Supporting text mining for e-Science: the challenges for Grid-enabled natural language processing

Over the last few years, language technology has moved rapidly from 'applied research' to 'engineering', and from small-scale to large-scale engineering. Applications such as advanced text mining systems are feasible, but very resource-intensive, while research seeking to address the underlying language processing questions faces very real practical and methodological limitations. The e-Science vision, and the creation of the e-Science Grid, promises the level of integrated large-scale technological support required to sustain this important and successful new technology area. In this paper, we discuss the foundations for the deployment of text mining and other language technology on the Grid - the protocols and tools required to build distributed large-scale language technology systems, meeting the needs of users, application builders and researchers

Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks

Selecting optimal parameters for a neural network architecture can often make the difference between mediocre and state-of-the-art performance. However, little is published which parameters and design choices should be evaluated or selected making the correct hyperparameter optimization often a "black art that requires expert experiences" (Snoek et al., 2012). In this paper, we evaluate the importance of different network design choices and hyperparameters for five common linguistic sequence tagging tasks (POS, Chunking, NER, Entity Recognition, and Event Detection). We evaluated over 50.000 different setups and found, that some parameters, like the pre-trained word embeddings or the last layer of the network, have a large impact on the performance, while other parameters, for example the number of LSTM layers or the number of recurrent units, are of minor importance. We give a recommendation on a configuration that performs well among different tasks.Comment: 34 pages. 9 page version of this paper published at EMNLP 201

2kenize: Tying Subword Sequences for Chinese Script Conversion

Simplified Chinese to Traditional Chinese character conversion is a common preprocessing step in Chinese NLP. Despite this, current approaches have poor performance because they do not take into account that a simplified Chinese character can correspond to multiple traditional characters. Here, we propose a model that can disambiguate between mappings and convert between the two scripts. The model is based on subword segmentation, two language models, as well as a method for mapping between subword sequences. We further construct benchmark datasets for topic classification and script conversion. Our proposed method outperforms previous Chinese Character conversion approaches by 6 points in accuracy. These results are further confirmed in a downstream application, where 2kenize is used to convert pretraining dataset for topic classification. An error analysis reveals that our method's particular strengths are in dealing with code-mixing and named entities.Comment: Accepted to ACL 202