1 |
Persian Sentence-level Sentiment Polarity Classification
|
|
|
|
In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03258138 ; ICOTEN, Jun 2021, Glasgow, United Kingdom (2021)
|
|
BASE
|
|
Show details
|
|
2 |
Persian Sentence-level Sentiment Polarity Classification
|
|
|
|
In: ICOTEN ; https://hal.archives-ouvertes.fr/hal-03241928 ; ICOTEN, May 2021, Glasgow, France (2021)
|
|
BASE
|
|
Show details
|
|
3 |
Extending persian sentiment lexicon with idiomatic expressions for sentiment analysis
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
|
|
|
|
BASE
|
|
Show details
|
|
5 |
A novel context-aware multimodal framework for persian sentiment analysis
|
|
|
|
BASE
|
|
Show details
|
|
6 |
A semi-supervised approach for sentiment analysis of arab (ic+ izi) messages: Application to the algerian dialect
|
|
|
|
BASE
|
|
Show details
|
|
7 |
A Semi-supervised Approach for Sentiment Analysis of Arab(ic+izi) Messages: Application to the Algerian Dialect
|
|
|
|
BASE
|
|
Show details
|
|
8 |
A Novel Context-Aware Multimodal Framework for Persian Sentiment Analysis
|
|
|
|
BASE
|
|
Show details
|
|
9 |
Novel Deep Convolutional Neural Network-Based Contextual Recognition of Arabic Handwritten Scripts
|
|
|
|
BASE
|
|
Show details
|
|
11 |
CochleaNet: A robust language-independent audio-visual model for real-time speech enhancement
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Offline Arabic Handwriting Recognition Using Deep Machine Learning: A Review of Recent Advances
|
|
|
|
BASE
|
|
Show details
|
|
13 |
Deep Neural Network Driven Binaural Audio Visual Speech Separation
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Visual Speech In Real Noisy Environments (VISION): A Novel Benchmark Dataset and Deep Learning-Based Baseline System
|
|
|
|
BASE
|
|
Show details
|
|
15 |
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
|
|
|
|
BASE
|
|
Show details
|
|
16 |
A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks ...
|
|
|
|
BASE
|
|
Show details
|
|
17 |
Lip-reading driven deep learning approach for speech enhancement
|
|
|
|
In: abs/1808.00046 ; 1 ; 10 (2019)
|
|
BASE
|
|
Show details
|
|
18 |
A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Persian Named Entity Recognition
|
|
Dashtipour, Kia; Gogate, Mandar; Adeel, Ahsan; Algarafi, Abdulrahman; Howard, Newton; Hussain, Amir. - : Institute of Electrical and Electronics Engineers Inc, 2017. : Piscataway, NJ, USA, 2017
|
|
Abstract:
Named Entity Recognition (NER) is an important natural language processing (NLP) tool for information extraction and retrieval from unstructured texts such as newspapers, blogs and emails. NER involves processing unstructured text for classification of words or expressions into relevant categories. In literature, NER has been developed for various languages but limited work has been conducted to develop NER for Persian text. This is due to limited resources (such as corpus, lexicons etc.) and tools for Persian named entities. In this paper, a novel scalable system for Persian Named Entity Recognition (PNER) is presented. The proposed PNER can recognize and extract three most important named entities in Persian script: the person name, location and date. The proposed PNER has been developed by combining a grammatical rule-based approach with machine learning. The proposed framework has integrated dictionaries of Persian named entities, Persian grammar rules and a Support Vector Machine (SVM). The performance evaluation of PNER in terms of precision, recall and f-measure has achieved comparable results with the state-of-the-art NER frameworks in other languages.
|
|
Keyword:
Dictionaries; Grammar; Handheld computers; Natural language processing; Support vector machines; Text recognition; Tools
|
|
URL: http://hdl.handle.net/1893/26709 http://dspace.stir.ac.uk/bitstream/1893/26709/1/08109733.pdf https://doi.org/10.1109/ICCI-CC.2017.8109733
|
|
BASE
|
|
Hide details
|
|
|
|