Точность классификации

This paper describes, compares, and evaluates three dif- ferent approaches for learning a semantic classification of library titles: 1) syntactically condensed titles, 2) com- plete titles, and 3) titles without …...

In many types of technical texts, meaning is embedded in noun compounds. A language un- derstanding program needs to be able to inter- pret these in order to ascertain sentence …...

This paper describes a method for utterance classification that does not require manual transcription of training data. The method combines domain independent acoustic models with off-the-shelf classifiers to give utterance …...

We address appropriate user modeling in order to generate cooperative responses to each user in spoken dialogue systems. Un- like previous studies that focus on user’s knowledge or typical kinds …...

We address the problem dealing with a large collection of data, and investigate the use of automatically constructing category hierarchy from a given set of categories to improve clas- sification …...

We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination …...

Most research on automated categorization of doc- uments has concentrated on the assignment of one or many categories to a whole text. However, new applications, e.g. in the area of …...

Precise Natural Language Understanding is needed in Geometry Tutoring to accurately determine the semantic content of students’ explanations. The paper presents an NLU system developed in the context of the …...

We propose models for semantic orienta- tions of phrases as well as classification methods based on the models. Although each phrase consists of multiple words, the semantic orientation of the …...

We describe a novel language-independent ap- proach to the task of determining the polarity, positive or negative, of the author’s opinion on a specific topic in natural language text. In …...

AbstractResearch on automatic humor recognition has developed several features which discriminate funny text from ordinary text. The features have been demonstrated to work well when classifying the funniness of single …...

The problem of automatically classifying the gender of a blog author has important appli- cations in many commercial domains. Exist- ing systems mainly use features such as words, word classes, …...

Automatically judging sentences for their grammaticality is potentially useful for several purposes — evaluating language technology systems, assessing language competence of second or foreign lan- guage learners, and so on. …...

A medical publication may or may not present an outcome. When an outcome is present, its polarity may be positive, negative or neu- tral. Information about the polarity of an …...

Native language identification (NLI) is the task of determining the native language of an author writing in a second language. Several pieces of earlier work have found that fea- tures …...

AbstractWe address the problem of automatic classification of associative and semantic relations between words, and particularly those that hold between nouns. Lexical relations such as synonymy, hypernymy/hyponymy, constitute the fundamental …...

AbstractWe present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an …...

AbstractIn this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs. The …...

Automatic methods to create entity dictionaries or gazetteers have used only a small number of entity types (18 at maximum), which could pose a limitation for fine-grained information extraction. This …...

Previous approaches to the task of native language identification (Koppel et al., 2005) have been limited to small, within-corpus evaluations. Because these are restrictive and unreliable, we apply cross-corpus evaluation …...

Citations are a valuable resource for characterizing scientific publications that has already been used in applications such as summarization and information retrieval. These applications could be even better served by …...

Document revision histories are a useful and abundant source of data for natural language processing, but selecting relevant data for the task at hand is not trivial. In this paper …...

Question Classification plays a significant part in Question Answering system. In order to obtain a classifier, we present in this pa- per1 a pragmatic approach that utilizes sim- ple sentence …...

Short answer questions for reading compre- hension are a common task in foreign lan- guage learning. Automatic short answer scor- ing is the task of automatically assessing the semantic content …...

This paper defines a systematic approach to Opinion Mining (OM) on YouTube comments by (i) modeling classifiers for predicting the opinion polarity and the type of comment and (ii) proposing …...

Inferring latent attributes of online users has many applications in public health, politics, and marketing. Most existing approaches rely on supervised learning algorithms, which re- quire manual data annotation and …...

Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the …...

AbstractTarget-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment …...

In a multi-label text classification task, in which multiple labels can be assigned to one text, label co-occurrence itself is informative. We propose a novel neural network initializa- tion method …...

The aim of this paper is to produce a method- ology for analyzing sentiments of selected Twitter messages, better known as Tweets. This project elaborates on two experiments carried out …...

AbstractIn this paper, we present a study for the identification of authors’ national variety of English in texts from social media. In data from Facebook and Twitter, information about the …...

In this paper we use a novel approach towards Arabic dialect identification using language bivalency and written code-switching. Bivalency between languages or dialects is where a word or element is …...

Colloquial dialects of Arabic can be roughly categorized into five groups based on relatedness and geographic location (Egyptian, North African/Maghrebi, Gulf, Iraqi, and Levantine), but given that all dialects utilize …...

Many computer-assisted language learning (CALL) systems offer gap-fill items, often with multiple choices in order to facilitate im- mediate feedback. Automatic distractor gener- ation can therefore be helpful in providing …...

AbstractFree text data from social media is now widely used in natural language processing research, and one of the most common machine learning tasks performed on this data is classification. …...

AbstractIn hierarchical text classification, we perform a sequence of inference steps to predict the category of a document from top to bottom of a given class taxonomy. Most of the …...

AbstractThe proliferation of fake news is a current issue that influences a number of important areas of society, such as politics, economy and health. In the Natural Language Processing area, …...

AbstractIn the case of using a deep learning (machine learning) framework for emotion classification, one significant difficulty faced is the requirement of building a large, emotion corpus in which each …...

AbstractThis paper describes our TemporalTeller system for SemEval Task 1: Unsupervised Lexical Semantic Change Detection. We develop a unified framework for the common semantic change detection pipelines including preprocessing, learning …...

AbstractIdentifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural …...

AbstractThe recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the …...

AbstractSeveral studies have been carried out on revealing linguistic features captured by BERT. This is usually achieved by training a diagnostic classifier on the representations obtained from different layers of …...

AbstractAbusive language detection has become an important tool for the cultivation of safe online platforms. We investigate the interaction of annotation quality and classifier performance. We use a new, fine-grained …...

AbstractWe report two essential improvements in readability assessment: 1. three novel features in advanced semantics and 2. the timely evidence that traditional ML models (e.g. Random Forest, using handcrafted features) …...

AbstractData augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations. Traditional methods first devise task-specific operations such as Synonym Substitute, then preset the …...

AbstractThe study of metaphors in media discourse is an increasingly researched topic as media are an important shaper of social reality and metaphors are an indicator of how we think …...

AbstractWeakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these …...

AbstractThis work deploys linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that distinguish the two …...

AbstractModern NLP classifiers are known to return uncalibrated estimations of class posteriors. Existing methods for posterior calibration rescale the predicted probabilities but often have an adverse impact on final classification …...

AbstractConstruction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central …...

AbstractMost systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends …...

AbstractHierarchical text classification (HTC) is a challenging task, in which the labels of texts can be organized into a category hierarchy. To deal with the HTC problem, many existing works …...