Language processing in social context

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Wars and conflicts have constituted major events throughout history. Despite their importance, the general public typically learns about such events only indirectly, through the lens of news media, which necessarily select and distort events before relaying them to readers.

What is natural language processing?

Quantifying these processes is important, as they are fundamental to how we see the world, but the task is difficult, as it requires working with large and representative datasets of unstructured news text in many languages. To address these issues, we propose several simple yet effective unsupervised methods for compiling and analyzing a multilingual corpus of millions of online news documents about armed conflicts. We then apply our methods to answer a number of research questions: First, how widely are armed conflicts covered by online news media across 13 languages, and how does this change as conflicts progress?

Second, what role does the level of violence of a conflict play? And third, how well informed is a reader when following a limited number of online news sources? We find that coverage levels are different across conflicts, but similar across languages for a given conflict; that Middle Eastern conflicts receive more attention than African conflicts, even when controlling for the level of violence; and that for most languages and conflicts, following very few sources is enough to stay continuously informed.

Finally, given the prominence of conflicts in the Middle East, we further analyze them in a more detailed case study. His research aims to understand, predict, and enhance human behavior in social and information networks by developing techniques in data science, data mining, network analysis, machine learning, and natural language processing. Please refer to Program ACL.

Please refer to Program WWW. Participants will need to detect the emotion for each utterance among four label candidates: joy, sadness, anger, and neutral. Each team needs to submit a paper describing their system before the task paper submission deadline. After the notification of evaluation, participants can analyze their models and results accordingly and update their paper so as to submit their camera ready version. There will be a special session in the workshop for the EmotionX task. Those teams that achieve the best results or propose novel models will be selected for oral presentation in this session, and others should present their systems with posters.

The type of presentation, oral or poster, will be announced together with the evaluation results. Please visit the challenge website EmotionX for the details. Paper Template. ACL Style Files. ACM Format sigconf. Submission Site. Opening and Welcome. Coffee Break. To make this process scalable, our dictionaries automatically improve using manually coded data samples in each language and industry. To date, Synthesio can offer auto-sentiment analysis in 12 languages including English, German, Spanish, French, Polish, Portuguese, Dutch, Chinese, Japanese, Russian, Italian and Turkish — making it very simple for global brands to gain valuable actionable insights into their consumer base — that may be implemented into PR, Marketing, Advertising, Sales and Customer Service efforts.

Once enough data social media verbatim is manually tagged, we can activate the system and start classifying verbatim — and our machine learning algorithm copies our classification process — and begins classifying automatically. The benefit of auto-text analysis is that computers can analyze and classify much more data at a much quicker rate than humans and sometimes with better accuracy! Thanks to our NLP team, we now have word clouds that seek out and display trends in negative, positive and neutral words and phrases associated with a specific brand or term.

Advanced word clouds simplify the detection of insights among very high volumes of data. This NLP word cloud displays the positive, negative and neutral terms associated with a new Microsoft TV ad — highlighting areas of concern, and trends in language around the ad. By simply clicking on each word, you can drill down and gain context by viewing the posts associated with each word. Metrics details.

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Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health.

In this paper, we present Moonstone , a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes. Our initial use case for the tool is focused on the automatic extraction of social risk factor information — in this case, housing situation , living alone , and social support — from clinical notes.

Nursing notes, social work notes, emergency room physician notes, primary care notes, hospital admission notes, and discharge summaries, all derived from the Veterans Health Administration, were used for algorithm development and evaluation.

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EHR Natural Language Processing Flags Social Determinant Search Terms

An evaluation of Moonstone demonstrated that the system is highly accurate in extracting and classifying the three variables of interest housing situation , living alone , and social support. The system achieved positive predictive value i. The Moonstone system is — to the best of our knowledge — the first freely available, open source natural language processing system designed to extract social risk factors from clinical text with good lives in facility to excellent lives alone performance. Although developed with the social risk factor identification task in mind, Moonstone provides a powerful tool to address a range of clinical natural language processing tasks, especially those tasks that require nuanced linguistic processing in conjunction with inference capabilities.

Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes, life expectancy and health care utilization. Some social risk factors such as alcohol and drug abuse can be captured using administrative and laboratory data.

Enhance Social Context Understanding with Semantic Chunks

However, data related to measures such as housing, living situation and social support are primarily recorded in free-text clinical notes, rather than as computable structured data, and hence resists easy incorporation into prediction models. In this paper, we present Moonstone , a new, highly configurable rule-based natural language processing NLP system designed to automatically extract information that require inferencing from clinical notes.

The use case to which we applied Moonstone for this study is extraction of S ocial D eterminants of H ealth SDOH — specifically, housing situation , living alone , and social support — from clinical notes derived from the V eterans Health A dministration VA. Building on previous rule-based clinical NLP systems [ 1 ], the Moonstone system is designed to be extensible to a range of clinical NLP tasks, especially those that involves the need for nuanced linguistic processing and inference.

The relationship between SDOH and health outcomes is well established [ 2 ]. Lack of housing, social isolation and lack of social support are associated with higher mortality and poor health outcomes. Despite the clear relationship between SDOH and health, these metrics are not routinely used in health services and outcomes research, mainly because many of these health measures are not collected as part of routine care. Therefore, most clinical outcome studies that rely on risk adjustment do not typically utilize social risk data, and models that do incorporate SDOH data are limited to demographic information derived from structured data e.

The importance of these metrics have been recently reinforced by the institution of Affordable Care Act penalties on hospitals with higher than average readmission rates, with the result that hospitals that care for vulnerable and disadvantaged populations are placed at financial risk. In recognition of the important role social factors play in health, the National Quality Forum, National Academy of Medicine, and the Department of Health and Human Services have recently emphasized the need for health care systems to identify and address social risk factors effects on patient care [ 5 ].

There are numerous NLP systems that attempt to extract clinically relevant data from unstructured clinical narratives [ 6 ]. For example, MedEx, a rule-based system designed to extract medication information — drug, dose, frequency — achieves F-scores Footnote 1 of greater than 0. Similarly, MedLee Med ical L anguage E xtraction and E ncoding System uses a rule-based approach to extract clinically relevant information from radiology reports and discharge summaries, and has been used successfully for a number of different clinical information extraction applications e.

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More recently, cTAKES c linical T ext A nalysis and K nowledge E xtraction S ystem utilizes open source technologies and a highly modularized system architecture in conjunction with both machine learning and rule-based methods to perform clinical information extraction tasks. The system has been used for multiple clinical NLP application domains e.


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The NLP systems described above are designed to extract information explicitly stated in clinical text e. This inferencing process requires a degree of semantic analysis and reasoning that existing clinical NLP systems, optimized as they are for explicit information extraction, cannot easily perform.

The current state of the art in automatic social risk factor analysis — as exemplified by Chen et al. However, there is a substantial amount of information relevant to the three variables of interest that is implicit i. Conversely, sentences that suggest that the patient does not experience regular contact and help from family and friends imply a lack of social support. For example, if an elderly patient requires public transport to get home from a medical procedure, this can be taken as evidence — but not proof — of lack of social support.

The number of possible textual instantiations of social support interactions is very large, and probably beyond the capabilities of a simple string matching approach to adequately address. Similar examples can be found for both the housing and living alone variables. Furthermore, the identity of the person with whom the patient lives can be indicative of whether a housing situation is stable or marginal.


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  • The effort to identify implicit, indirect meaning is complicated by several factors:. The target variable is often several inference steps away from what is stated explicitly in the text. Although potentially relevant words are relatively common in clinical text, relevant sentences appear much more sparsely, with very few documents in our corpus containing such sentences.

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