access literature as a proxy for the literature to be searched. We evaluated the feasibility of this approach by building a high-precision query for identifying studies that perform gene expression microarray experiments.*Methodology and Results*We built decision rules from unigram and bigram features of the open access literature. Minor syntax modifications were needed to translate the decision rules into the query languages of PubMed Central, Highwire Press, and Google Scholar. We mapped all retrieval results to PubMed identifiers and considered our query results as the union of retrieved articles across all portals. Compared to our reference standard, the derived full-text query found 56% (95% confidence interval, 52% to 61%) of intended studies, and 90% (86% to 93%) of studies identified by the full-text search met the reference standard criteria. Due to this relatively high precision, the derived query was better suited to the intended application than alternative baseline MeSH queries.*Significance*Using open access literature to develop queries for full-text portals is an open, flexible, and effective method for retrieval of biomedical literature articles based on article full-text. We hope our approach will raise awareness of the constraints and opportunities in mainstream full-text information retrieval and provide a useful tool for today’s researchers.

Using open access literature to guide full-text query formulation

Abstract Background Much scientific knowledge is contained in the details of the full-text biomedical literature. Most research in automated retrieval presupposes that the target literature can be downloaded and preprocessed prior to query. Unfortunately, this is not a practical or maintainable option for most users due to licensing restrictions, website terms of use, and sheer volume. Scientific article full-text is increasingly queriable through portals such as PubMed Central, Highwire Press, Scirus, and Google Scholar. However, because these portals only support very basic Boolean queries and full text is so expressive, formulating an effective query is a difficult task for users. We propose improving the formulation of full-text queries by using the open access literature as a proxy for the literature to be searched. We evaluated the feasibility of this approach by building a high-precision query for identifying studies that perform gene expression microarray experiments. *Methodology and Results* We built decision rules from unigram and bigram features of the open access literature. Minor syntax modifications were needed to translate the decision rules into the query languages of PubMed Central, Highwire Press, and Google Scholar. We mapped all retrieval results to PubMed identifiers and considered our query results as the union of retrieved articles across all portals. Compared to our reference standard, the derived full-text query found 56% (95% confidence interval, 52% to 61%) of intended studies, and 90% (86% to 93%) of studies identified by the full-text search met the reference standard criteria. Due to this relatively high precision, the derived query was better suited to the intended application than alternative baseline MeSH queries. *Significance* Using open access literature to develop queries for full-text portals is an open, flexible, and effective method for retrieval of biomedical literature articles based on article full-text. We hope our approach will raise awareness of the constraints and opportunities in mainstream full-text information retrieval and provide a useful tool for today’s researchers.

A machine learning approach to sentiment analysis in multilingual Web texts

Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.

SENTIMENT CLASSIFICATION of MOVIE REVIEWS USING CONTEXTUAL VALENCE SHIFTERS

We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect of valence shifters on classifying the reviews. We examine three types of valence shifters: negations, intensifiers, and diminishers. Negations are used to reverse the semantic polarity of a particular term, while intensifiers and diminishers are used to increase and decrease, respectively, the degree to which a term is positive or negative. The first method classifies reviews based on the number of positive and negative terms they contain. We use the General Inquirer to identify positive and negative terms, as well as negation terms, intensifiers, and diminishers. We also use positive and negative terms from other sources, including a dictionary of synonym differences and a very large Web corpus. To compute corpus‐based semantic orientation values of terms, we use their association scores with a small group of positive and negative terms. We show that extending the term‐counting method with contextual valence shifters improves the accuracy of the classification. The second method uses a Machine Learning algorithm, Support Vector Machines. We start with unigram features and then add bigrams that consist of a valence shifter and another word. The accuracy of classification is very high, and the valence shifter bigrams slightly improve it. The features that contribute to the high accuracy are the words in the lists of positive and negative terms. Previous work focused on either the term‐counting method or the Machine Learning method. We show that combining the two methods achieves better results than either method alone.