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The authors present a system developed for the 2011 i2b2 Challenge on Sentiment Classification, whose aim was to automatically classify sentences in suicide notes using a scheme of 15 topics, mostly emotions. The system combines machine learning with a rule-based methodology. The features used to represent a problem were based on lexico–semantic properties of individual words in addition to regular expressions used to represent patterns of word usage across different topics. A naïve Bayes classifier was trained using the features extracted from the training data consisting of 600 manually annotated suicide notes. Classification was then performed using the naïve Bayes classifier as well as a set of pattern–matching rules. The classification performance was evaluated against a manually prepared gold standard consisting of 300 suicide notes, in which 1,091 out of a total of 2,037 sentences were associated with a total of 1,272 annotations. The competing systems were ranked using the micro-averaged F-measure as the primary evaluation metric. Our system achieved the F-measure of 53% (with 55% precision and 52% recall), which was significantly better than the average performance of 48.75% achieved by the 26 participating teams.

The reasons that drive someone to commit suicide are complex and their study has attracted the attention of scientists in different domains. Analyzing this phenomenon could significantly improve the preventive efforts. In this paper we present a method for sentiment analysis of suicide notes submitted to the i2b2/VA/Cincinnati Shared Task 2011. In this task the sentences of 900 suicide notes were labeled with the possible emotions that they reflect. In order to label the sentence with emotions, we propose a hybrid approach which utilizes both rule based and machine learning techniques. To solve the multi class problem a rule-based engine and an SVM model is used for each category. A set of syntactic and semantic features are selected for each sentence to build the rules and train the classifier. The rules are generated manually based on a set of lexical and emotional clues. We propose a new approach to extract the sentence’s clauses and constitutive grammatical elements and to use them in syntactic and semantic feature generation. The method utilizes a novel method to measure the polarity of the sentence based on the extracted grammatical elements, reaching precision of 41.79 with recall of 55.03 for an f-measure of 47.50. The overall mean f-measure of all submissions was 48.75% with a standard deviation of 7%.

We present a system to automatically identify emotion-carrying sentences in suicide notes and to detect the specific fine-grained emotion conveyed. With this system, we competed in Track 2 of the 2011 Medical NLP Challenge,14 where the task was to distinguish between fifteen emotion labels, from guilt, sorrow, and hopelessness to hopefulness and happiness.

Since a sentence can be annotated with multiple emotions, we designed a thresholding approach that enables assigning multiple labels to a single instance. We rely on the probability estimates returned by an SVM classifier and experimentally set thresholds on these probabilities. Emotion labels are assigned only if their probability exceeds a certain threshold and if the probability of the sentence being emotion-free is low enough. We show the advantages of this thresholding approach by comparing it to a naïve system that assigns only the most probable label to each test sentence, and to a system trained on emotion-carrying sentences only.

This paper describes the National Research Council of Canada’s submission to the 2011 i2b2 NLP challenge on the detection of emotions in suicide notes. In this task, each sentence of a suicide note is annotated with zero or more emotions, making it a multi-label sentence classification task. We employ two distinct large-margin models capable of handling multiple labels. The first uses one classifier per emotion, and is built to simplify label balance issues and to allow extremely fast development. This approach is very effective, scoring an F-measure of 55.22 and placing fourth in the competition, making it the best system that does not use web-derived statistics or re-annotated training data. Second, we present a latent sequence model, which learns to segment the sentence into a number of emotion regions. This model is intended to gracefully handle sentences that convey multiple thoughts and emotions. Preliminary work with the latent sequence model shows promise, resulting in comparable performance using fewer features.

We describe our approach for creating a system able to detect emotions in suicide notes. Motivated by the sparse and imbalanced data as well as the complex annotation scheme, we have considered three hybrid approaches for distinguishing between the different categories. Each of the three approaches combines machine learning with manually derived rules, where the latter target very sparse emotion categories. The first approach considers the task as single label multi-class classification, where an SVM and a CRF classifier are trained to recognise fifteen different categories and their results are combined. Our second approach trains individual binary classifiers (SVM and CRF) for each of the fifteen sentence categories and returns the union of the classifiers as the final result. Finally, our third approach is a combination of binary and multi-class classifiers (SVM and CRF) trained on different subsets of the training data. We considered a number of different feature configurations. All three systems were tested on 300 unseen messages. Our second system had the best performance of the three, yielding an F1 score of 45.6% and a Precision of 60.1% whereas our best Recall (43.6%) was obtained using the third system.

