a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. Two versions: (1) a fine-tuned model of HeBERT applied on legal and legislative documents, and (2) uses HeBERT's architecture guidlines to train a BERT model from scratch.

This code and dataset provide an established benchmark for neural sentiment analysis for Modern Hebrew.

a large pre-trained language model for Modern Hebrew, publicly available, pre-training on Oscar, Texts of Hebrew tweets, all of Hebrew Wikipedia, published by the OnlpLab team.

HeBERT is a Hebrew pretrained language model for Polarity Analysis and Emotion Recognition, published by Dr. Inbal Yahav Shenberger and Avichay Chriqui. It is based on Google's BERT architecture and it is BERT-Base config. HeBert was trained on three dataset: OSCAR, A Hebrew dump of Wikipedia, Emotion User Generated Content (UGC) data that was collected for the purpose of this study. The model was evaluated on downstream tasks: HebEMO - emotion recognition model, and sentiment analysis.

a Hebrew pre-trained language model, trained on the same dataset as the previous SOTA Hebrew PLM AlephBERT, consisting of approximately 2 billion words of text but with a substantially increased vocabulary of 128,000 word pieces. Published as a collaboration of the OnlpLab team and Dicta.

Easy-to-use Python interface for Hebrew clinical psychology text analysis. Useful for various psychology applications such as detecting emotional state, well being, relationship quality in conversation, identifying topics (e.g., family, work) and many more.

a pre-trained BERT model for modern Hebrew, with the masked-language-modeling objective.

A fine-tuned model for prefix segmentation task.

A fine-tuned model for morphological tagging task.

The package is based on Heinrich's java implementation of collapsed Gibbs sampling with an extra variable to model the generative nature of lemmas in Hebrew.

OnlpLab's code and models for neural modeling of Hebrew NER. Described in the TACL paper Neural Modeling for Named Entities and Morphology (NEMO2).

Yonatan Bitton's code that recognizes medical entities in a Hebrew text.

A custom spaCy pipeline for Hebrew text including a transformer-based multitask NER model that recognizes 16 entity types in Hebrew, including GPE, PER, LOC and ORG.

A de-identification toolkit for clinical text in Hebrew. Demo: https://hebsafeharbor-demo.azurewebsites.net/

A de-identification toolkit for clinical text in Hebrew. An improved version of Microsoft's HebSafeHarbor project.

A Python package for browsing and processing ancient corpora, focused on the Hebrew Bible Database.

Easy-to-use Python interface for accessing top-k word translations and for building a new bilingual lexicon from a custom parallel corpus.

The weights (e.g. a trained model) for a Hebrew version for Howard's and Ruder's ULMFiT model. Trained on the Hebrew Wikipedia corpus.

Sequence-to-sequence learning for Hebrew transliteration (converting between Hebrew text and Latin transliteration).

Converts YAP's output from the SPMRL scheme to UD v2.

Doron Adler's Hebrew text generation model based on EleutherAI's gpt-neo.

Analytical tools for Jewish texts. They also have a GitHub organization: https://github.com/Dicta-Israel-Center-for-Text-Analysis.

A Python library for looking up the frequencies of words in 44 languages, including Hebrew. The Hebrew data is based on Wikipedia, OPUS OpenSubtitles 2018 and SUBTLEX, Google Books Ngrams 2012, Web text from OSCAR and Twitter.

A commercial engine for search and entity tagging in Hebrew.

A text analysis and textual categorized entity extraction API for Hebrew, Arabic and Farsi texts.

Automatic analysis of free text in Hebrew.

Online text-to-speech service for Hebrew.

a WhatsApp bot that receives a voice note and transcribe it to text.

a WhatsApp bot that receives a voice note and transcribe it to text.

Transcription.

A tool for managing annotation projects. Handles right-to-left and part-of-word marking. Tutorial video: https://www.youtube.com/watch?v=eTlrTC_n_yg

A tool for linked data annotation.

an open source text annotation tool for humans. It provides annotation features for text classification, sequence labeling and sequence to sequence tasks. So, you can create labeled data for sentiment analysis, named entity recognition, text summarization and so on.

A Hebrew version of the Simlex-999 resource for the evaluation of models that learn the meaning of words and concepts.

A web-based tool for research and collaboration over text data. Handles right-to-left and part-of-word marking.

Web-based. Support RTL and project management.

OpenNLP has a tagging tool.

opeNER has a tagging tool.

A backend-independent client-side annotation framework.

A tool to edit training examples for rasa NLU. Handles right-to-left and part-of-word marking.

An online environment for collaborative text annotation. Does not support right-to-left.

