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We consider two models for the sequence labeling (tagging) problem. The first one is a Pattern-Based Conditional Random Field (PB), in which the energy of a string (chain labeling) x=x1⁢…⁢xn∈Dn is a sum of terms over intervals [i,j] where each term is non-zero ...

The trade in antiquities and cultural objects has proven difficult to understand and yet is highly dynamic. Currently, there are few computational tools that allow researchers to systematically understand the nature of the legal market, which can also ...

Building a computer system, which can automatically answer questions in the human language, speech or text, is a long-standing goal of the Artificial Intelligence (AI) field. Question analysis, the task of extracting important information from the ...

Fine-grained sentiment analysis of online product reviews is important to both potential consumers and sellers. Automatically extracting appraisal expressions from online reviews is one of the key issues in fine-grained sentiment analysis. The existing ...

Address standardization is the process of recognizing and normalizing free-form addresses into a common standard format. In today's digital economy, this process is increasingly challenging such as in ecommerce fulfillment, logistic planning, ...

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections. However, keyphrases are not always provided with the papers, but they need to be extracted from their ...

This paper addresses the problem of extracting and segmenting references from PDF documents. The novelty of the presented approach lies in its capability to discover highly varying references mainly in terms of content, length and location in the ...

Character-level sequence label tagging is the most efficient way to solve unknown words problem for Chinese word segment. But the most widely used model, Conditional Random Fields (CRF), needs a large amount of manual design features. So it is ...

In recent years, one of the most important scientific task in robotics is research aimed at developing methods that ensure a safe interaction between a robot and a human. An important aspect of solving this problem is the need to find a solution ...

As robots become increasingly prevalent in human environments, there will inevitably be times when the robot needs to interrupt a human to initiate an interaction. Our work introduces the first interruptibility-aware mobile-robot system, which uses ...

The paper analyzes implementation of intelligence-led policing into the National Police of Ukraine. The architecture of analytical system is proposed based on the analysis of state-of-the-art in the National Police of Ukraine. The mathematical apparatus ...

We propose a concurrent Fully Convolutional Networks(CFCN) structure which contains three Fully Convolutional Networks(FCN). Gaussian filter, Mean filter and Median filter are chosen to pre-process the original multimodal MR images. Then, we fuse the ...

Semantic segmentation and other pixel-level labeling tasks have made significant progress recently due to the deep learning paradigm. Many state-of-the-art structured prediction methods also include a random field model with a hand-crafted Gaussian ...

Robots are increasingly being deployed in unstructured human environments where they will need to approach and interrupt collocated humans. Most prior work on robot interruptions has focused on how to interrupt a person or on estimating a human's ...

A large number of patients discuss treatments in online health communities (OHCs). One research question of interest to health researchers is whether treatments being discussed in OHCs are eventually used by community members in their real lives. In ...

Address Cleansing is a very challenging problem, in particular, for geographies where there is a lot of variability in address nomenclature. This paper explains how a machine learning classifier is used to build a model that not only identifies a valid ...

There are many studies that show using class-based features improves the performance of natural language processing (NLP) tasks such as syntactic part-of-speech tagging, dependency parsing, sentiment analysis, and slot filling in natural language ...

We develop an unsupervised learning framework for extracting popular product attributes from product description pages originated from different E-commerce Web sites. Unlike existing information extraction methods that do not consider the popularity of ...

Complex chronic diseases (e.g., autism, lupus, and Parkinson's) are remarkably heterogeneous across individuals. This heterogeneity makes treatment difficult for caregivers because they cannot accurately predict the way in which the disease will progress ...

Focal cortical dysplasia (FCD) is the most common cause of pediatric epilepsy and the third most common cause in adults with treatment-resistant epilepsy. Surgical resection of the lesion is the most effective treatment to stop seizures. Technical ...