Diana Inkpen

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.

Semantic text similarity using corpus-based word similarity and string similarity

We present a method for measuring the semantic similarity of texts using a corpus-based measure of semantic word similarity and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Existing methods for computing text similarity have focused mainly on either large documents or individual words. We focus on computing the similarity between two sentences or two short paragraphs. The proposed method can be exploited in a variety of applications involving textual knowledge representation and knowledge discovery. Evaluation results on two different data sets show that our method outperforms several competing methods.

Real-Word Spelling Correction using Google Web 1T 3-grams

We present a method for detecting and correcting multiple real-word spelling errors using the Google Web IT 3-gram data set and a normalized and modified version of the Longest Common Subsequence (LCS) string matching algorithm. Our method is focused mainly on how to improve the detection recall (the fraction of errors correctly detected) and the correction recall (the fraction of errors correctly amended), while keeping the respective precisions (the fraction of detections or amendments that are correct) as high as possible. Evaluation results on a standard data set show that our method outperforms two other methods on the same task.

Building and Using a Lexical Knowledge Base of Near-Synonym Differences

Choosing the wrong word in a machine translation or natural language generation system can convey unwanted connotations, implications, or attitudes. The choice between near-synonyms such as error, mistake, slip, and blunderwords that share the same core meaning, but differ in their nuancescan be made only if knowledge about their differences is available. We present a method to automatically acquire a new type of lexical resource: a knowledge base of near-synonym differences. We develop an unsupervised decision-list algorithm that learns extraction patterns from a special dictionary of synonym differences. The patterns are then used to extract knowledge from the text of the dictionary. The initial knowledge base is later enriched with information from other machine-readable dictionaries. Information about the collocational behavior of the near-synonyms is acquired from free text. The knowledge base is used by Xenon, a natural language generation system that shows how the new lexical resource can be used to choose the best near-synonym in specific situations.

A new algorithm for reducing the workload of experts in performing systematic reviews

OBJECTIVE To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment. DESIGN The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not. Weight engineering (WE) techniques were added to reduce underestimation for Medical Subject Headings (MeSH)-based and Publication Type (PubType)-based features. Cross-validation experiments were performed to evaluate the classifiers parameters and performance. MEASUREMENTS Work saved over sampling (WSS) at no less than a 95% recall was used as the main measure of performance. RESULTS The minimum workload reduction for a systematic review for one topic, achieved with a FCNB/WE classifier, was 8.5%; the maximum was 62.2% and the average over the 15 topics was 33.5%. This is 15.0% higher than the average workload reduction obtained using a voting perceptron-based automated citation classification system. CONCLUSION The FCNB/WE classifier is simple, easy to implement, and produces significantly better results in reducing the workload than previously achieved. The results support it being a useful algorithm for machine-learning-based automation of systematic reviews of drug class efficacy for disease treatment.

Enhanced LSTM for Natural Language Inference

Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data (Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford Natural Language Inference Dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result---it further improves the performance even when added to the already very strong model.

A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts

The Machine Learning (ML) field has gained its momentum in almost any domain of research and just recently has become a reliable tool in the medical domain. The empirical domain of automatic learning is used in tasks such as medical decision support, medical imaging, protein-protein interaction, extraction of medical knowledge, and for overall patient management care. ML is envisioned as a tool by which computer-based systems can be integrated in the healthcare field in order to get a better, more efficient medical care. This paper describes a ML-based methodology for building an application that is capable of identifying and disseminating healthcare information. It extracts sentences from published medical papers that mention diseases and treatments, and identifies semantic relations that exist between diseases and treatments. Our evaluation results for these tasks show that the proposed methodology obtains reliable outcomes that could be integrated in an application to be used in the medical care domain. The potential value of this paper stands in the ML settings that we propose and in the fact that we outperform previous results on the same data set.

Acquiring Collocations for Lexical Choice between Near-Synonyms

We extend a lexical knowledge-base of near-synonym differences with knowledge about their collocational behaviour. This type of knowledge is useful in the process of lexical choice between near-synonyms. We acquire collocations for the near-synonyms of interest from a corpus (only collocations with the appropriate sense and part-of-speech). For each word that collocates with a near-synonym we use a differential test to learn whether the word forms a less-preferred collocation or an anti-collocation with other near-synonyms in the same cluster. For this task we use a much larger corpus (the Web). We also look at associations (longer-distance co-occurrences) as a possible source of learning more about nuances that the near-synonyms may carry.

Using a heterogeneous dataset for emotion analysis in text

In this paper, we adopt a supervised machine learning approach to recognize six basic emotions (anger, disgust, fear, happiness, sadness and surprise) using a heterogeneous emotion-annotated dataset which combines news headlines, fairy tales and blogs. For this purpose, different features sets, such as bags of words, and N-grams, were used. The Support Vector Machines classifier (SVM) performed significantly better than other classifiers, and it generalized well on unseen examples.

Semantic Similarity for Detecting Recognition Errors in Automatic Speech Transcripts

Browsing through large volumes of spoken audio is known to be a challenging task for end users. One way to alleviate this problem is to allow users to gist a spoken audio document by glancing over a transcript generated through Automatic Speech Recognition. Unfortunately, such transcripts typically contain many recognition errors which are highly distracting and make gisting more difficult. In this paper we present an approach that detects recognition errors by identifying words which are semantic outliers with respect to other words in the transcript. We describe several variants of this approach. We investigate a wide range of evaluation measures and we show that we can significantly reduce the number of errors in content words, with the trade-off of losing some good content words.