A framework for crowdsourcing of data analysis and enrichment tasks.

A crowdsourced text annotator. Built with React and Redux (possibly also with pybossa).

System for HEBrew Text: ANnotations for Queries and Markup. SHEBANQ is an online environment for studying the Hebrew Bible.

The ONLP Lab team research Natural Language Processing (NLP) foundations, algorithms and applications, and develop morphological, syntactic and semantic models.

Prof. Reut Tsarfaty is Associate Professor at the Computer Science department at Bar-Ilan University, and the head of the ONLP Lab. She is interested in developing models for analyzing, understanding and generating utterances in natural language, and in applications of natural language processing to dowbstream applications such as natural language programming, natural language navigation, and the analysis and generation of user-content in social media. Her research is funded by an ISF grant (1739/26) and an ERC Starting Grant (677352).

Data Scientist - the ONLP Lab.

The founder of the Natural Language Processing (NLP) Lab at Bar-Ilan.

Professor at Bar Ilan University's Computer Science Department, and the Research Director of the Israeli branch of the Allen Institute for Artificial Intelligence.

Lecturer, Bar Ilan University, The Department of the Literature of the Jewish People

Prof. Moshe Koppel of the Department of Computer Science conducts research on a variety of machine learning applications including text categorization, image processing, speaker recognition and automated game playing.

An interdisciplinary center for research and for teaching in new media and related areas, such as big data, information science, network cultures and digital sociology.

Director of the Open Media and Information Lab (OMILab). Research interests and publications focus on various aspects of speech sciences, with expertise in speech prosody, acoustic phonetics, and speech communication and text analytics.

Interested in speech prosody analyses, combinatorial auctions and computer Networks (especially Ad-Hoc networks, mobile and cellular networks).

Research topics: EasyFirst Syntactic Dependency Parsing, Hebrew NLP, Medical NLP, Text Summarization, Natural Language Generation (NLG).

Professor at the Department of Computer Science, Ben-Gurion University of the Negev. His research interests are in Computational Linguistics, Natural Language Generation and Intelligent User Interfaces.

Teaching fellow and Researcher, Ben Gurion University, Israel. Area of interest: Computational Linguistics, Morphology, Hebrew.

Dr. Oren Tzur is an Assistant Professor (Senior Lecturer) at the department of Software & Information Systems Engineering (SISE) at Ben Gurion University, and the head of the NLP and Social dynamics lab (NAS-LAB).

Prof. Shuly Wintner is a professor of Computer Science at the University of Haifa, Israel. His main areas of interest are computational linguistics and natural language processing. Specific research topics include linguistic formalisms, formal grammar, computational morphology and syntax, processing of Hebrew, machine translation and computational approaches to language acquisition.

Working on Information Extraction and Semantics, as well as in other Natural Language Processing applications. I am also interested in Machine Learning - and the application of learning to NLP problems.

Prof. Jonathan Berant is Associate Professor at the Blavatnik School of Computer Science, Tel-Aviv University. He works on Natural Language Understanding problems such as Semantic Parsing, Question Answering, Paraphrasing, Reading Comprehension, and Textual Entailment.

Prof. Joseph (Yossi) Keshet is an Associate Professor at the Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering. He is the director of the Speech, Language, and Deep Learning Lab and affiliated with the Signal and Image processing Lab (SIPL).

Assistant Professor at the faculty of Computer Science. Focus: interpretability and robustness.

(retired)

An Assistant Professor at the faculty of Industrial Engineering and Management of the Technion. Working on Natural Language Processing (NLP). Interested in language learning in its context and design models that integrate domain and world knowledge with data-driven methods.

Feldman's main areas of research are natural language processing, entity extraction and text relations, text sentiment analysis, and language processing for algorithmic trading. He is one of the founder of the discipline of text mining.

With his main contribution in the area of Neuroscience, where he developed a comprehensive theory of the brain, Prof. Rappoport's Computer Science area of interest is language (Computational Linguistics, Natural Language Processing (NLP)), from cognitive science and machine learning perspectives.

His fields of interest are Computational Linguistics and Natural Language Processing. Specifically, I conduct research on semantic (meaning) representation from a computational perspective. My research is tightly linked to statistical learning, language technology (such as Machine Translation and Information Extraction), and computational modeling of child language acquisition.

Prof. Shahaf's research focuses on helping people make sense of the world. She designs algorithms that help people understand the underlying structure of complex topics, and connect the dots between different pieces. She also likes to formalize intuitive notions; see recent work on Computational Humor.

