The load resistance in the process of building the steel frame is provided by a combination of long-term work supplemented by temporary assistance when needed. The resistance of permanent structures such as the development progresses. In a self-supporting structure is completed when the resistance of the user job completion.
Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification.
The years 1945–55 saw the emergence of a radically new kind of device: the high-speed
stored-program digital computer. Secret wartime projects in areas such as code-breaking, radar and ballistics had produced a wealth of ideas and technologies that
kick-started this first decade of the Information Age. The brilliant mathematician and
code-breaker Alan Turing was just one of several British pioneers whose prototype
machines led the way.
Turning theory into practice proved tricky, but by 1948 five UK research groups
had begun to build practical stored-program computers.
Windows XP LIVE CD
Any PC user can create a CD that runs Win XP without being installed, and use it to troubleshoot and recover crashed machines. This is one of the best written and best composed tutorials I have seen so far, and not only is it well done but it is extremely useful for anyone as a first aid kit but especially to Network Admins, Systems Admins/Engineers. Let’s get started ☺ Please go to the next page …
With this concise book, you’ll learn the art of building hypermedia APIs that don’t simply run on the Web, but that actually exist in the Web. You’ll start with the general principles and technologies behind this architectural approach, and then dive hands-on into three fully-functional API examples.
Too many APIs rely on concepts rooted in desktop and local area network patterns that don’t scale well—costly solutions that are difficult to maintain over time.
This paper presents an attempt at building a large scale distributed composite language model that simultaneously accounts for local word lexical information, mid-range sentence syntactic structure, and long-span document semantic content under a directed Markov random ﬁeld paradigm.
Recently confusion network decoding shows the best performance in combining outputs from multiple machine translation (MT) systems. However, overcoming different word orders presented in multiple MT systems during hypothesis alignment still remains the biggest challenge to confusion network-based MT system combination. In this paper, we compare four commonly used word alignment methods, namely GIZA++, TER, CLA and IHMM, for hypothesis alignment.
Statistical methods require very large corpus with high quality. But building large and faultless annotated corpus is a very difficult job. This paper proposes an efficient m e t h o d to construct part-of-speech tagged corpus. A rulebased error correction m e t h o d is proposed to find and correct errors semi-automatically by user-defined rules. We also make use of user's correction log to reflect feedback. Experiments were carried out to show the efficiency of error correction process of this workbench. The result shows that about 63.2 % of tagging errors can be corrected. ...
The dominant practice of statistical machine translation (SMT) uses the same Chinese word segmentation specification in both alignment and translation rule induction steps in building Chinese-English SMT system, which may suffer from a suboptimal problem that word segmentation better for alignment is not necessarily better for translation.
Lattice decoding in statistical machine translation (SMT) is useful in speech translation and in the translation of German because it can handle input ambiguities such as speech recognition ambiguities and German word segmentation ambiguities. We show that lattice decoding is also useful for handling input variations. Given an input sentence, we build a lattice which represents paraphrases of the input sentence. We call this a paraphrase lattice. Then, we give the paraphrase lattice as an input to the lattice decoder. ...
This paper extends the training and tuning regime for phrase-based statistical machine translation to obtain ﬂuent translations into morphologically complex languages (we build an English to Finnish translation system). Our methods use unsupervised morphology induction. Unlike previous work we focus on morphologically productive phrase pairs – our decoder can combine morphemes across phrase boundaries. Morphemes in the target language may not have a corresponding morpheme or word in the source language.
Recent advances in Machine Translation (MT) have brought forth a new paradigm for building NLP applications in low-resource scenarios. To build a sentiment classiﬁer for a language with no labeled resources, one can translate labeled data from another language, then train a classiﬁer on the translated text. This can be viewed as a domain adaptation problem, where labeled translations and test data have some mismatch.
In this paper I present a Master’s thesis proposal in syntax-based Statistical Machine Translation. I propose to build discriminative SMT models using both tree-to-string and tree-to-tree approaches. Translation and language models will be represented mainly through the use of Tree Automata and Tree Transducers. These formalisms have important representational properties that makes them well-suited for syntax modeling. nce it’s u
In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker for Basque. After several experiments, and trained with a little corpus of 100,000 words, the sys tem guesses correctly not placing com mas with a precision of 96% and a re call of 98%. It also gets a precision of 70% and a recall of 49% in the task of placing commas. Finally, we have shown that these results can be im proved using a bigger and a more ho mogeneous corpus to train, that is,...
This paper proposes a novel method for phrase-based statistical machine translation by using pivot language. To conduct translation between languages Lf and Le with a small bilingual corpus, we bring in a third language Lp, which is named the pivot language. For Lf-Lp and Lp-Le, there exist large bilingual corpora. Using only Lf-Lp and Lp-Le bilingual corpora, we can build a translation model for Lf-Le. The advantage of this method lies in that we can perform translation between Lf and Le even if there is no bilingual corpus available for this language pair. ...
Writing English is a big barrier for most Chinese users. To build a computer-aided system that helps Chinese users not only on spelling checking and grammar checking but also on writing in the way of native-English is a challenging task. Although machine translation is widely used for this purpose, how to find an efficient way in which human collaborates with computers remains an open issue.
This lecture introduces you to convolutional neural networks. These models have revolutionized speech and object recognition. The goal is for you to learn: Convnets for object recognition and language, how to design convolutional layers, how to design pooling layers, how to build convnets in torch.
RESEARCH on the problems of machine translation has been going on for several years in this country and abroad. 1 To date it has been concerned primarily with the complicated linguistic problems involved in mechanical translation, since the engineers can probably build the necessary equipment.
We present an approach for detecting salient (important) dates in texts in order to automatically build event timelines from a search query (e.g. the name of an event or person, etc.). This work was carried out on a corpus of newswire texts in English provided by the Agence France Presse (AFP). In order to extract salient dates that warrant inclusion in an event timeline, we ﬁrst recognize and normalize temporal expressions in texts and then use a machine-learning approach to extract salient dates that relate to a particular topic....
In this paper we present BabelNet – a very large, wide-coverage multilingual semantic network. The resource is automatically constructed by means of a methodology that integrates lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition Machine Translation is also applied to enrich the resource with lexical information for all languages. We conduct experiments on new and existing gold-standard datasets to show the high quality and coverage of the resource.