Арабский набор данных
Related Papers
Memory-Based Learning of Morphology with Stochastic Transducers
This paper discusses the supervised learn- ing of morphology using stochastic trans- ducers, trained using the Expectation- Maximization (EM) algorithm. Two ap- proaches are presented: first, using the transducers directly …...
Unsupervised Bilingual Morpheme Segmentation and Alignment with Context-rich Hidden Semi-Markov Models
This paper describes an unsupervised dynamic graphical model for morphological segmen- tation and bilingual morpheme alignment for statistical machine translation. The model ex- tends Hidden Semi-Markov chain models by using …...
Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
AbstractPrepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and …...
Linking Databases using Matched Arabic Names
In this paper, a new hybrid algorithm that combines both token-based and character-based approaches is presented. The basic Levenshtein approach also has been extended to the token-based distance metric. The …...
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities
AbstractIn this paper we present our contribution to SemEval-2018, a classifier for classifying multi-label emotions of Arabic and English tweets. We attempted “Affect in Tweets”, specifically Task E-c: Detecting Emotions …...
Multilingual Multi-class Sentiment Classification Using Convolutional Neural Networks
This paper describes a language-independent model for multi-class sentiment analysis using a simple neural network architecture of five layers (Embedding, Conv1D, GlobalMaxPooling and two Fully-Connected). The advantage of the proposed …...
AlexU-AL at SemEval-2022 Task 6: Detecting Sarcasm in Arabic Text Using Deep Learning Techniques
AbstractSarcasm detection is an important task in Natural Language Understanding. Sarcasm is a form of verbal irony that occurs when there is a discrepancy between the literal and intended meanings …...
JCT at SemEval-2022 Task 6-A: Sarcasm Detection in Tweets Written in English and Arabic using Preprocessing Methods and Word N-grams
AbstractIn this paper, we describe our submissions to SemEval-2022 contest. We tackled subtask 6-A - “iSarcasmEval: Intended Sarcasm Detection In English and Arabic – Binary Classification”. We developed different models …...
MILIE: Modular & Iterative Multilingual Open Information Extraction
AbstractOpen Information Extraction (OpenIE) is the task of extracting (subject, predicate, object) triples from natural language sentences. Current OpenIE systems extract all triple slots independently. In contrast, we explore the …...
UoR-NCL at SemEval-2022 Task 6: Using ensemble loss with BERT for intended sarcasm detection
AbstractSarcasm has gained notoriety for being difficult to detect by machine learning systems due to its figurative nature. In this paper, Bidirectional Encoder Representations from Transformers (BERT) model has been …...