Dr. Yael Netzer has PhD in Computer Science and MA studies in Hebrew Literature at Ben Gurion University. She is a teaching fellow in Ben Gurion University and Haifa University, teaching Digital Humanities for Computer Science and for the Humanities, and in Tel Aviv University teaching Digital Humanities for archivists. She works at Dicta, the Israeli Center for Text Analysis. She is working as a DH consultant in the Digital Humanities lab in Haifa University. In recent years, Netzer develops and implements methods for digital personal archives, and is most interested in knowledge representation for archives, libraries and for the humanities.

Studies the neural bases of linguistic knowledge and processing.

Research fields: functional anatomy of language, linguistic theory (syntax, semantics), language acquisition, aphasia, individual variation.

146 Arabic children stories (MSA).

Open Source Arabic Corpora (MSA). 22,000 text documents, each belonging to 1 of 10 categories: Economics, History, Entertainments, etc.

The First Arabic COVID-19 Twitter Dataset with Propagation Networks. About 3.2M tweets in mixed dialect Arabic associated with COVID-19, an ongoing collection starting at January 2020.

A Corpus of Levantine Arabic Dialects. 117,805 natural sentences from conversations in various Levantine dialects: Jordania, Palestinian, Lebanese, Syrian

More than 5 million newspaper articles in MSA.

A Crawl for Today’s Arabic Web. 150M Arabic Web pages with high coverage of dialectal Arabic, Egyptian, Gulf, Levantine (~7M) and Maghrebi, as well as MSA, from a variety of sources - Wikipedia, Alexa, ArClueWeb09, and Twitter, etc.

A multilingual Parallel Arabic DIalectal Corpus. A parallel corpus of 6,400 sentences in multiple Arabic dialects: Algerian, Maghreb, Syrian, Palestinian and MSA, for dialect detection and machine translation.

Bilingual Corpus of Arabic-English Parallel Tweets. 166,000 bilingual tweets in Arabic and English (parallel) for machine translation tasks.

An Arabic Twitter Corpus for Crisis Events. A large-scale crisis-related multi-dialect Arabic Twitter corpus of 1,658,795 unique tweets from 22 emergency events. This corpus can be leveraged for several tasks, including crisis detection and crisis type classification.

a multi Dialect multi National Arabic Song Lyrics Corpus. More than 30,000 Arabic song lyrics in 6 Arabic dialects (Egyptian, Levantine, etc.) for singers from 18 different Arabic countries, segmented into sentences and words and labeled with song information.

Wikipedia, the free encyclopedia, publishes dumps of its content as XML files on a monthly basis.

QCRI Al Jazeera Speech Resource - The largest transcribed Arabic speech corpus with around 2,000 hours with multi-layer annotation, in multi-dialect and code-switching speech, crawled from the Al Jazeera news channel, for speech recognition, dialect identification, punctuation restoration, speaker identification, speaker linking, etc.

The DiaCorpus project is a collaboration between the Data Science Institute (DSI) and Israeli Innovation authority. The purpose of the project is to create a first of a kind Arabic textual repository, in a local dialect (Israeli / Palestinian). This project is part of the National Language Processing plan of Israel.

A Large Dataset of Dialectal Arabic Tweets. About 25K tweets that are annotated via crowdsourcing for 5 Arabic dialects: Egyptian, Maghrebi, Levantine, Gulf, and Iraqi.

10,000 tweets classified as objective, subjective positive, subjective negative, and subjective mixed.

Reviews with three classes from different services. The dataset combines reviews from hotels, books, movies, products and a few airlines. It has three classes (Mixed, Negative and Positive). Most were mapped from reviewers' ratings with 3 being mixed, above 3 positive and below 3 negative. Each row has a label and text separated by a tab (tsv). Text (reviews) were cleaned by removing Arabic diacritics and non-Arabic characters. The dataset has no duplicate reviews. The hotels and book reviews are a subset of HARD and BRAD. The rest were selected from hadyelsahar with a little over 100 airlines reviews collected manually.

Hotel Arabic-Reviews Dataset. This dataset contains 93700 hotel reviews in Arabic language. The hotel reviews were collected from Booking.com website during June/July 2016. The reviews are expressed in Modern Standard Arabic as well as dialectal Arabic.

Books Reviews in Arabic Dataset. This dataset contains 510,600 book reviews in Arabic language. The reviews were collected from GoodReads.com website during June/July 2016. This work is an extension of the early dataset of large-scale Arabic dataset, LABR, which has around 63K Arabic Book Reviews collected from GoodReads.com. The reviews are expressed mainly in Modern Standard Arabic but there are reviews in dialectal Arabic as well.

33K Automatically annotated Reviews in Domains of Movies, Hotels, Restaurants and Products

Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.