Language modeling using a statistical dependency grammar parser

Author(s):  
Wen Wang ◽  
M.R. Harper
2001 ◽  
Vol 32 (12) ◽  
pp. 10-15 ◽  
Author(s):  
Akinori Ito ◽  
Chiori Hori ◽  
Masaharu Katoh ◽  
Masaki Kohda

1994 ◽  
Author(s):  
R. Schwartz ◽  
L. Nguyen ◽  
F. Kubala ◽  
G. CHou ◽  
G. Zavaliagkos ◽  
...  

2019 ◽  
Author(s):  
Chang Liu ◽  
Zhen Zhang ◽  
Pengyuan Zhang ◽  
Yonghong Yan
Keyword(s):  

2021 ◽  
Vol 55 (1) ◽  
pp. 231-263
Author(s):  
Timothy Osborne

Abstract The so-called ‘Big Mess Construction’ (BMC) frustrates standard assumptions about the structure of nominal groups. The normal position of an attributive adjective is after the determiner and before the noun, but in the BMC, the adjective precedes the determiner, e.g. that strange a sound, so big a scandal, too lame an excuse. Previous accounts of the BMC are couched in ‘Phrase Structure Grammar’ (PSG) and view the noun or the determiner (or the preposition of) as the root/head of the BMC phrase. In contrast, the current approach, which is couched in a ‘Dependency Grammar’ (DG) model, argues that the adjective is in fact the root/head of the phrase. A number of insights point to the adjective as the root/head, the most important of which is the optional appearance of the preposition of, e.g. that strange of a sound, so big of a scandal, too lame of an excuse.


2017 ◽  
Vol 51 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Jay M. Ponte ◽  
W. Bruce Croft

Author(s):  
Abdul Rehman Javed ◽  
Saif Ur Rehman ◽  
Mohib Ullah Khan ◽  
Mamoun Alazab ◽  
Habib Ullah Khan

With the recent advancement of smartphone technology in the past few years, smartphone usage has increased on a tremendous scale due to its portability and ability to perform many daily life tasks. As a result, smartphones have become one of the most valuable targets for hackers to perform cyberattacks, since the smartphone can contain individuals’ sensitive data. Smartphones are embedded with highly accurate sensors. This article proposes BetaLogger , an Android-based application that highlights the issue of leaking smartphone users’ privacy using smartphone hardware sensors (accelerometer, magnetometer, and gyroscope). BetaLogger efficiently infers the typed text (long or short) on a smartphone keyboard using Language Modeling and a Dense Multi-layer Neural Network (DMNN). BetaLogger is composed of two major phases: In the first phase, Text Inference Vector is given as input to the DMNN model to predict the target labels comprising the alphabet, and in the second phase, sequence generator module generate the output sequence in the shape of a continuous sentence. The outcomes demonstrate that BetaLogger generates highly accurate short and long sentences, and it effectively enhances the inference rate in comparison with conventional machine learning algorithms and state-of-the-art studies.


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