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13 records found

LOREM

Language-consistent Open Relation Extraction from Unstructured Text

We introduce a Language-consistent multi-lingual Open Relation Extraction Model (LOREM) for finding relation tuples of any type between entities in unstructured texts. LOREM does not rely on language-specific knowledge or external NLP tools such as translators or PoS-taggers, and ...
Named Entity Recognition (NER) is an essential information retrieval task. It enables a wide range of natural language processing applications such as semantic search, machine translation, etc. The NER can be formulated as the task of identifying and typing words or phrases in a ...
The automatic mapping of Adverse Drug Reaction (ADR) reports from user-generated content to concepts in a controlled medical vocabulary provides valuable insights for monitoring public health. While state-of-the-art deep learning-based sequence classification techniques achieve i ...
Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this p ...

Coner

A Collaborative Approach for Long-Tail Named Entity Recognition in Scientific Publications

Named Entity Recognition (NER) for rare long-tail entities as e.g., often found in domain-specific scientific publications is a challenging task, as typically the extensive training data and test data for fine-tuning NER algorithms is lacking. Recent approaches presented promisin ...
This paper describes the system that team MYTOMORROWS-TU DELFT developed for the 2019 Social Media Mining for Health Applications (SMM4H) Shared Task 3, for the end-to-end normalization of ADR tweet mentions to their corresponding MEDDRA codes. For the first two steps, we reuse a ...

SmartPub

A Platform for Long-Tail Entity Extraction from Scientific Publications

This demo presents SmartPub, a novel web-based platform that supports the exploration and visualization of shallow meta-data (e.g., author list, keywords) and deep meta-data--long tail named entities which are rare, and often relevant only in specific knowledge domain--from scien ...

TSE-NER

An Iterative Approach for Long-Tail Entity Extraction in Scientific Publications

Named Entity Recognition and Typing (NER/NET) is a challenging task, especially with long-tail entities such as the ones found in scientific publications. These entities (e.g. “WebKB”, “StatSnowball”) are rare, often relevant only in specific knowledge domains, yet important for ...

Concept Focus

Semantic Meta-Data For Describing MOOC Content

MOOCs promised to herald a new age of open education.
However, efficient access to MOOC content is still hard, thus unneces-
sarily complicating many use cases like efficient re-use of material, or
tailored access for life-long learning scenarios. O ...

Nudge your Workforce

A Study on the Effectiveness of Task Notification Strategies in Enterprise Mobile Crowdsourcing

As crowdsourcing gains popularity, organisations seek ways to systematically and reliably involve their workforce with data processing pipelines. Mobile crowdsourcing allows for opportunistic task executions and thus, potentially, for higher throughput. However, how to engage and ...
Data processing pipelines are a core object of interest for data scientist and practitioners operating in a variety of data-related application domains. To effectively capitalise on the experience gained in the creation and adoption of such pipelines, the need arises for mechanis ...
With the increasing amount of scientific publications in digital libraries, it is crucial to capture “deep meta-data” to facilitate more effective search and discovery, like search by topics, research methods, or data sets used in a publication. Such meta-data can also help to be ...
The rise of Big Data analytics has been a disruptive game changer for many application domains, allowing the integration into domain-specific applications and systems of insights and knowledge extracted from external big data sets. The effective ``injection'' of external Big Data ...