This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-of-speech tagging, syntactic chunking, and named entity recognition. We ﬁrst propose a simple yet powerful semi-supervised discriminative model appropriate for handling large scale unlabeled data. Then, we describe experiments performed on widely used test collections, namely, PTB III data, CoNLL’00 and ’03 shared task data for the above three NLP tasks, respectively. ...
Let’s face it.
The formal part of software testing is a bore and a necessary
evil at best.
At least that is what most people in software development
will tell you. Testers are on projects to point out mistakes.
Who wants to do that?
Well that is a perception, and this book isn’t going to change it.
What this book will do is skip the ceremony and present
testing concepts, tying them together in a sequential and
The last twenty years of the last millennium are characterized by complex automatization
of industrial plants. Complex automatization of industrial plants means a
switch to factories, automatons, robots and self adaptive optimization systems. The
mentioned processes can be intensified by introducing mathematical methods into
all physical and chemical processes
Nowadays, Agile application development is usually done at a fast pace when many developers are working on the same piece of code. Every so often, this becomes a real challenge if there’s no permanent control over consistency of the project source. It is often impossible to force lazy and/or busy programmers to execute tests before and after each of their commits. Continuous Integration is a well-known life saver for distributed development environments with TeamCity being one of the best and easy-to-use instruments utilizing it. ...
This study uses what Cresswell (2003) refers to as a ‘mixed methods approach’, one that combines
quantitative and qualitative data collection and a ‘sequential explanatory strategy’ in which the
collection and analysis of the quantitative data is followed by the collection and analysis of the
qualitative data (p 215).
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A sequential explanatory mixed methods approach was chosen for this study as the intention was to
use the qualitative results to ‘assist in explaining and interpreting the findings of a primarily
quantitative study’ (Cresswell, 2003, p 215). Scores from a current IELTS Test (Test 2) and an earlier
one (Test 1) provided quantitative data for analysis. Interviews were conducted after Test 2 with
almost all of the participants. A combination of both quantitative and qualitative approaches such as
this is justified by many researchers in human research.
The previous chapter examined methods for creating sensitized paths in combinational logic extending from stuck-at faults on logic gates to observable outputs. We now attempt to create tests for sequential circuits where the outputs are a function not just of present inputs but of past inputs as well. The objective will be the same: to create a sensitized path from the point where a fault occurs to an observable output. However, there are new factors that must be taken into consideration.
HAVER & BOECKER today is the
only company that is able to offer
all solutions within the so-called
screening circle. THE SCREENING
CIRCLE comprises the sequential
process steps needed to form the
complete screening process, and thus includes: material analysis,
professional customer advising,
screening machine size determination,
screening media selection,
system and design calculations,
in-house testing prior to machine
delivery, machine installation and
start-up, and worldwide customer
This paper describes the conversion of a Hidden Markov Model into a sequential transducer that closely approximates the behavior of the stochastic model. This transformation is especially advantageous for part-of-speech tagging because the resulting transducer can be composed with other transducers that encode correction rules for the most frequent tagging errors. The speed of tagging is also improved. The described methods have been implemented and successfully tested on six languages.
We present an unsupervised, nonparametric Bayesian approach to coreference resolution which models both global entity identity across a corpus as well as the sequential anaphoric structure within each document. While most existing coreference work is driven by pairwise decisions, our model is fully generative, producing each mention from a combination of global entity properties and local attentional state. Despite being unsupervised, our system achieves a 70.3 MUC F1 measure on the MUC-6 test set, broadly in the range of some recent supervised results. ...