Query: Classification sentences

title: A Deep Neural Network Sentence Level Classification Method with Context Information
abstract: In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification. This context is, however, often ignored. Where methods do make use of context, only small amounts are considered, making it difficult to scale. We present a new method for sentence classification, Context LSTM CNN, that makes use of potentially large contexts. The method also utilizes long range dependencies within the sentence being classified, using an LSTM, and short span features, using a stacked CNN. Our experiments demonstrate that this approach consistently improves over previous methods on two different datasets.

title: Multi Label Classification of Patient Notes a Case Study on ICD Code Assignment
abstract: In the context of the Electronic Health Record, automated diagnosis coding of patient notes is a useful task, but a challenging one due to the large number of codes and the length of patient notes. We investigate four models for assigning multiple ICD codes to discharge summaries taken from both MIMIC II and III. We present Hierarchical Attention GRU (HA GRU) a hierarchical approach to tag a document by identifying the sentences relevant for each label. HA GRU achieves state of the art results. Furthermore, the learned sentence level attention layer highlights the model decision process, allows easier error analysis, and suggests future directions for improvement.

title: AttnConvnet at SemEval 2018 Task 1: Attention based Convolutional Neural Networks for Multi label Emotion Classification
abstract: In this paper, we propose an attention based classifier that predicts multiple emotions of a given sentence. Our model imitates human's two step procedure of sentence understanding and it can effectively represent and classify sentences. With emoji to meaning preprocessing and extra lexicon utilization, we further improve the model performance. We train and evaluate our model with data provided by SemEval 2018 task 1 5, each sentence of which has several labels among 11 given emotions. Our model achieves 5th/1st rank in English/Spanish respectively.

title: Translations as Additional Contexts for Sentence Classification
abstract: In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier. However, such contexts are domain dependent and thus cannot be used for another classification task with an inappropriate domain. In contrast, we propose the use of translated sentences as context that is always available regardless of the domain. We find that naive feature expansion of translations gains only marginal improvements and may decrease the performance of the classifier, due to possible inaccurate translations thus producing noisy sentence vectors. To this end, we present multiple context fixing attachment (MCFA) a series of modules attached to multiple sentence vectors to fix the noise in the vectors using the other sentence vectors as context. We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets. We are the first to use translations as domain free contexts for sentence classification.

title: Automatic Classification of Sentences in Dutch Laws
abstract: The work described here builds on  where we presented a categorisation of norms or provisions in legislation. We claimed that the categories are characterized by the use of typical sentence structures and that this would enable automatic detection and classification. In this paper we present the results of experiments in such automatic classification of provisions. We have defined fourteen different categories of provisions, and compiled a list of 81 sentence structures for those categories from twenty Dutch laws. Based on these structures, a parser was used to classify the sentences in fifteen different Dutch laws, classifying 94% of 476 sentences correctly. It compares well with other, statistical approaches. An important improvement of our classifier will be the distinction of principal and auxiliary sentences.

title: Sentiment Classification towards Question Answering with Hierarchical Matching Network
abstract: In an e commerce environment, user oriented question answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high quality annotated corpus with specially designed annotation guidelines for QA style sentiment classification. On the basis, we propose a three stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q sentence, A sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q sentence, A sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self matching attention layer. Experimental results, comparing with a number of state of the art baselines, demonstrate the impressive effectiveness of the proposed approach for QA style sentiment classification.

title: Movie Genre Classification from Plot Summaries Using Bidirectional LSTM
abstract: Movie plot summaries are expected to reflect the genre of movies since many spectators read the plot summaries before deciding to watch a movie. In this study, we perform movie genre classification from plot summaries of movies using bidirectional LSTM (Bi LSTM) We first divide each plot summary of a movie into sentences and assign the genre of corresponding movie to each sentence. Next, using the word representations of sentences, we train Bi LSTM networks. We estimate the genres for each sentence separately. Since plot summaries generally contain multiple sentences, we use majority voting for the final decision by considering the posterior probabilities of genres assigned to sentences. Our results reflect that, training Bi LSTM network after dividing the plot summaries into their sentences and fusing the predictions for individual sentences outperform training the network with the whole plot summaries with the limited amount of data. Moreover, employing Bi LSTM performs better compared to basic Recurrent Neural Networks (RNNs) and Logistic Regression (LR) as a baseline.

title: PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
abstract: We present PubMed 200k RCT, a new dataset based on PubMed for sequential sentence classification. The dataset consists of approximately 200,000 abstracts of randomized controlled trials, totaling 2.3 million sentences. Each sentence of each abstract is labeled with their role in the abstract using one of the following classes: background, objective, method, result, or conclusion. The purpose of releasing this dataset is twofold. First, the majority of datasets for sequential short text classification (i.e. classification of short texts that appear in sequences) are small: we hope that releasing a new large dataset will help develop more accurate algorithms for this task. Second, from an application perspective, researchers need better tools to efficiently skim through the literature. Automatically classifying each sentence in an abstract would help researchers read abstracts more efficiently, especially in fields where abstracts may be long, such as the medical field.

title: Extractive Summarization Using Multi Task Learning with Document Classification
abstract: The need for automatic document summarization that can be used for practical applications is increasing rapidly. In this paper, we propose a general framework for summarization that extracts sentences from a document using externally related information. Our work is aimed at single document summarization using small amounts of reference summaries. In particular, we address document summarization in the framework of multi task learning using curriculum learning for sentence extraction and document classification. The proposed framework enables us to obtain better feature representations to extract sentences from documents. We evaluate our proposed summarization method on two datasets: financial report and news corpus. Experimental results demonstrate that our summarizers achieve performance that is comparable to state of the art systems.

title: Portable automatic text classification for adverse drug reaction detection via multi corpus training
abstract: Automatic detection of adverse drug reaction (ADR) mentions from text has recently received significant interest in pharmacovigilance research. Current research focuses on various sources of text based information, including social media where enormous amounts of user posted data is available, which have the potential for use in pharmacovigilance if collected and filtered accurately. The aims of this study are: (i) to explore natural language processing (NLP) approaches for generating useful features from text, and utilizing them in optimized machine learning algorithms for automatic classification of ADR assertive text segments; (ii) to present two data sets that we prepared for the task of ADR detection from user posted internet data; and (iii) to investigate if combining training data from distinct corpora can improve automatic classification accuracies.  One of our three data sets contains annotated sentences from clinical reports, and the two other data sets, built in house, consist of annotated posts from social media. Our text classification approach relies on generating a large set of features, representing semantic properties (e.g. sentiment, polarity, and topic) from short text nuggets. Importantly, using our expanded feature sets, we combine training data from different corpora in attempts to boost classification accuracies.  Our feature rich classification approach performs significantly better than previously published approaches with ADR class F scores of 0.812 (previously reported best: 0.770) 0.538 and 0.678 for the three data sets. Combining training data from multiple compatible corpora further improves the ADR F scores for the in house data sets to 0.597 (improvement of 5.9 units) and 0.704 (improvement of 2.6 units) respectively.  Our research results indicate that using advanced NLP techniques for generating information rich features from text can significantly improve classification accuracies over existing benchmarks. Our experiments illustrate the benefits of incorporating various semantic features such as topics, concepts, sentiments, and polarities. Finally, we show that integration of information from compatible corpora can significantly improve classification performance. This form of multi corpus training may be particularly useful in cases where data sets are heavily imbalanced (e.g. social media data) and may reduce the time and costs associated with the annotation of data in the future.