Even though Automatic Speech Recognition (ASR) systems significantly improved over the last decade, they still introduce a lot of errors when they transcribe voice to text. One of the most common reasons for these errors is phonetic confusion between similar-sounding expressions. As a result, ASR transcriptions often contain "quasi-oronyms", i.e., words or phrases that sound similar to the source ones, but that have completely different semantics (e.g., "win" instead of "when" or "accessible on defecting" instead of "accessible and affecting"). These errors significantly affect the performance of downstream Natural Language Understanding (NLU) models (e.g., intent classification, slot filling, etc.) and impair user experience. To make NLU models more robust to such errors, we propose novel phonetic-aware text representations. Specifically, we represent ASR transcriptions at the phoneme level, aiming to capture pronunciation similarities, which are typically neglected in word-level representations (e.g., word embeddings). To train and evaluate our phoneme representations, we generate noisy ASR transcriptions of four existing datasets - Stanford Sentiment Treebank, SQuAD, TREC Question Classification and Subjectivity Analysis - and show that common neural network architectures exploiting the proposed phoneme representations can effectively handle noisy transcriptions and significantly outperform state-of-the-art baselines. Finally, we confirm these results by testing our models on real utterances spoken to the Alexa virtual assistant.

Named entities are frequently used in a metonymic manner.They serve as references to related entities such as people and organisations.Accurate identification and interpretation of metonymy can be directly beneficial to various NLP applications, such as Named Entity Recognition and Geographical Parsing.Until now, metonymy resolution (MR) methods mainly relied on parsers, taggers, dictionaries, external word lists and other handcrafted lexical resources.We show how a minimalist neural approach combined with a novel predicate window method can achieve competitive results on the Se-mEval 2007 task on Metonymy Resolution.Additionally, we contribute with a new Wikipedia-based MR dataset called RelocaR, which is tailored towards locations as well as improving previous deficiencies in annotation guidelines.

The workshop on Mining Online Health Reports (MOHRS) draws upon the rapidly developing field of Computational Health, focusing on textual content that has been generated through the various facets of Web activity. Online user-generated information mining, especially from social media platforms and search engines, has been in the forefront of many research efforts, especially in the fields of Information Retrieval and Natural Language Processing. The incorporation of such data and techniques in a number of health-oriented applications has provided strong evidence about the potential benefits, which include better population coverage, timeliness and the operational ability in places with less established health infrastructure. The workshop aims to create a platform where relevant state-of-the-art research is presented, but at the same time discussions among researchers with cross-disciplinary backgrounds can take place. It will focus on the characterisation of data sources, the essential methods for mining this textual information, as well as potential real-world applications and the arising ethical issues. MOHRS '17 will feature 3 keynote talks and 4 accepted paper presentations, together with a panel discussion session.

Automatically recognising medical concepts mentioned in social media messages (e.g.tweets) enables several applications for enhancing health quality of people in a community, e.g.real-time monitoring of infectious diseases in population.However, the discrepancy between the type of language used in social media and medical ontologies poses a major challenge.Existing studies deal with this challenge by employing techniques, such as lexical term matching and statistical machine translation.In this work, we handle the medical concept normalisation at the semantic level.We investigate the use of neural networks to learn the transition between layman's language used in social media messages and formal medical language used in the descriptions of medical concepts in a standard ontology.We evaluate our approaches using three different datasets, where social media texts are extracted from Twitter messages and blog posts.Our experimental results show that our proposed approaches significantly and consistently outperform existing effective baselines, which achieved state-of-the-art performance on several medical concept normalisation tasks, by up to 44%.

Dealing with the medical terminology is a challenge when searching for patients based on the relevance of their medical records towards a given query. Existing work used query expansion (QE) to extract expansion terms from different document collections to improve query representation. However, the usefulness of particular document collections for QE was not measured and taken into account during retrieval. In this work, we investigate two automatic approaches that measure and leverage the usefulness of document collections when exploiting multiple document collections to improve query representation. These two approaches are based on resource selection and learning to rank techniques, respectively. We evaluate our approaches using the TREC Medical Records track's test collection. Our results show the potential of the proposed approaches, since they can effectively exploit 14 different document collections, including both domain-specific (e.g. MEDLINE abstracts) and generic (e.g. blogs and webpages) collections, and significantly outperform existing effective baselines, including the best systems participating at the TREC Medical Records track. Our analysis shows that the different collections are not equally useful for QE, while our two approaches can automatically weight the usefulness of expansion terms extracted from different document collections effectively.

Previous studies have shown that health reports in social media, such as Dai-lyStrength and Twitter, have potential for monitoring health conditions (e.g.adverse drug reactions, infectious diseases) in particular communities.However, in order for a machine to understand and make inferences on these health conditions, the ability to recognise when laymen's terms refer to a particular medical concept (i.e.text normalisation) is required.To achieve this, we propose to adapt an existing phrase-based machine translation (MT) technique and a vector representation of words to map between a social media phrase and a medical concept.We evaluate our proposed approach using a collection of phrases from tweets related to adverse drug reactions.Our experimental results show that the combination of a phrase-based MT technique and the similarity between word vector representations outperforms the baselines that apply only either of them by up to 55%.

In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions ("Yes"--in favour of Independence vs. "No"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline.