This paper describes the sentiment classification system developed by the Mayo Clinic team for the 2011 I2B2/VA/Cincinnati Natural Language Processing (NLP) Challenge. The sentiment classification task is to assign any pertinent emotion to each sentence in suicide notes. We have implemented three systems that have been trained on suicide notes provided by the I2B2 challenge organizer—a machine learning system, a rule-based system, and a system consisting of a combination of both. Our machine learning system was trained on re-annotated data in which apparently inconsistent emotion assignment was adjusted. Then, the machine learning methods by RIPPER and multinomial Naïve Bayes classifiers, manual pattern matching rules, and the combination of the two systems were tested to determine the emotions within sentences. The combination of the machine learning and rule-based system performed best and produced a micro-average F-score of 0.5640.

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

We describe the Open University team’s submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.

This paper describes a system developed for Track 2 of the 2011 Medical NLP Challenge on identifying emotions in suicide notes. Our approach involves learning a collection of one-versus-all classifiers, each deciding whether or not a particular label should be assigned to a given sentence. We explore a variety of features types—syntactic, semantic and surface-oriented. Cost-sensitive learning is used for dealing with the issue of class imbalance in the data.

Objective:

To create a sentiment classification system for the Fifth i2b2/VA Challenge Track 2, which can identify thirteen subjective categories and two objective categories.

Design:

We developed a hybrid system using Support Vector Machine (SVM) classifiers with augmented training data from the Internet. Our system consists of three types of classification-based systems: the first system uses spanning n-gram features for subjective categories, the second one uses bag-of-n-gram features for objective categories, and the third one uses pattern matching for infrequent or subtle emotion categories. The spanning n-gram features are selected by a feature selection algorithm that leverages emotional corpus from weblogs. Special normalization of objective sentences is generalized with shallow parsing and external web knowledge. We utilize three sources of web data: the weblog of LiveJournal which helps to improve the feature selection, the eBay List which assists in special normalization of information and instructions categories, and the suicide project web which provides unlabeled data with similar properties as suicide notes.

Measurements:

The performance is evaluated by the overall micro-averaged precision, recall and F-measure.

Result:

Our system achieved an overall micro-averaged F-measure of 0.59. Happiness_peacefulness had the highest F-measure of 0.81. We were ranked as the second best out of 26 competing teams.

Conclusion:

Our results indicated that classifying fine-grained sentiments at sentence level is a non-trivial task. It is effective to divide categories into different groups according to their semantic properties. In addition, our system performance benefits from external knowledge extracted from publically available web data of other purposes; performance can be further enhanced when more training data is available.

Due to the complexity of emotions in suicide notes and the subtle nature of sentiments, this study proposes a fusion approach to tackle the challenge of sentiment classification in suicide notes: leveraging WordNet-based lexicons, manually created rules, character-based n-grams, and other linguistic features. Although our results are not satisfying, some valuable lessons are learned and promising future directions are identified.

In this paper we report on the approaches that we developed for the 2011 i2b2 Shared Task on Sentiment Analysis of Suicide Notes. We have cast the problem of detecting emotions in suicide notes as a supervised multi-label classification problem. Our classifiers use a variety of features based on (a) lexical indicators, (b) topic scores, and (c) similarity measures. Our best submission has a precision of 0.551, a recall of 0.485, and a F-measure of 0.516.

Operative notes contain rich information about techniques, instruments, and materials used in procedures. To assist development of effective information extraction (IE) techniques for operative notes, we investigated the sublanguage used to describe actions within the operative report ‘procedure description’ section. Deep parsing results of 362,310 operative notes with an expanded Stanford parser using the SPECIALIST Lexicon resulted in 200 verbs (92% coverage) including 147 action verbs. Nominal action predicates for each action verb were gathered from WordNet, SPECIALIST Lexicon, New Oxford American Dictionary and Stedman’s Medical Dictionary. Coverage gaps were seen in existing lexical, domain, and semantic resources (Unified Medical Language System (UMLS) Metathesaurus, SPECIALIST Lexicon, WordNet and FrameNet). Our findings demonstrate the need to construct surgical domain-specific semantic resources for IE from operative notes.

