AIMC Topic: Semantics

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Learning about phraseology from corpora: A linguistically motivated approach for Multiword Expression identification.

PloS one
Multiword Expressions (MWEs) are idiosyncratic combinations of words which pose important challenges to Natural Language Processing. Some kinds of MWEs, such as verbal ones, are particularly hard to identify in corpora, due to their high degree of mo...

Hierarchical fusion of common sense knowledge and classifier decisions for answer selection in community question answering.

Neural networks : the official journal of the International Neural Network Society
The goal of answer selection is to select the most applicable answers from an answer candidate pool. It plays an essential role in numerous applications in information retrieval (IR) and natural language processing (NLP). In this paper, we introduce ...

Identifying disease trajectories with predicate information from a knowledge graph.

Journal of biomedical semantics
BACKGROUND: Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often dia...

A system for automatically extracting clinical events with temporal information.

BMC medical informatics and decision making
BACKGROUND: The popularization of health and medical informatics yields huge amounts of data. Extracting clinical events on a temporal course is the foundation of enabling advanced applications and research. It is a structure of presenting informatio...

CAS: corpus of clinical cases in French.

Journal of biomedical semantics
BACKGROUND: Textual corpora are extremely important for various NLP applications as they provide information necessary for creating, setting and testing those applications and the corresponding tools. They are also crucial for designing reliable meth...

Leveraging maximum entropy and correlation on latent factors for learning representations.

Neural networks : the official journal of the International Neural Network Society
Many tasks involve learning representations from matrices, and Non-negative Matrix Factorization (NMF) has been widely used due to its excellent interpretability. Through factorization, sample vectors are reconstructed as additive combinations of lat...

Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context window.

PloS one
Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial seman...

A deep learning model for detecting mental illness from user content on social media.

Scientific reports
Users of social media often share their feelings or emotional states through their posts. In this study, we developedĀ a deep learning model to identify a user's mental state based on his/her posting information. To this end, we collected posts from m...

A knowledge-based system to find over-the-counter medicines for self-medication.

Journal of biomedical informatics
This study developed a medicine query system based on Semantic Web and open data especially for self-medication users to search over-the-counter (OTC) medicines. Most existing medicine query systems are based on keyword searches. If users are uncerta...

Cross-Modal Search for Social Networks via Adversarial Learning.

Computational intelligence and neuroscience
Cross-modal search has become a research hotspot in the recent years. In contrast to traditional cross-modal search, social network cross-modal information search is restricted by data quality for arbitrary text and low-resolution visual features. In...