Сөйлемдерді жіктеу

In this paper we describe a biography summarization system using sentence classification and ideas from information retrieval. Although the individual techniques are not new, assembling and applying them to generate …...

We present a set of experiments involving sentence classification, addressing issues of representation and feature selection, and we compare our findings with simi- lar results from work on the more …...

In this paper we propose algorithms to automatically classify sentences into metaphoric or normal usages. Our algo- rithms only need the WordNet and bigram counts, and does not require training. …...

. This paper addresses a task of opinion extraction from given documents and its positive/negative classification. We propose a sentence classification method using a notion of syntactic piece. Syntactic piece …...

In this paper, we propose an automatic sen- tence classification model that can map sen- tences of a given biomedical abstract into set of pre-defined categories which are used for …...

In our contribution to the ALTA 2012 shared task we experimented with the use of cluster-based features for sentence clas- sification. In a first stage we cluster the documents according …...

We describe the feature sets and methodol- ogy that produced the winning entry to the ALTA 2012 Shared Task (sentence classi- fication in evidence-based medicine). Our approach is based on …...

We study the automatic detection of sug- gestion expressing text among the opin- ionated text. The examples of such sug- gestions in online reviews would be, cus- tomer suggestions about …...

Kernel methods have been widely stud- ied in several natural language process- ing tasks such as relation extraction and sentence classification. In this work, we present a new graph kernel …...

We compare different word embeddings from a standard window based skipgram model, a skipgram model trained using dependency context features and a novel skipgram variant that utilizes additional information from …...

AbstractWe present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory …...

AbstractRecent work on the problem of latent tree learning has made it possible to train neural networks that learn to both parse a sentence and use the resulting parse to …...

Sentence Classification is one of the most fun- damental tasks in NLP, where the aim is to classify a given sentence into a pre-defined set of classes. A lot of …...

AbstractThe GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at …...

AbstractAs a step toward better document-level understanding, we explore classification of a sequence of sentences into their corresponding categories, a task that requires understanding sentences in context of the document. …...

AbstractObtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et. al., 2018) to …...

AbstractDefinition extraction is an important task in Nature Language Processing, and it is used to identify the terms and definitions related to terms. The task contains sentence classification task (i.e., …...

AbstractThis paper describes participation in DeftEval 2020 (part of SemEval sharing task competition), and is focused on the sentence classification. Our approach to the task was to create an ensemble …...

AbstractIn this paper we present, describe, and evaluate SentiEcon, a large, comprehensive, domain-specific computational lexicon designed for sentiment analysis applications, for which we compiled our own corpus of online business …...

AbstractWhile much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient. We …...

AbstractMultilingual pre-trained models could leverage the training data from a rich source language (such as English) to improve performance on low resource languages. However, the transfer quality for multilingual Machine …...

AbstractThis paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling …...

AbstractNeural network based models have achieved impressive results on the sentence classification task. However, most of previous work focuses on designing more sophisticated network or effective learning paradigms on monolingual …...

AbstractThis paper focuses on tree-based modeling for the sentence classification task. In existing works, aggregating on a syntax tree usually considers local information of sub-trees. In contrast, in addition to …...

AbstractDetection of toxic spans - detecting toxicity of contents in the granularity of tokens - is crucial for effective moderation of online discussions. The baseline approach for this problem using …...

AbstractThe input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with …...

Pre-trained Transformer-based neural lan- guage models, such as BERT, have achieved remarkable results on varieties of NLP tasks. Recent works have shown that attention-based models can benefit from more focused …...

AbstractMultilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation …...

AbstractAs unlabeled data carry rich task-relevant information, they are proven useful for few-shot learning of language model. The question is how to effectively make use of such data. In this …...

AbstractIn the pursuit of natural language understanding, there has been a long standing interest in tracking state changes throughout narratives. Impressive progress has been made in modeling the state of …...

AbstractWe propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications. To identify the most important …...

AbstractGenerating synthetic data for supervised learning from large-scale pre-trained language models has enhanced performances across several NLP tasks, especially in low-resource scenarios. In particular, many studies of data augmentation employ …...

AbstractSynonym and antonym practice are the most common practices in our early childhood. It correlated our known words to a better place deep in our intuition. At the beginning of …...

AbstractLogical reasoning over text is an important ability that requires understanding the semantics of the text and reasoning through them to arrive at correct inferences. Prior works on pretraining language …...