Clinical Natural Language Processing in languages other than English: opportunities and challenges

Background: Natural language processing applied to clinical text or aimed at a clinical outcome has been thriving in recent years. This paper offers the first broad overview of clinical Natural Language Processing (NLP) for languages other than English. Recent studies are summarized to offer insights and outline opportunities in this area. Main Body We envision three groups of intended readers: (1) NLP researchers leveraging experience gained in other languages, (2) NLP researchers faced with establishing clinical text processing in a language other than English, and (3) clinical informatics researchers and practitioners looking for resources in their languages in order to apply NLP techniques and tools to clinical practice and/or investigation. We review work in clinical NLP in languages other than English. We classify these studies into three groups: (i) studies describing the development of new NLP systems or components de novo, (ii) studies describing the adaptation of NLP architectures developed for English to another language, and (iii) studies focusing on a particular clinical application. Conclusion: We show the advantages and drawbacks of each method, and highlight the appropriate application context. Finally, we identify major challenges and opportunities that will affect the impact of NLP on clinical practice and public health studies in a context that encompasses English as well as other languages

Deaf children need language, not (just) speech

Deaf and Hard of Hearing (DHH) children need to master at least one language (spoken or signed) to reach their full potential. Providing access to a natural sign language supports this goal. Despite evidence that natural sign languages are beneficial to DHH children, many researchers and practitioners advise families to focus exclusively on spoken language. We critique the Pediatrics article ‘Early Sign Language Exposure and Cochlear Implants’ (Geers et al., 2017) as an example of research that makes unsupported claims against the inclusion of natural sign languages. We refute claims that (1) there are harmful effects of sign language and (2) that listening and spoken language are necessary for optimal development of deaf children. While practical challenges remain (and are discussed) for providing a sign language-rich environment, research evidence suggests that such challenges are worth tackling in light of natural sign languages providing a host of benefits for DHH children – especially in the prevention and reduction of language deprivation.Accepted manuscrip

Spanish named entity recognition in the biomedical domain

Named Entity Recognition in the clinical domain and in languages different from English has the difficulty of the absence of complete dictionaries, the informality of texts, the polysemy of terms, the lack of accordance in the boundaries of an entity, the scarcity of corpora and of other resources available. We present a Named Entity Recognition method for poorly resourced languages. The method was tested with Spanish radiology reports and compared with a conditional random fields system.Peer ReviewedPostprint (author's final draft

Hypothesis Testing based Intrinsic Evaluation of Word Embeddings

We introduce the cross-match test - an exact, distribution free, high-dimensional hypothesis test as an intrinsic evaluation metric for word embeddings. We show that cross-match is an effective means of measuring distributional similarity between different vector representations and of evaluating the statistical significance of different vector embedding models. Additionally, we find that cross-match can be used to provide a quantitative measure of linguistic similarity for selecting bridge languages for machine translation. We demonstrate that the results of the hypothesis test align with our expectations and note that the framework of two sample hypothesis testing is not limited to word embeddings and can be extended to all vector representations.Comment: Accepted to RepEval 2017: The Second Workshop on Evaluating Vector Space Representations for NL