In the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve the cohorts whose medical histories cover the inclusion criteria specified in a query, which are often complex and include multiple medical conditions. For example, a query may aim to find patients with both 'lupus nephritis' and 'thrombotic thrombocytopenic purpura'. In a typical best-match retrieval setting, any patient exhibiting all of the inclusion criteria should naturally be ranked higher than a patient that only exhibits a subset, or none, of the criteria. In this work, we extend the two main existing models for ranking patients to take into account the coverage of the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within the records of a particular patient, and thereby rank highly those patients whose medical records are likely to cover all of the specified criteria. In particular, our proposed approach estimates the relevance of a patient, based on the mixture of the probability that the patient is retrieved by a patient ranking model for a given query, and the likelihood that the patient's records cover the query criteria. The latter is measured using the relevance towards each of the criteria stated in the query, represented in the form of sub-queries. We thoroughly evaluate our proposed approach using the test collection provided by the TREC 2011 and 2012 Medical Records track. Our results show significant improvements over existing strong baselines.

Negated language is frequently used by medical practitioners to indicate that a patient does not have a given medical condition. Traditionally, information retrieval systems do not distinguish between the positive and negative contexts of terms when indexing documents. For example, when searching for patients with angina, a retrieval system might wrongly consider a patient with a medical record stating ``no evidence of angina" to be relevant. While it is possible to enhance a retrieval system by taking into account the context of terms within the indexing representation of a document, some non-relevant medical records can still be ranked highly, if they include some of the query terms with the intended context. In this paper, we propose a novel learning framework that effectively handles negated language. Based on features related to the positive and negative contexts of a term, the framework learns how to appropriately weight the occurrences of the opposite context of any query term, thus preventing documents that may not be relevant from being retrieved. We thoroughly evaluate our proposed framework using the TREC 2011 and 2012 Medical Records track test collections. Our results show significant improvements over existing strong baselines. In addition, in combination with a traditional query expansion and a conceptual representation approach, our proposed framework could achieve a retrieval effectiveness comparable to the performance of the best TREC 2011 and 2012 systems, while not addressing other challenges in medical records search, such as the exploitation of semantic relationships between medical terms.

Two main approaches have emerged in the literature for the effective deployment of a search system to rank patients having a medical history relevant to a query. The first approach is to directly rank patients based on the relevance of their medical history, represented as a concatenation of their associated medical records. Instead, the second approach initially retrieves the relevant medical records of patients, and then ranks the patients based on the relevance of their retrieved medical records. However, these two approaches may be useful for different queries. In this work, we propose a novel supervised approach that can effectively identify when to use either of the two aforementioned patient ranking approaches to attain effective retrieval performance. In particular, our approach deploys a classifier to learn to select a ranking approach when ranking patients, by using query difficulty measures, such as query performance predictors and the number of medical concepts detected in a query, as learning features. We thoroughly evaluate our approach using the standard test collections provided by the TREC Medical Records track. Our results show significant improvements over existing strong baselines.

Electronic medical records (EMRs) are being increasingly used worldwide to facilitate improved healthcare services [2,3]. They describe the clinical decision process relating to a patient, detailing the observed symptoms, the conducted diagnostic tests, the identified diagnoses and the prescribed treatments. However, medical records search is challenging, due to the implicit knowledge inherent within the medical records - such knowledge may be known by medical practitioners, but hidden to an information retrieval (IR) system [3]. For instance, the mention of a treatment such as a drug may indicate to a practitioner that a particular diagnosis has been made even if this was not explicitly mentioned in the patient's EMRs. Moreover, the fact that a symptom has not been observed by a clinician may rule out some specific diagnoses.

The complexity of medical terminology raises challenges when searching medical records. For example, 'cancer', 'tumour', and 'neoplasms', which are synonyms, may prevent a traditional search system from retrieving relevant records that contain only synonyms of the query terms. Prior works use bag-of-concepts approaches, to deal with this by representing medical terms sharing the same meanings using concepts from medical resources (e.g. MeSH). The relevance scores are then combined with a traditional bag-of-words representation, when inferring the relevance of medical records. Even though the existing approaches are effective, the predicted retrieval effectiveness of either the bag-of-words or bag-of-concepts representation, which may be used to effectively model the score combination and hence improve retrieval performance, is not taken into account. In this paper, we propose a novel learning framework that models the importance of the bag-of-words and the bag-of-concepts representations, combining their scores on a per-query basis. Our proposed framework leverages retrieval performance predictors, such as the clarity score and AvIDF, calculated on both representations as learning features. We evaluate our proposed framework using the TREC Medical Records track's test collections. As our proposed framework can significantly outperform an existing approach that linearly merges the relevance scores, we conclude that retrieval performance predictors can be effectively leveraged when combining the relevance scores.

In medical records, negative qualifiers, e.g. no or without, are commonly used by health practitioners to identify the absence of a medical condition. Without considering whether the term occurs in a negative or positive context, the sole presence of a query term in a medical record is insufficient to imply that the record is relevant to the query. In this paper, we show how to effectively handle such negation within a medical records information retrieval system. In particular, we propose a term representation that tackles negated language in medical records, which is further extended by considering the dependence of negated query terms. We evaluate our negation handling technique within the search task provided by the TREC Medical Records 2011 track. Our results, which show a significant improvement upon a system that does not consider negated context within records, attest the importance of handling negation.

Expanding a query with acronyms or their corresponding 'long-forms' has not been shown to provide consistent improvements in the biomedical IR literature. The major open issue with expanding acronyms in a query is their inherent ambiguity, as an acronym can refer to multiple long-forms. At the same time, a long-form identified in a query can be expanded with its acronym(s); however, some of these may be also ambiguous and lead to poor retrieval performance. In this work, we propose the use of the EMIM (Expected Mutual Information Measure) between a long-form and its abbreviated acronym to measure ambiguity. We experiment with expanding both acronyms and long-forms identified in the queries from the adhoc task of the TREC 2004 Genomics track. Our preliminary analysis shows the potential of both acronym and long-form expansions for biomedical IR.