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Teaching industrial robots by demonstration can significantly decrease the repurposing costs of assembly lines worldwide. To achieve this goal, the robot needs to detect and track each component with high accuracy. To speedup the initial object recognition phase, the learning system can gather information from assembly manuals in order to identify which parts and tools are required for assembling...
Aiming at the problem that the traditional single neural network method is limited in feature dimension extraction, a new deep-fusion convolutional neural network is proposed. It uses two kinds of different representations (i.e., word vector and shortest dependency path) as different inputs of convolutional neural network, therefore, it is capable to learn more dimension text features automatically...
With the high usage of internet today, people started sharing much of the information with each other online. In this paper, we propose to monitor user activity for any hazardous behavior like terrorism on Gmail and Twitter. We apply Natural Language Processing (NLP) techniques like POS tagging, Chunking, Stemming, and WordNet Processing to extract the keyword and check to see whether the information...
Since social media have become very popular during the past few years, researchers have been focusing on being able to automatically process and extract sentiments information from large volume of social media data. This paper contributes to the topic, by focusing on sentiment analysis for Chinese social media. In this paper, we propose to rely on Part of Speech (POS) tags in order to extract unigrams...
Problem reports at NASA are similar to bug reports: they capture defects found during test, post-launch operational anomalies, and document the investigation and corrective action of the issue. These artifacts are a rich source of lessons learned for NASA, but are expensive to analyze since problem reports are comprised primarily of natural language text. We apply {topic modeling to a corpus of NASA...
The prominence and usefulness of cell phones has made them appealing focuses for harmful and nosy applications. Android's current risk communication mechanism relies on users to understand the permissions that an app is requesting and to base the installation decision on the list of permissions. The users do not understand or consider the permission information as it requires technical knowledge....
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung disease that affects airflow to the lungs. Discovering the co-occurrence of COPD with other diseases and symptoms is invaluable to medical staff. Building co-occurrence indexes and finding causal relationships with COPD can be difficult because often times disease prevalence within a population influences results. A method which can better...
In this fast moving world, people are ignorant about their health issues and avoid routine check-ups. It is very difficult for users to spend longer time on-line and explore health information. To solve this problem, we provide voice-based android application to the user where user can interact with system and get inference of diseases and their remedies by giving the symptoms as input. For processing...
Knowledge discovery is the process of extracting useful or hidden patterns in data. With the growth of data in a structural form, such as social networks, extracting knowledge from data represented in the form of graphs is an emerging technique. In this paper, we demonstrate how "skills" data from resumes (i.e., what skills an applicant possesses) can be modelled into a type of graph data...
OOV term translation plays an important role in natural language processing. Although many researchers in the past have endeavored to solve the OOV term translation problems, but none existing methods offer definition or context information of OOV terms. Furthermore, non-existing methods focus on cross-language definition retrieval for OOV terms. Never the less, it has always been so difficult to...
Risk permeates all aspects of doing business. However, support tools capable of systematically identifying the complete spectrum of risks that a company might face are currently lacking. Such a tool would need to reliably identify company-risk relationships from unstructured sources, therefore providing a qualitative assessment of risk exposure. We propose a supervised learning approach that combines...
Social media interactions have become increasingly important in today's world. A survey conducted in 2014 among adult Americans found that a majority of those surveyed use at least one social media site. Twitter, in particular, serves 310 million active users on a monthly basis, and thousands of tweets are published every second. The public nature of this data makes it a prime candidate for data mining...
Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is...
In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach...
The inter-departmental interactions and coordination of resources are two essential components for realising a smart city platform. In this study, we investigated citizens' role in enhancing and facilitating the delivery of services by merging three key aspects of the smart city research field, namely Internet of People, Internet of Things and Web of Data. To this end, we developed a hybrid approach...
Information Extraction (IE), one of the important tasks in text analysis and Natural Language Processing (NLP), involves extracting meaningful pieces of knowledge from unstructured information sources, as unstructured data is computationally opaque. The intent of IE is to produce a knowledge base i.e. organize the information in a way that it is useful to people and arrange the information in a semantic...
Aspect Based Sentiment Analysis (ABSA) provides further insight into the analysis of social media. Understanding user opinion about different aspects of products, services or policies can be used for improving and innovating in an effective way. Thus, it is becoming an increasingly important task in the Natural Language Processing (NLP) realm. The standard pipeline of aspect-based sentiment analysis...
The problem being addressed in this paper is that using brute force in Natural Language Processing and Machine Learning combined with advanced statistics will only approximate meaning and thus will not deliver in terms of real text understanding. Counting words and tracking word order or parsing by syntax will also result in probability and guesswork at best. Their vendors struggle in delivering accurate...
While several relation extraction algorithms have been developed in the past decade, mainly in the English language, only few researchers target the Arabic language owing to its complexity and rich morphology. This paper proposes a semi-supervised pattern-based bootstrapping technique to extract Arabic semantic relation that lies between entities. In order to enhance the performance to suit the morphologically...
The implementation of electronic medical records (EMRs) produces a huge amount of unstructured clinical text. This domain-specific clinical text has opened a stage for temporal information extraction (TIE) due to its significance of exploitation in medical care and richness of temporality. Processing temporal information in clinical text is much more difficult in comparison to newswire text due to...
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