Towards Useful Word Embeddings: Evaluation on Information Retrieval, Text Classification, and Language Modeling
|Druh||Článek ve sborníku|
|Konference||Proceedings of the Fourteenth Workshop on Recent Advances in Slavonic Natural Language Processing, RASLAN 2020|
|Fakulta / Pracoviště MU|
|Klíčová slova||Evaluation; word vectors; word2vec; fastText; information retrieval; text classification; language modeling|
Since the seminal work of Mikolov et al. (2013), word vectors of log-bilinear models have found their way into many NLP applications and were extended with the positional model.
Although the positional model improves accuracy on the intrinsic English word analogy task, prior work has neglected its evaluation on extrinsic end tasks, which correspond to real-world NLP applications.
In this paper, we describe our first steps in evaluating positional weighting on the information retrieval, text classification, and language modeling extrinsic end tasks.