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In this paper, we evaluate a vari- ety of knowledge sources and super- vised learning algorithms for word sense disambiguation on SENSEVAL-2 and SENSEVAL-1 data. Our knowledge sources include the …...

This paper investigates several augmented mixture models that are competitive alternatives to standard Bayesian models and prove to be very suitable to word sense disambiguation and related classifica- tion tasks. …...

Automatic text categorization is a problem of automatically assigning text documents to predefined categories. In order to classify text documents, we must extract good features from them. In previous research, …...

In this paper, we address the problem of dealing with a large collection of data and propose a method for text classifi- cation which manipulates data using two well-known machine …...

This paper proposes a multi-dimensional framework for classifying text documents. In this framework, the concept of multi- dimensional category model is introduced for representing classes. In contrast with traditional flat …...

We consider the problem of classifying doc- uments not by topic, but by overall senti- ment, e.g., determining whether a review is positive or negative. Using movie re- views as …...

We describe experiments with a Naive Bayes text classifier in the context of anti- spam E-mail filtering, using two different statistical event models: a mul- ti-variate Bernoulli model and a …...

In this paper, we introduce a new semi-supervised learning model for word sense disambiguation based on Kernel Prin- cipal Component Analysis (KPCA), with experiments showing that it can further improve …...

In this paper, we describe our experiments on statistical word sense disambiguation (WSD) using two systems based on different ap- proaches: Na¨ıve Bayes on word tokens and Max- imum Entropy …...

Sentiment classification is the task of labeling a re- view document according to the polarity of its pre- vailing opinion (favorable or unfavorable). In ap- proaching this problem, a model …...

In this paper, we examine the task of extracting a set of biographic facts about target individuals from a collection of Web pages. We automatically anno- tate training text with …...

We present results on addressee identifica- tion in four-participants face-to-face meet- ings using Bayesian Network and Naive Bayes classifiers. First, we investigate how well the addressee of a dialogue act …...

In this paper, we describe our two SemEval- 2007 entries. Our first entry, for Task 5: Multilingual Chinese-English Lexical Sam- ple Task, is a supervised system that decides the most …...

This paper describes a system for classify- ing semantic relations among nominals, as in SemEval task 4. This system uses a corpus of 2,500 compounds annotated with WordNet senses and …...

Searching and reading the Web is one of the principal methods used to seek out infor- mation to resolve problems about technol- ogy in general and digital devices in partic- …...

AbstractThis paper proposes a new method of the sentiment analysis utilizing inter-sentence structures especially for coping with reversal phenomenon of word polarity such as quotation of other’s opinions on an …...

Financial investors trade on the basis of infor- mation, and in particular, on the likelihood that a piece of information will impact the mar- ket. The ability to predict this …...

. In this article, we report significant findings resulting from an investigation into the correlation between adjective density, calculated as the proportion of adjectives in word tokens, and degrees of …...

This paper presents a first-order logic learn- ing approach to determine rhetorical relations between discourse segments. Beyond lin- guistic cues and lexical information, our ap- proach exploits compositional semantics and …...

In this paper, it is presented an unsuper- vised approach to automatically discover the latent keyphrases contained in scien- tific articles. The proposed technique is constructed on the basis of …...

Pathology reports are used to store infor- mation about cells and tissues of a patient, and they are crucial to monitor the health of individuals and population groups. In this …...

Recurrent event queries (REQ) constitute a special class of search queries occurring at regular, predictable time intervals. The fresh- ness of documents ranked for such queries is generally of critical …...

AbstractThis paper presented an overview of Chinese bi-character words’ morphological types, and proposed a set of features for machine learning approaches to predict these types based on composite characters’ information. …...

Quotations from financial leaders can have significant influence upon the immediate prospects of economic actors. Indiscreet or candid comments from senior business leaders have had detrimental effects upon their organizations. …...

We propose the negation naive Bayes (NNB): a new method to categorize prod- uct pages on the Web depending on their information. It is a modified version of the naive …...

We offer a supervised machine learn- ing approach for recognizing erroneous words in the output of a speech recognizer. We have investigated several sets of fea- tures combined with two …...

Sentiment Analysis in Twitter has become an important task due to the huge user-generated content published over such media. Such analysis could be useful for many domains such as Marketing, …...

This paper describes the systems with which we participated in the task Sentiment Analysis in Twitter of SEMEVAL 2013 and specifically the Message Polarity Classification. We used a 2-stage pipeline …...

This paper is to introduce our participation in the WMT13 shared tasks on Quality Estima- tion for machine translation without using ref- erence translations. We submitted the results for Task …...

