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.
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.
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.
2 code implementations • 11 Apr 2024 • Robert Vacareanu, Vlad-Andrei Negru, Vasile Suciu, Mihai Surdeanu
We analyze how well pre-trained large language models (e. g., Llama2, GPT-4, Claude 3, etc) can do linear and non-linear regression when given in-context examples, without any additional training or gradient updates.
no code implementations • 5 Apr 2024 • Fahmida Alam, Md Asiful Islam, Robert Vacareanu, Mihai Surdeanu
We introduce a meta dataset for few-shot relation extraction, which includes two datasets derived from existing supervised relation extraction datasets NYT29 (Takanobu et al., 2019; Nayak and Ng, 2020) and WIKIDATA (Sorokin and Gurevych, 2017) as well as a few-shot form of the TACRED dataset (Sabo et al., 2021).
no code implementations • 5 Mar 2024 • Robert Vacareanu, Fahmida Alam, Md Asiful Islam, Haris Riaz, Mihai Surdeanu
Human interventions to the rules for the TACRED relation \texttt{org:parents} boost the performance on that relation by as much as 26\% relative improvement, without negatively impacting the other relations, and without retraining the semantic matching component.
1 code implementation • 11 Jul 2023 • Sushma Anand Akoju, Robert Vacareanu, Haris Riaz, Eduardo Blanco, Mihai Surdeanu
To this end, we modify the original texts using a set of phrases - modifiers that correspond to universal quantifiers, existential quantifiers, negation, and other concept modifiers in Natural Logic (NL) (MacCartney, 2009).
no code implementations • 19 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.
1 code implementation • 12 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 Aspect-Based Sentiment Analysis (ABSA) +2
1 code implementation • LREC 2022 • Robert Vacareanu, Marco A. Valenzuela-Escarcega, George C. G. Barbosa, Rebecca Sharp, Mihai Surdeanu
While deep learning approaches to information extraction have had many successes, they can be difficult to augment or maintain as needs shift.
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).
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.