{'fraction_non_alphanumeric': 0.023890784982935155, 'fraction_numerical': 0.006825938566552901, 'mean_word_length': 5.311827956989247, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Towards Useful Word Embeddings

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.