Rosetta Genomics Ltd.

International Business Machines Corporation

Ranit Aharonov

Amir Avniel

Yael Karov

Itzhak Bentwich

Noam Slonim

Ilya Shnayderman

Nitzan Rosenfeld

Shai Rosenwald

Alon Halfon

Artem Spector

Hila Benjamin

Lena Dankin

Liat Ein-Dor

David Konopnicki

Yosi Mass

Charles Arthur Jochim

Ella Rabinovich

Roy N Bar-Haim

Orith Toledo-Ronen

Benjamin Sznajder

Chen Yanover

Eyal Shnarch

Yaara Goldschmidt

Liat Ein Dor

Amir Menczel

Nir Dromi

Michal Ozery-Flato

Ariel Gera

Leshem Choshen

Michal Shmueli-scheuer

Elron Bandel

Issac Bentwich

Yonat Shemer-Avni

Asal Levy

Itzhak Benwich

Elad Venezian

Bentwich Itzhak

A computer-implemented method including: receiving, as input, a dataset comprising training pairs (s, t), wherein each training pair comprises (i) a source sentence s and (ii) a target paraphrase t of the source sentences; at a training stage, training a machine learning model on the dataset, to obtain a trained quality-controlled paraphrase generator model, wherein during the training stage, each of the training pairs is associated with a predicted control vector representing a predicted paraphrase quality of the source sentence in the training pair; and at an inference stage, inferencing the trained quality-controlled paraphrase generator model on an input sentence, wherein the input sentence is associated with an input quality control vector, to obtain an output paraphrase of the input sentence which conforms to the quality control vector.

Quality controlled paraphrase generation

An example system includes a processor to receive a list of sentiment carrying discourse markers. The processor is to select sentences in a text corpus that begin with a discourse marker from the list of sentiment carrying discourse markers followed by a comma. The processor is to remove each discourse marker and comma from a beginning of the selected sentences and labeling each of the sentences with a sentiment associated with to a corresponding removed discourse marker to generate a weakly labeled dataset. The processor is to inter-train a pretrained language model using the generated weakly labeled dataset to generate a sentiment model.

Generating sentiment models using weak labels generated from discourse markers

An example system includes a processor to pre-train a transformer-based language model on a general domain. The processor can inter-train the pre-trained transformer-based language model using partitioning and classification to generate an inter-trained transformer-based pre-trained language model. The processor can then fine-tune the inter-trained transformer-based pre-trained language model on a target task to generate a fine-tuned transformer-based language model.

Inter-training of pre-trained transformer-based language models using partitioning and classification

A method including: generating, from a text corpus, a lexicon of unigrams and bigrams comprising an embedding for each of said unigrams and bigrams; training a machine learning classifier on a training set comprising a subset of said lexicon, wherein each of said unigrams and bigrams in said subset has a sentiment label; applying said machine learning classifier to said lexicon, to (i) predict a sentiment of each of said unigrams and bigrams, and (ii) update said lexicon with the predicted sentiments; and performing statistical analysis on said updated lexicon, to extract one or more sentiment composition lexicons, wherein each of said one or more sentiment composition lexicons is associated with a sentiment composition class.

Learning sentiment composition from sentiment lexicons

Method and apparatus for training and using a classifier for words. Embodiments include receiving a first plurality of sentences comprising a first word that is associated with a class and a second plurality of sentences comprising a second word that is not associated with the class. Embodiments include training a classifier using positive training data for the class that is based on the first plurality of sentences and negative training data for the class that is based on the second plurality of sentences. Embodiments include determining a measure of correlation between a third word and the class by using a sentence comprising the third word as an input to the classifier. Embodiments include using the measure of correlation to perform an action selected from the following list: selecting content to provide to a user; determining an automatic chat response; or filtering a set of content.

Classification engine for learning properties of words and multi-word expressions

A method of estimating a thematic similarity of sentences, comprising receiving a corpus of a plurality of documents describing a plurality of topics where each document comprises a plurality of sentences arranged in a plurality of sections, constructing sentence triplets for at least some of the sentences, each sentence triplet comprising a respective sentence, a respective positive sentence selected randomly from the section comprising the respective sentence and a respective negative sentence selected randomly from another section, training a first neural network with the sentence triplets to identify sentence-sentence vectors mapping each sentence with a shorter distance to its respective positive sentence compared to the distance to its respective negative sentence and outputting the first neural network for estimating thematic similarity between a pair of sentences by computing a distance between the sentence-sentence vectors produced for each sentence of the pair by the first neural network.

Learning thematic similarity metric from article text units

There is provided, in accordance with some embodiments, a method for receiving electronic documents representing a dependency graph comprising feature extractors at each graph node and directed edges corresponding to computational dependencies of the feature extractors. For at least some feature extractors, extractor defining data, comprising extractor data and computational dependencies of the graph node in the dependency graph are determined, and a node lookup key based on the extractor defining data is computed. When the node lookup key is associated with a stored set of output feature values, the stored set is assigned as output values of the feature extractor. When node lookup key is not associated with a stored set of output feature values, a new set of output feature values is computed, stored, and associated the node lookup key. The one set of output feature values are sent as an output feature set.

Modular memoization, tracking and train-data management of feature extraction

Described herein are polynucleotides associated with prostate and lung cancer. The polynucleotides are miRNAs and miRNA precursors. Related methods and compositions that can be used for diagnosis, prognosis, and treatment of those medical conditions are disclosed. Also described herein are methods that can be used to identify modulators of prostate and lung cancer.

Nucleic acids related to MiR-29B-1-5P and uses thereof

Nucleic acid compositions related to MIR-138-1-3P and uses thereof

The present invention provides nucleic acid sequences that are used for identification, classification and diagnosis of specific types of cancers. The nucleic acid sequences can also be used for prognosis evaluation of a subject based on the expression pattern of a biological sample.

Diagnosis and prognosis of various types of cancers

HSA-MIR-146B-5P-related nucleic acids and uses thereof

Described herein are novel polynucleotides associated with prostate and lung cancer. The polynucleotides are miRNAs and miRNA precursors. Related methods and compositions that can be used for diagnosis, prognosis, and treatment of those medical conditions are disclosed. Also described herein are methods that can be used to identify modulators of prostate and lung cancer.

MicroRNAs and uses thereof

Described herein are novel polynucleotides associated with viral infections. The polynucleotides are miRNAs and miRNA precursors. Related methods and compositions that can be used for diagnosis, prognosis, and treatment of those medical conditions are disclosed. Also described herein are methods that can be used to identify modulators of viral infections.

Viral and viral associated miRNAs and uses thereof

The present invention provides a process for classification of cancers and tissues of origin through the analysis of the expression patterns of specific microRNAs and nucleic acid molecules relating thereto. Classification according to a microRNA tree-based expression framework allows optimization of treatment, and determination of specific therapy.

Gene expression signature for classification of tissue of origin of tumor samples

Growing community of inventors

Tel Aviv, Israel

Ranit Aharonov