TY - GEN
T1 - Semantic Label Representations with Lbl2Vec
T2 - 16th International Conference on Web Information Systems and Technologies, WEBIST 2020 and 17th International Conference on Web Information Systems and Technologies, WEBIST 2021
AU - Schopf, Tim
AU - Braun, Daniel
AU - Matthes, Florian
N1 - Publisher Copyright:
© 2023, Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In this paper, we evaluate the Lbl2Vec approach for unsupervised text document classification. Lbl2Vec requires only a small number of keywords describing the respective classes to create semantic label representations. For classification, Lbl2Vec uses cosine similarities between label and document representations, but no annotation information. We show that Lbl2Vec significantly outperforms common unsupervised text classification approaches and a widely used zero-shot text classification approach. Furthermore, we show that using more precise keywords can significantly improve the classification results of similarity-based text classification approaches.
AB - In this paper, we evaluate the Lbl2Vec approach for unsupervised text document classification. Lbl2Vec requires only a small number of keywords describing the respective classes to create semantic label representations. For classification, Lbl2Vec uses cosine similarities between label and document representations, but no annotation information. We show that Lbl2Vec significantly outperforms common unsupervised text classification approaches and a widely used zero-shot text classification approach. Furthermore, we show that using more precise keywords can significantly improve the classification results of similarity-based text classification approaches.
KW - Natural language processing
KW - Semantic label representations
KW - Text representations
KW - Text similarity
KW - Unsupervised text classification
UR - http://www.scopus.com/inward/record.url?scp=85148685635&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-24197-0_4
DO - 10.1007/978-3-031-24197-0_4
M3 - Conference contribution
AN - SCOPUS:85148685635
SN - 9783031241963
T3 - Lecture Notes in Business Information Processing
SP - 59
EP - 73
BT - Web Information Systems and Technologies - 16th International Conference, WEBIST 2020, and 17th International Conference, WEBIST 2021, Revised Selected Papers
A2 - Marchiori, Massimo
A2 - Domínguez Mayo, Francisco José
A2 - Filipe, Joaquim
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 October 2021 through 28 October 2021
ER -