This paper describes experiments in which I tried to distinguish between Flemish and Netherlandic Dutch subtitles, as originally proposed in the VarDial 2018 Dutch–Flemish Subtitle task. However, rather than using all data as a monolithic block, I divided them into two non-overlapping domains and then investigated how the relation between training and test domains influences the recognition quality. I show that the best estimate of the level of recognizability of the language varieties is derived when training on one domain and testing on another. Apart from the quantitative results, I also present a qualitative analysis, by investigating in detail the most distinguishing features in the various scenarios. Here too, it is with the out-of-domain recognition that some genuine differences between Flemish and Netherlandic Dutch can be found.

Talking about odors and flavors is difficult for most people, yet experts appear to be able to convey critical information about wines in their reviews. This seems to be a contradiction, and wine expert descriptions are frequently received with criticism. Here, we propose a method for probing the language of wine reviews, and thus offer a means to enhance current vocabularies, and as a by-product question the general assumption that wine reviews are gibberish. By means of two different quantitative analyses—support vector machines for classification and Termhood analysis—on a corpus of online wine reviews, we tested whether wine reviews are written in a consistent manner, and thus may be considered informative; and whether reviews feature domain-specific language. First, a classification paradigm was trained on wine reviews from one set of authors for which the color, grape variety, and origin of a wine were known, and subsequently tested on data from a new author. This analysis revealed that, regardless of individual differences in vocabulary preferences, color and grape variety were predicted with high accuracy. Second, using Termhood as a measure of how words are used in wine reviews in a domain-specific manner compared to other genres in English, a list of 146 wine-specific terms was uncovered. These words were compared to existing lists of wine vocabulary that are currently used to train experts. Some overlap was observed, but there were also gaps revealed in the extant lists, suggesting these lists could be improved by our automatic analysis.

Entities play an essential role in understanding textual documents, regardless of whether the documents are short, such as tweets, or long, such as news articles. In short textual documents, all entities mentioned are usually considered equally important because of the limited amount of information. In long textual documents, however, not all entities are equally important: some are salient and others are not. Traditional entity topic models (ETMs) focus on ways to incorporate entity information into topic models to better explain the generative process of documents. However, entities are usually treated equally, without considering whether they are salient or not. In this work, we propose a novel ETM, Salient Entity Topic Model, to take salient entities into consideration in the document generation process. In particular, we model salient entities as a source of topics used to generate words in documents, in addition to the topic distribution of documents used in traditional topic models. Qualitative and quantitative analysis is performed on the proposed model. Application to entity salience detection demonstrates the effectiveness of our model compared to the state-of-the-art topic model baselines.

Anomaly detection can be seen as an unsupervised learning task in which a predictive model created on historical data is used to detect outlying instances in new data. This work addresses possibly promising but relatively uncommon application of anomaly detection to text data. Two English-language and one Polish-language Internet discussion forums devoted to psychoactive substances received from home-grown plants, such as hashish or marijuana, serve as text sources that are both realistic and possibly interesting on their own, due to potential associations with drug-related crime. The utility of two different vector text representations is examined: the simple bag of words representation and a more refined Global Vectors (GloVe) representation, which is an example of the increasingly popular word embedding approach. They are both combined with two unsupervised anomaly detection methods, based on one-class support vector machines (SVM) and based on dissimilarity to k-medoids clusters. The GloVe representation is found definitely more useful for anomaly detection, permitting better detection quality and ameliorating the curse of dimensionality issues with text clustering. The cluster dissimilarity approach combined with this representation outperforms one-class SVM with respect to detection quality and appears a more promising approach to anomaly detection in text data.

Benchmarks can be a useful step toward the goals of the field (when the benchmark is on the critical path), as demonstrated by the GLUE benchmark, and deep nets such as BERT and ERNIE. The case for other benchmarks such as MUSE and WN18RR is less well established. Hopefully, these benchmarks are on a critical path toward progress on bilingual lexicon induction (BLI) and knowledge graph completion (KGC). Many KGC algorithms have been proposed such as Trans[DEHRM], but it remains to be seen how this work improves WordNet coverage. Given how much work is based on these benchmarks, the literature should have more to say than it does about the connection between benchmarks and goals. Is optimizing P@10 on WN18RR likely to produce more complete knowledge graphs? Is MUSE likely to improve Machine Translation?