Semi-supervised learning (SSL) methods augment standard machine learning (ML) techniques to leverage unlabeled data. SSL techniques are often effective in text classification, where labeled data is scarce but large unlabeled …...

In phrase-based statistical machine trans- lation systems, variation in grammatical structures between source and target lan- guages can cause large movements of phrases. Modeling such movements is cru- cial in …...

AbstractEarly detection of suicidal thoughts is an important part of effective suicide prevention. Such thoughts may be expressed online, especially by young people. This paper presents on-going work on the …...

AbstractWe show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text. As an example …...

The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figura- tive Language in Twitter. The proposed ap- proach employs syntactical and morphological features, which indicate sentiment polarity …...

In regard to document classification, semi- supervised learning using the Naive Bayes method and EM algorithm was a great suc- cess, and we refer to this method as NBEM in …...

Named Entity Disambiguation (NED) al- gorithms disambiguate mentions of named entities with respect to a knowledge-base, but sometimes the context might be poor or misleading. In this paper we introduce …...

AbstractIn this paper, we present a study on tweet classification which aims to define the communication behavior of the 103 French museums that participated in 2014 in the Twitter operation: …...

This paper is a novel study that views sarcasm detection in dialogue as a sequence labeling task, where a dialogue is made up of a se- quence of utterances. We …...

The complex word identification task refers to the process of identifying difficult words in a sentence from the perspective of readers be- longing to a specific target audience. This task …...

This paper describes the results of the partici- pation of The University of Melbourne in the community question-answering (CQA) task of SemEval 2016 (Task 3-B). We obtained a MAP score …...

AbstractNBSVM is one of the most popular methods for text classification and has been widely used as baselines for various text representation approaches. It uses Naive Bayes (NB) feature to …...

AbstractThis paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on …...

AbstractThis paper describes our approach for SemEval-2017 Task 4: Sentiment Analysis in Twitter. We have participated in Subtask A: Message Polarity Classification subtask and developed two systems. The first system …...

Under the issue of gender and Natural Language Processing (NLP), most papers aim at gender- norm language that spoken by biologically males and females with opposite-sex desires. However, from the …...

AbstractThis paper describes the system devel-oped for SemEval 2017 task 6: #HashTagWars -Learning a Sense of Hu-mor. Learning to recognize sense of hu-mor is the important task for language understanding …...

The quality of training data is one of the crucial problems when a learning-centered approach is employed. This paper proposes a new method to investigate the quality of a large …...

AbstractThe “Multilingual Emoji Prediction” task focuses on the ability of predicting the correspondent emoji for a certain tweet. In this paper, we investigate the relation between words and emojis. In …...

AbstractWith text lacking valuable information avail-able in other modalities, context may provide useful information to better detect emotions. In this paper, we do a systematic exploration of the role of …...

AbstractShort-text classification is a challenging task, due to the sparsity and high dimensionality of the feature space. In this study, we aim to analyze and classify Turkish tweets based on …...

In this paper, we conducted an aspect-based sentiment analysis using Google Maps user reviews of Indonesia’s tourism destinations which are Borobudur and Prambanan Temple. The aspects we used are Attractions, …...

AbstractThe classic deep learning paradigm learns a model from the training data of a single task and the learned model is also tested on the same task. This paper studies …...

AbstractOur approach is constructed to improve on a couple of aspects; preprocessing with an emphasis on humor sense detection, using embeddings from state-of-the-art language model(Elmo), and ensembling the results came …...

AbstractWeb-users produce and publish high volumes of data of various types, such as text, images, and videos. The platforms try to restrain their users from publishing offensive content to keep …...

AbstractOffensive language identification (OLI) in user generated text is automatic detection of any profanity, insult, obscenity, racism or vulgarity that is addressed towards an individual or a group. Due to …...

AbstractBuilding an interpretable AI diagnosis system for breast cancer is an important embodiment ofAI assisted medicine. Traditional breast cancer diagnosis methods based on machine learning areeasy to explain but the …...

AbstractThis paper describes the submission of the team Amrita_CEN to the shared task on iSarcasm Eval: Intended Sarcasm Detection in English and Arabic at SemEval 2022. We employed machine learning …...

AbstractAutomatic identification of cyberbullying from textual content is known to be a challenging task. The challenges arise from the inherent structure of cyberbullying and the lack of labeled large-scale corpus, …...

We present the results of a project perform- ing sentiment analysis on tweets from Ger- man politicians and party accounts for the 2021 German federal election. We collected over 58,000 …...