The authors tackle the problem of domain adaptation for NER, where the label set of the target domain is different from the source domain.

They first train a CRF model on the source domain. Next, they train a LR classifier to predict labels in the target domain, based on predicted label scores from the model. Finally, the weights from the classifier are used to initialise another CRF model, which is then fine-tuned on the target domain data.

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The authors compare different image recognition models and image data sources for multimodal word representation learning.

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Image recognition models used for vector generation

Experiments are performed on SimLex-999 (similarity) and MEN (relatedness). The performance of different models (AlexNet, GoogLeNet, VGGNet) is found to be quite similar, with VGGNet performing slightly better at the cost of requiring more computation. Using search engines for image sources gives good coverage; ImageNet performs quite well with VGGNet; ESP Game dataset gave the lowest performance. Combining visual and linguistic vectors was found to be beneficial on both English and Italian.

They train word2vec skip-gram embeddings using coordinations as context. They use 11 manual patterns to extract coordinations (eg “X and Y”, “either X or Y”, etc). From “boats or planes”, “boats” will be a context of “planes” and “planes” will be a context of “boats”.

They evaluate on SimLex-999 and find that this performs badly on nouns. However, it beats normal skip-gram and dependency-based skip-gram on verbs and adjectives.

The authors have a dataset of 780 electronic health records and they use it to detect various medical events such as adverse drug events, drug dosage, etc. The task is done by assigning a label to each word in the document.

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Annotation statistics for the corpus of health records.

They look at CRFs, LSTMs and GRUs. Both LSTMs and GRUs outperform the CRF, but the best performance is achieved by a GRU trained on whole documents.

They describe a method for augmenting existing word embeddings with knowledge of semantic constraints. The idea is similar to retrofitting by Faruqui et al. (2015), but using additional constraints and a different optimisation function.

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Existing word vectors are further optimised to 1) have high similarity for known synonyms, 2) have low similarity for known antonyms, and 3) have high similarity to words that were highly similar in the original space. They evaluate on SimLex-999, showing state-of-the-art performance. Also, they use the method to improve a dialogue tracking system.