An improved neural network model for joint POS tagging and dependency parsing

We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakov\'a, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: https://github.com/datquocnguyen/jPTDPComment: 11 pages; In Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, to appea

Dynamic Feedback Pulse Shaping For High Power Chirped Pulse Amplification System

The topic of this proposal is the development of high peak power laser sources with a focus on linearly chirped pulse laser sources. In the past decade chirped optical pulses have found a plethora of applications such as photonic analog-to-digital conversion, optical coherence tomography, laser ranging, etc. This dissertation analyzes the aforementioned applications of linearly chirped pulses and their technical requirements, as well as the performance of previously demonstrated parabolic pulse shaping approaches. The experimental research addresses the topic of parabolic pulse generation in two distinct ways. First, pulse shaping technique involving a time domain approach is presented, that results in stretched pulses with parabolic profiles with temporal duration of 15 ns. After pulse is shaped into a parabolic intensity profile, the pulse is compressed with DCF fiber spool by 100 times to 80 ps duration at FWHM. A different approach of pulse shaping in frequency domain is performed, in which a spectral processor based on Liquid Crystal on Silicon technology is used. The pulse is stretched to 1.5 ns before intensity mask is applied, resulting in a parabolic intensity profile. Due to frequency to time mapping, its temporal profile is also parabolic. After pulse shaping, the pulse is compressed with a bulk compressor, and subsequently analyzed with a Frequency Resolved Optical Gating (FROG). The spectral content of the compressed pulse is feedback to the spectral processor and used to adjust the spectral phase mask applied on the pulse. The resultant pulse iv after pulse shaping with feedback mechanism is a Fourier transform, sub-picosecond ultrashort pulse with 5 times increase in peak power. The appendices in this dissertation provide additional material used for the realization of the main research focus of the dissertation. Specification and characterization of major components of equipments and devices used in the experiment are present. The description of Matlab algorithms that was used to calculate required signals for pulse shaping are shown. A brief description of the Labview code used to control the spectral processor will also be illustrated

A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing

We present a novel neural network model that learns POS tagging and graph-based dependency parsing jointly. Our model uses bidirectional LSTMs to learn feature representations shared for both POS tagging and dependency parsing tasks, thus handling the feature-engineering problem. Our extensive experiments, on 19 languages from the Universal Dependencies project, show that our model outperforms the state-of-the-art neural network-based Stack-propagation model for joint POS tagging and transition-based dependency parsing, resulting in a new state of the art. Our code is open-source and available together with pre-trained models at: https://github.com/datquocnguyen/jPTDPComment: v2: also include universal POS tagging, UAS and LAS accuracies w.r.t gold-standard segmentation on Universal Dependencies 2.0 - CoNLL 2017 shared task test data; in CoNLL 201

The Cult of Ho Chi Minh: Commemoration and Contestation

Ho Chi Minh, the “father of modern Viet Nam,” remains a powerful figure in contemporary Vietnamese politics and culture. Since his death in 1969, the Vietnamese Communist Party has constructed a state cult surrounding his image. The construction of the Ho Chi Minh memorial complex in Hanoi, the propagation of Ho Chi Minh’s teachings, and the state commemorative rituals for Uncle Ho contribute to his continuous presence. The state cult posits Ho Chi Minh not only as the “father figure” to whom Vietnamese people pay respect and tribute, but also as the moral compass by which the people orient themselves socially and culturally. The state cult, however, is continuously contested. On the one hand, meanings attributed to the state commemoration of Ho Chi Minh are changing temporally and regionally. On the other hand, development of various religious cults of Uncle Ho challenges the Party’s hegemonic interpretation of the image of Ho Chi Minh. Drawing from historical research and short-term fieldwork, this paper discusses various modes of commemorative rituals dedicated to Ho Chi Minh, and explores how they contribute to the cult of Ho Chi Minh as a contested field of knowledge, where political, cultural, and personal meanings are constantly negotiated. Particular attention is paid to how Vietnamese people, both in Vietnam and abroad, perform, construct, and challenge the discourses surrounding the cult, as well as to how the Party and the state respond to these voices of discordance