Search Results for author: Robert Vacareanu

Found 8 papers, 1 papers with code

PatternRank: Jointly Ranking Patterns and Extractions for Relation Extraction Using Graph-Based Algorithms

no code implementations PANDL (COLING) 2022 Robert Vacareanu, Dane Bell, Mihai Surdeanu

In this paper we revisit the direction of using lexico-syntactic patterns for relation extraction instead of today’s ubiquitous neural classifiers.

Relation Extraction

A Human-machine Interface for Few-shot Rule Synthesis for Information Extraction

no code implementations NAACL (ACL) 2022 Robert Vacareanu, George C.G. Barbosa, Enrique Noriega-Atala, Gus Hahn-Powell, Rebecca Sharp, Marco A. Valenzuela-Escárcega, Mihai Surdeanu

We propose a system that assists a user in constructing transparent information extraction models, consisting of patterns (or rules) written in a declarative language, through program synthesis. Users of our system can specify their requirements through the use of examples, which are collected with a search interface. The rule-synthesis system proposes rule candidates and the results of applying them on a textual corpus; the user has the option to accept the candidate, request another option, or adjust the examples provided to the system. Through an interactive evaluation, we show that our approach generates high-precision rules even in a 1-shot setting.

Relation Extraction

Neural-Guided Program Synthesis of Information Extraction Rules Using Self-Supervision

no code implementations PANDL (COLING) 2022 Enrique Noriega-Atala, Robert Vacareanu, Gus Hahn-Powell, Marco A. Valenzuela-Escárcega

We propose a neural-based approach for rule synthesis designed to help bridge the gap between the interpretability, precision and maintainability exhibited by rule-based information extraction systems with the scalability and convenience of statistical information extraction systems.

Language Modelling Program Synthesis

A Weak Supervision Approach for Few-Shot Aspect Based Sentiment

no code implementations19 May 2023 Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan

We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks.

Aspect Extraction Aspect Sentiment Triplet Extraction +2

Instruction Tuning for Few-Shot Aspect-Based Sentiment Analysis

no code implementations12 Oct 2022 Siddharth Varia, Shuai Wang, Kishaloy Halder, Robert Vacareanu, Miguel Ballesteros, Yassine Benajiba, Neha Anna John, Rishita Anubhai, Smaranda Muresan, Dan Roth

Aspect-based Sentiment Analysis (ABSA) is a fine-grained sentiment analysis task which involves four elements from user-generated texts: aspect term, aspect category, opinion term, and sentiment polarity.

Aspect-Based Sentiment Analysis (ABSA) Few-Shot Learning +1

An Unsupervised Method for Learning Representations of Multi-word Expressions for Semantic Classification

no code implementations COLING 2020 Robert Vacareanu, Marco A. Valenzuela-Esc{\'a}rcega, Rebecca Sharp, Mihai Surdeanu

This paper explores an unsupervised approach to learning a compositional representation function for multi-word expressions (MWEs), and evaluates it on the Tratz dataset, which associates two-word expressions with the semantic relation between the compound constituents (e. g. the label employer is associated with the noun compound government agency) (Tratz, 2011).

Parsing as Tagging

no code implementations LREC 2020 Robert Vacareanu, George Caique Gouveia Barbosa, Marco A. Valenzuela-Esc{\'a}rcega, Mihai Surdeanu

For example, for the sentence John eats cake, the tag to be predicted for the token cake is -1 because its head (eats) occurs one token to the left.

Dependency Parsing TAG

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