An ensemble of supervised maximum entropy classifiers can accurately detect and identify sentiments expressed in suicide notes. Using lexical and syntactic features extracted from a training set of externally annotated suicide notes, we trained separate classifiers for each of fifteen pre-specified emotions. This formed part of the 2011 i2b2 NLP Shared Task, Track 2. The precision and recall of these classifiers related strongly with the number of occurrences of each emotion in the training data. Evaluating on previously unseen test data, our best system achieved an F1 score of 0.534.

Background

Detection of sentences that describe protein-protein interactions (PPIs) in biomedical publications is a challenging and unresolved pattern recognition problem. Many state-of-the-art approaches for this task employ kernel classification methods, in particular support vector machines (SVMs). In this work we propose a novel data integration approach that utilises semantic kernels and a kernel classification method that is a probabilistic analogue to SVMs. Semantic kernels are created from statistical information gathered from large amounts of unlabelled text using lexical semantic models. Several semantic kernels are then fused into an overall composite classification space. In this initial study, we use simple features in order to examine whether the use of combinations of kernels constructed using word-based semantic models can improve PPI sentence detection.

Results

We show that combinations of semantic kernels lead to statistically significant improvements in recognition rates and receiver operating characteristic (ROC) scores over the plain Gaussian kernel, when applied to a well-known labelled collection of abstracts. The proposed kernel composition method also allows us to automatically infer the most discriminative kernels.

Conclusions

The results from this paper indicate that using semantic information from unlabelled text, and combinations of such information, can be valuable for classification of short texts such as PPI sentences. This study, however, is only a first step in evaluation of semantic kernels and probabilistic multiple kernel learning in the context of PPI detection. The method described herein is modular, and can be applied with a variety of feature types, kernels, and semantic models, in order to facilitate full extraction of interacting proteins.

We describe the submission entered by SRI International and UC Davis for the I2B2 NLP Challenge Track 2. Our system is based on a machine learning approach and employs a combination of lexical, syntactic, and psycholinguistic features. In addition, we model the sequence and locations of occurrence of emotions found in the notes. We discuss the effect of these features on the emotion annotation task, as well as the nature of the notes themselves. We also explore the use of bootstrapping to help account for what appeared to be annotator fatigue in the data. We conclude a discussion of future avenues for improving the approach for this task, and also discuss how annotations at the word span level may be more appropriate for this task than annotations at the sentence level.

Background

This work describes a system for identifying event mentions in bio-molecular research abstracts that are either speculative (e.g. analysis of IkappaBalpha phosphorylation, where it is not specified whether phosphorylation did or did not occur) or negated (e.g. inhibition of IkappaBalpha phosphorylation, where phosphorylation did not occur). The data comes from a standard dataset created for the BioNLP 2009 Shared Task. The system uses a machine-learning approach, where the features used for classification are a combination of shallow features derived from the words of the sentences and more complex features based on the semantic outputs produced by a deep parser.

Method

To detect event modification, we use a Maximum Entropy learner with features extracted from the data relative to the trigger words of the events. The shallow features are bag-of-words features based on a small sliding context window of 3-4 tokens on either side of the trigger word. The deep parser features are derived from parses produced by the English Resource Grammar and the RASP parser. The outputs of these parsers are converted into the Minimal Recursion Semantics formalism, and from this, we extract features motivated by linguistics and the data itself. All of these features are combined to create training or test data for the machine learning algorithm.

Results

Over the test data, our methods produce approximately a 4% absolute increase in F-score for detection of event modification compared to a baseline based only on the shallow bag-of-words features.

Conclusions

Our results indicate that grammar-based techniques can enhance the accuracy of methods for detecting event modification.

In 2007, suicide was the tenth leading cause of death in the U.S. Given the significance of this problem, suicide was the focus of the 2011 Informatics for Integrating Biology and the Bedside (i2b2) Natural Language Processing (NLP) shared task competition (track two). Specifically, the challenge concentrated on sentiment analysis, predicting the presence or absence of 15 emotions (labels) simultaneously in a collection of suicide notes spanning over 70 years. Our team explored multiple approaches combining regular expression-based rules, statistical text mining (STM), and an approach that applies weights to text while accounting for multiple labels. Our best submission used an ensemble of both rules and STM models to achieve a micro-averaged F1 score of 0.5023, slightly above the mean from the 26 teams that competed (0.4875).

