Florida International University

Mark Finlayson

Naphtali David Rishe

Deya Banisakher

Mustafa Ocal

Joshua Daniel Eisenberg

Systems and methods are provided for enabling users to work with TimeML annotations and perform advanced temporal analysis, offering a comprehensive suite of features. Systems and methods of embodiments allow users to parse TimeML annotations, construct TimeML graphs, and execute the timeline extraction (TLEX) algorithm to effect complete timeline extraction. An algorithm can be incorporated for increasing connectivity in temporal graphs, which identifies graph disconnectivity and recommends links based on temporal reasoning, thereby enhancing the coherence of the graph representation. Also, a built-in validation algorithm can be included, ensuring compliance with TimeML annotation guidelines.

Systems and methods for performing temporal analysis

Devices and methods for learning and/or predicting the self-reported pain improvement levels of osteoarthritis (OA) patients are provided. A device or apparatus can include a processor and a machine-readable medium in operable communication with the processor and having stored thereon an algorithm and a unique set of features. The algorithm and set of features can enable building one or more models that learn the self-reported pain improvement levels of OA patients.

Systems and methods for predicting pain level

Systems and methods for discovering and/or determining section types for a given document class in a data-driven manner are provided. A modified Bayesian model merging algorithm can be used, along with extending an Analogical Story Merging (ASM) algorithm. The systems and methods can learn the section structure of documents without a pre-existing ontology of sections or time-intensive annotation efforts.

Systems and methods for determining document section types

Systems and methods for quantifying temporal indeterminacy of timelines are provided. Systems and methods can rely on solving temporal constraint problems to extract timelines and can calculate the temporal relation loss during timeline transformation and then identify the temporal indeterminate sections of extracted timelines from both timelines and temporal graphs to measure the total temporal information loss.

Systems and methods for evaluating temporal dependency trees

Systems and methods for automatically modeling the discourse structure of psychiatric reports and segmenting these reports into various sections are provided. The systems and methods can be based around a model that learns the section types, positions, and sequence and can automatically segment unlabeled text in a psychiatric report into the corresponding sections. Knowledge of the ordering of the sections can improve the performance of a section classifier and a text segmenter. A Hierarchical Hidden Markov Model (HHMM) can be trained and can categorize sections in psychiatric reports into a predefined section label.

Systems and methods for segmenting documents

Methods and devices for story detection in text are provided. A device can include an input device for receiving text data a processor configured to: tokenize each paragraph in the text data and split each tokenized paragraph into sentences; parse each sentence from the tokenized paragraphs; label each predicate in each sentence with its respective semantic role; and assign a verb class to each predicate; and determine whether respective arguments of each predicate contains a character. The device can further include a support vector machine configured to determine whether a story is present within each paragraph based upon whether each predicate contains a character.

Features for classification of stories

Methods for classifying a point of view and diegesis are provided. A method can include providing a processor in operable communication with a computer-readable medium, receiving a narrative text, extracting a set of features from the narrative text, transmitting the features into a feature vector, transmitting a plurality of feature vectors to a support vector machine, predicting a point of view and diegesis for the narrative text associate with a particular feature vector, and annotating the narrative text.

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North Bay Village, FL, United States of America

Mark Finlayson