Papers by Maciej Piasecki

5 papers
Brand-Product Relation Extraction Using Heterogeneous Vector Space Representations (2020.lrec-1)

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Challenge: a study of the performance of NLP in relation extraction focuses on a business sector . a morphological dictionary can be used to extract named-entity pairs .
Approach: They propose to use annotated textual corpora to perform Brand-Product relation extraction . they propose to propose query expansion by morpho-syntactically related words .
Outcome: The proposed method improves the performance of the Brand-Product relation extraction task.
Classifier-based Polarity Propagation in a WordNet (L18-1)

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Challenge: a wordnet-based sentiment lexicon can be built to express sentiment polarity in a way shared across domains.
Approach: They propose a method to build a sense-level sentiment lexicon on the basis of a wordnet . they use a rich set of wordnet-based features to recognize and assign sentiment polarity values .
Outcome: The proposed method allows for the construction of a more reliable sentiment lexicon . the proposed method is partially automated, but it's performance drops in cross-domain applications .
BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language (2024.lrec-main)

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Challenge: Existing multilingual evaluation benchmarks focus on IR in the Polish language, but the Polish is a relatively new field due to the limited availability of Polish datasets.
Approach: They propose to establish large-scale resources for IR in the Polish language and translate them into a new benchmark which includes 13 datasets.
Outcome: The proposed benchmarks are based on 13 open IR datasets in Polish and are a pioneering development in this area.
Deep Neural Representations for Multiword Expressions Detection (2022.acl-srw)

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Challenge: Existing methods for multiword expression detection are based on sequence labeling and statistical measures.
Approach: They propose a weakly supervised method for multiword expressions extraction . they use a lexicon of English multiword lexical units as a reference knowledge base .
Outcome: The proposed method can be easily applied to other languages.
Developing PUGG for Polish: A Modern Approach to KBQA, MRC, and IR Dataset Construction (2024.findings-acl)

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Challenge: Existing KBQA datasets are outdated and inefficient in human labor, and assisting tools like Large Language Models (LLM) are not utilized to reduce the workload.
Approach: They propose a semi-automated question answering task that uses structured knowledge graphs to answer extensive knowledge-intensive questions.
Outcome: The proposed approach includes KBQA, MRC, and Information Retrieval tasks for low-resource languages.

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