45,698 research outputs found
AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
Patients increasingly turn to search engines and online content before, or in
place of, talking with a health professional. Low quality health information,
which is common on the internet, presents risks to the patient in the form of
misinformation and a possibly poorer relationship with their physician. To
address this, the DISCERN criteria (developed at University of Oxford) are used
to evaluate the quality of online health information. However, patients are
unlikely to take the time to apply these criteria to the health websites they
visit. We built an automated implementation of the DISCERN instrument (Brief
version) using machine learning models. We compared the performance of a
traditional model (Random Forest) with that of a hierarchical encoder
attention-based neural network (HEA) model using two language embeddings, BERT
and BioBERT. The HEA BERT and BioBERT models achieved average F1-macro scores
across all criteria of 0.75 and 0.74, respectively, outperforming the Random
Forest model (average F1-macro = 0.69). Overall, the neural network based
models achieved 81% and 86% average accuracy at 100% and 80% coverage,
respectively, compared to 94% manual rating accuracy. The attention mechanism
implemented in the HEA architectures not only provided 'model explainability'
by identifying reasonable supporting sentences for the documents fulfilling the
Brief DISCERN criteria, but also boosted F1 performance by 0.05 compared to the
same architecture without an attention mechanism. Our research suggests that it
is feasible to automate online health information quality assessment, which is
an important step towards empowering patients to become informed partners in
the healthcare process
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
Translation Alignment and Extraction Within a Lexica-Centered Iterative Workflow
This thesis addresses two closely related problems. The first, translation alignment, consists of identifying bilingual document pairs that are translations of each other within
multilingual document collections (document alignment); identifying sentences, titles,
etc, that are translations of each other within bilingual document pairs (sentence alignment); and identifying corresponding word and phrase translations within bilingual
sentence pairs (phrase alignment). The second is extraction of bilingual pairs of equivalent word and multi-word expressions, which we call translation equivalents (TEs), from sentence- and phrase-aligned parallel corpora.
While these same problems have been investigated by other authors, their focus has
been on fully unsupervised methods based mostly or exclusively on parallel corpora.
Bilingual lexica, which are basically lists of TEs, have not been considered or given enough importance as resources in the treatment of these problems. Human validation of TEs, which consists of manually classifying TEs as correct or incorrect translations, has also not been considered in the context of alignment and extraction. Validation strengthens the importance of infrequent TEs (most of the entries of a validated lexicon) that otherwise would be statistically unimportant.
The main goal of this thesis is to revisit the alignment and extraction problems in the
context of a lexica-centered iterative workflow that includes human validation. Therefore, the methods proposed in this thesis were designed to take advantage of knowledge accumulated in human-validated bilingual lexica and translation tables obtained by unsupervised methods. Phrase-level alignment is a stepping stone for several applications, including the extraction of new TEs, the creation of statistical machine translation systems, and the creation of bilingual concordances. Therefore, for phrase-level alignment, the higher accuracy of human-validated bilingual lexica is crucial for achieving higher quality results in these downstream applications.
There are two main conceptual contributions. The first is the coverage maximization
approach to alignment, which makes direct use of the information contained in a lexicon, or in translation tables when this is small or does not exist. The second is the introduction of translation patterns which combine novel and old ideas and enables precise and productive extraction of TEs. As material contributions, the alignment and extraction methods proposed in this thesis have produced source materials for three lines of research, in the context of three PhD theses (two of them already defended), all sharing with me the supervision of my advisor. The topics of these lines of research are statistical machine translation, algorithms and data structures for indexing and querying phrase-aligned parallel corpora, and bilingual lexica classification and generation. Four publications have resulted directly from the work presented in this thesis and twelve from the collaborative lines of research
Learning to generate one-sentence biographies from Wikidata
We investigate the generation of one-sentence Wikipedia biographies from
facts derived from Wikidata slot-value pairs. We train a recurrent neural
network sequence-to-sequence model with attention to select facts and generate
textual summaries. Our model incorporates a novel secondary objective that
helps ensure it generates sentences that contain the input facts. The model
achieves a BLEU score of 41, improving significantly upon the vanilla
sequence-to-sequence model and scoring roughly twice that of a simple template
baseline. Human preference evaluation suggests the model is nearly as good as
the Wikipedia reference. Manual analysis explores content selection, suggesting
the model can trade the ability to infer knowledge against the risk of
hallucinating incorrect information
Explicit diversification of event aspects for temporal summarization
During major events, such as emergencies and disasters, a large volume of information is reported on newswire and social media platforms. Temporal summarization (TS) approaches are used to automatically produce concise overviews of such events by extracting text snippets from related articles over time. Current TS approaches rely on a combination of event relevance and textual novelty for snippet selection. However, for events that span multiple days, textual novelty is often a poor criterion for selecting snippets, since many snippets are textually unique but are semantically redundant or non-informative. In this article, we propose a framework for the diversification of snippets using explicit event aspects, building on recent works in search result diversification. In particular, we first propose two techniques to identify explicit aspects that a user might want to see covered in a summary for different types of event. We then extend a state-of-the-art explicit diversification framework to maximize the coverage of these aspects when selecting summary snippets for unseen events. Through experimentation over the TREC TS 2013, 2014, and 2015 datasets, we show that explicit diversification for temporal summarization significantly outperforms classical novelty-based diversification, as the use of explicit event aspects reduces the amount of redundant and off-topic snippets returned, while also increasing summary timeliness
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
- …