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. We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object using not binary polarity (good or bad) but …...

We address the rating-inference problem, wherein rather than simply decide whether a review is “thumbs up” or “thumbs down”, as in previous sentiment analy- sis work, one must determine an …...

AbstractAutomatically detecting discourse segments is an important preliminary step towards full discourse parsing. Previous research on discourse segmentation have relied on the assumption that elementary discourse units (EDUs) in a …...

In this paper we introduce a method for searching appropriate articles from knowledge bases (e.g. Wikipedia) for a given query and its context. In our approach, this problem is transformed …...

Part-of-speech (POS) taggers can be quite accurate, but for practical use, accuracy often has to be sacrificed for speed. For example, the maintainers of the Stanford tagger (Toutanova et al., …...

In this paper, we present a novel ap- proach for identifying argumentative dis- course structures in persuasive essays. The structure of argumentation consists of sev- eral components (i.e. claims and …...

Answer Validation is an important step in Automatic Question Answering systems and nowadays by spreading Community Question Answering systems it is known as an impor- tant task by itself. Previous …...

In this paper we describe the SpRL-CWW en- try into SemEval 2015: Task 8 SpaceEval. It detects spatial and motion relations as de- fined by the ISO-Space specifications in two …...

Understanding the nature of the errors of a machine learning system is often difficult for multiclass classification problems with a large number of classes. This is true even more so …...

AbstractWe present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely …...

AbstractThis paper investigates learning methods for multi-class classification using labeled data for the target classification scheme and another labeled data for a similar but different classification scheme (support scheme). We …...

AbstractIn this paper, we describe experiments designed to explore and evaluate the impact of punctuation marks on the task of native language identification. Punctuation is specific to each language, and …...

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. …...

AbstractStance detection in social media is a well-studied task in a variety of domains. Nevertheless, previous work has mostly focused on multiclass versions of the problem, where the labels are …...

AbstractWe report our ongoing work about a new deep architecture working in tandem with a statistical test procedure for jointly training texts and their label descriptions for multi-label and multi-class …...

AbstractNative language identification (NLI) – identifying the native language (L1) of a person based on his/her writing in the second language (L2) – is useful for a variety of purposes, …...

AbstractThe ability to control for the kinds of information encoded in neural representation has a variety of use cases, especially in light of the challenge of interpreting these models. We …...

Neural biomedical named entity recognition (BioNER) methods usually require a large amount of annotated data, while the anno- tated BioNER datasets are often difficult to obtain and small in scale …...

AbstractThis paper presents a translationese study based on the parallel data from the Russian National Corpus (RNC). We explored differences between literary texts originally authored in Russian and fiction translated …...

AbstractOffensive language detection and analysis has become a major area of research in Natural Language Processing. The freedom of participation in social media has exposed online users to posts designed …...

AbstractMitigating bias in training on biased datasets is an important open problem. Several techniques have been proposed, however the typical evaluation regime is very limited, considering very narrow data conditions. …...

AbstractAutomatic approaches to irony detection have been of interest to the NLP community for a long time, yet, state-of-the-art approaches still fall way short of what one would consider a …...

AbstractEntailment has been recognized as an important metric for evaluating natural language understanding (NLU) models, and recent studies have found that entailment pretraining benefits weakly supervised fine-tuning. In this work, …...