This study evaluated the use of machine learning techniques in the classification of sentence type. 7253 structured abstracts and 204 unstructured abstracts of Randomized Controlled Trials from MedLINE were parsed into sentences and each sentence was labeled as one of four types (Introduction, Method, Result, or Conclusion). Support Vector Machine (SVM) and Linear Classifier models were generated and evaluated on cross-validated data. Treating sentences as a simple "bag of words", the SVM model had an average ROC area of 0.92. Adding a feature of relative sentence location improved performance markedly for some models and overall increasing the average ROC to 0.95. Linear classifier performance was significantly worse than the SVM in all datasets. Using the SVM model trained on structured abstracts to predict unstructured abstracts yielded performance similar to that of models trained with unstructured abstracts in 3 of the 4 types. We conclude that classification of sentence type seems feasible within the domain of RCT's. Identification of sentence types may be helpful for providing context to end users or other text summarization techniques.

In this study, we analyzed the compatibility between an ontology of the biomedical domain (the UMLS Semantic Network) and two other ontologies: the Upper Cyc Ontology (UCO) and WordNet. 1) We manually mapped UMLS Semantic Types to UCO. One fifth of the UMLS Semantic Types had exact mapping to UCO types. UCO provides generic concepts and a structure that relies on a larger number of categories, despite its lack of depth in the biomedical domain. 2) We compared semantic classes in the UMLS and WordNet. 2% of the UMLS concepts from the Health Disorder class were present in WordNet, and compatibility between classes was 48%. WordNet, as a general language-oriented ontology is a source of lay knowledge, particularly important for consumer health applications.

ASMOV (Automated Semantic Matching of Ontologies with Verification) is a novel algorithm that uses lexical and structural characteristics of two ontologies to iteratively calculate a similarity measure between them, derives an alignment, and then verifies it to ensure that it does not contain semantic inconsistencies. In this paper, we describe the ASMOV algorithm, and then present experimental results that measure its accuracy using the OAEI 2008 tests, and that evaluate its use with two different thesauri: WordNet, and the Unified Medical Language System (UMLS). These results show the increased accuracy obtained by combining lexical, structural and extensional matchers with semantic verification, and demonstrate the advantage of using a domain-specific thesaurus for the alignment of specialized ontologies.

This paper reports on the results of an initiative to create and annotate a corpus of suicide notes that can be used for machine learning. Ultimately, the corpus included 1,278 notes that were written by someone who died by suicide. Each note was reviewed by at least three annotators who mapped words or sentences to a schema of emotions. This corpus has already been used for extensive scientific research.

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large set of labels. The number of participants was larger than in any previous biomedical challenge task. We describe the data production process and the evaluation measures, and give a preliminary analysis of the results. Many systems performed at levels approaching the inter-coder agreement, suggesting that human-like performance on this task is within the reach of currently available technologies.

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate future research. The other key feature of the task is that it required categorization with respect to a large set of labels. The number of participants was larger than in any previous biomedical challenge task. We describe the data production process and the evaluation measures, and give a preliminary analysis of the results. Many systems performed at levels approaching the inter-coder agreement, suggesting that human-like performance on this task is within the reach of currently available technologies.

Background

Alexithymia, or “no words for feelings”, is a personality trait which is associated with difficulties in emotion recognition and regulation. It is unknown whether this deficit is due primarily to regulation, perception, or mentalizing of emotions. In order to shed light on the core deficit, we tested our subjects on a wide range of emotional tasks. We expected the high alexithymics to underperform on all tasks.

Method

Two groups of healthy individuals, high and low scoring on the cognitive component of the Bermond-Vorst Alexithymia Questionnaire, completed questionnaires of emotion regulation and performed several emotion processing tasks including a micro expression recognition task, recognition of emotional prosody and semantics in spoken sentences, an emotional and identity learning task and a conflicting beliefs and emotions task (emotional mentalizing).

Results

The two groups differed on the Emotion Regulation Questionnaire, Berkeley Expressivity Questionnaire and Empathy Quotient. Specifically, the Emotion Regulation Quotient showed that alexithymic individuals used more suppressive and less reappraisal strategies. On the behavioral tasks, as expected, alexithymics performed worse on recognition of micro expressions and emotional mentalizing. Surprisingly, groups did not differ on tasks of emotional semantics and prosody and associative emotional-learning.

Conclusion

Individuals scoring high on the cognitive component of alexithymia are more prone to suppressive emotion regulation strategies rather than reappraisal strategies. Regarding emotional information processing, alexithymia is associated with reduced performance on measures of early processing as well as higher order mentalizing. However, difficulties in the processing of emotional language were not a core deficit in our alexithymic group.