Advances in natural language processing

Hirschberg, Julia; Manning, Christopher D.

doi:10.1126/science.aaa8685

Cited by 1,256 publications

(721 citation statements)

References 39 publications

Supporting

Mentioning

689

Contrasting

Unclassified

Order By: Relevance

“…The collaboration between psychologists and computing researchers is rapidly evolving, and new studies at the intersection of these fields are published regularly. For example, a recent special issue of Science Magazine dedicated to progress in AI had several mentions to this area (Bohannon 2015;Hirschberg and Manning 2015;Horvitz and Mulligan 2015).…”

Section: Research Limitationsmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

237

148

View full text Add to dashboard Cite

Natural language processing (NLP) techniques can be used to make inferences about peoples' mental states from what they write on Facebook, Twitter and other social media. These inferences can then be used to create online pathways to direct people to health information and assistance and also to generate personalized interventions. Regrettably, the computational methods used to collect, process and utilize online writing data, as well as the evaluations of these techniques, are still dispersed in the literature. This paper provides a taxonomy of data sources and techniques that have been used for mental health support and intervention. Specifically, we review how social media and other data sources have been used to detect emotions and identify people who may be in need of psychological assistance; the computational techniques used in labeling and diagnosis; and finally, we discuss ways to generate and personalize mental health interventions. The overarching aim of this scoping review is to highlight areas of research where NLP has been applied in the mental health literature and to help develop a common language that draws together the fields of mental health, human-computer interaction and NLP.

show abstract

Section: Research Limitationsmentioning

confidence: 99%

Natural language processing in mental health applications using non-clinical texts

Calvo¹,

Milne²,

Hussain³

et al. 2017

Nat. Lang. Eng.

237

148

View full text Add to dashboard Cite

show abstract

“…As such, these methods are not capable of taking into account the actual semantic relationships between parts of the document, individual sentences or even subclauses [2], [3]. Accordingly, these methods often struggle to achieve a favorable performance for longer documents and, hence, new approaches are desired [4].…”

Section: Introductionmentioning

confidence: 99%

Improving Sentiment Analysis with Document-Level Semantic Relationships from Rhetoric Discourse Structures

Märkle-Huß

Feuerriegel

Prendinger

2017

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

Abstract-Conventional sentiment analysis usually neglects semantic information between (sub-)clauses, as it merely implements so-called bag-of-words approaches, where the sentiment of individual words is aggregated independently of the document structure. Instead, we advance sentiment analysis by the use of rhetoric structure theory (RST), which provides a hierarchical representation of texts at document level. For this purpose, texts are split into elementary discourse units (EDU). These EDUs span a hierarchical structure in the form of a binary tree, where the branches are labeled according to their semantic discourse. Accordingly, this paper proposes a novel combination of weighting and grid search to aggregate sentiment scores from the RST tree, as well as feature engineering for machine learning. We apply our algorithms to the especially hard task of predicting stock returns subsequent to financial disclosures. As a result, machine learning improves the balanced accuracy by 8.6 percent compared to the baseline.

show abstract

“…Although NLP is often a sub-system integrated with an application, its growing presence surely cannot be ignored. In IS, NLP research generally concerns the refinement and application of NLP techniques to solve real-world problems [3], such as creating spoken dialogue systems [4], speech-to-speech translation engines [5], mining social media [6] for individual information analysis, or identifying sentiment toward products and services [7]. However, researchers in IS have yet to establish NLP as a mainstream research area.…”

Section: Introductionmentioning

confidence: 99%

“…As an applied discipline, NLP research in IS often seeks to solve real world problems by applying existing NLP-related algorithms and tasks [3]. The research output may include several design artifacts implementing one or more prototypical task and/or algorithms.…”

Section: Introductionmentioning

confidence: 99%