L2F/INESC-ID at SemEval-2017 Tasks 1 and 2: Lexical and semantic features in word and textual similarity
By Pedro Fialho, Hugo Rodrigues, Luísa Coheur, Paulo Quaresma. 2017
4 citations recorded by the source corpus.
This paper describes our approach to the SemEval-2017 “Semantic Textual Similarity” and “Multilingual Word Similarity” tasks. In the former, we test our approach in both English and Spanish, and use a linguistically-rich set of features. These move from lexical to semantic features. In particular, we try to take advantage of the recent Abstract Meaning Representation and SMATCH measure. Although without state of the art results, we introduce semantic structures in textual similarity and analyze their impact. Regarding word similarity, we target the English language and combine WordNet information with Word Embeddings. Without matching the best systems, our approach proved to be simple and effective.
Subfields: Artificial Intelligence, Molecular Biology, Natural Language Processing, Computational Linguistics & Semantics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- José Camacho-Collados
- Mohammad Taher Pilehvar
- Nigel Collier
- Roberto Navigli
- Alessandro Raganato
- Hinrich Schütze
- Chris Biemann
- Mirella Lapata
In Molecular Biology
In Natural Language Processing
- José Camacho-Collados
- Mohammad Taher Pilehvar
- Roberto Navigli
- Nigel Collier
- Alessandro Raganato
- Hinrich Schütze
- Vijay Mago
- Dhivya Chandrasekaran
In Computational Linguistics & Semantics
- José Camacho-Collados
- Mohammad Taher Pilehvar
- Roberto Navigli
- Lasha Abzianidze
- Mirella Lapata
- Alessandro Raganato
Citations in the corpus
- SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity (2017, background): “…about the individual systems: OoO (Xu et al., 2017), HCCL (He et al., 2017), Citius (Gamallo, 2017), jmp8 (Melka and Bernard, 2017), l2f (Fialho et al., 2017), QLUT (Meng et al., 2017), RUFINO (Jimenez et al., 2017), MERALI (Mensa et al., 2017), Luminoso (Speer and Lowry-Duda, 2017),…”