78,306 research outputs found
A Review Paper on Various Search Engines (Google, Yahoo, Altavista, Ask and Bing)
Search engines are used in the web as a tool for information retrieval. As the web is a huge repository of heterogeneous and unstructured data so to filter out relevant information from unnecessary ones search engines are needed. Search engines usually consists of crawling module, page repository, indexing module, querying module and ranking module. The inter communication between these modules describes the working methodology of a search engine. This paper aims to focus on the comparative analysis of five major search engines i.e Google, Yahoo, Altavista, Ask and Bing in a tabular form based on some features. The features include search operator, search web, search images, search videos, search news, search maps, search books, advance search, change background, change search settings, display number of results, shopping, translation services, multi-language support, questions/answers, directory, advertising programs, business services, themes, case sensitive, finance, safe search, search pad, career and preferences. Google stands out as the best search engine amongst all search engines, which works on Page Rank algorithm. Page Rank is a numeric value which determines the importance of a web page by calculating the number of backlinks
Limitations of Cross-Lingual Learning from Image Search
Cross-lingual representation learning is an important step in making NLP
scale to all the world's languages. Recent work on bilingual lexicon induction
suggests that it is possible to learn cross-lingual representations of words
based on similarities between images associated with these words. However, that
work focused on the translation of selected nouns only. In our work, we
investigate whether the meaning of other parts-of-speech, in particular
adjectives and verbs, can be learned in the same way. We also experiment with
combining the representations learned from visual data with embeddings learned
from textual data. Our experiments across five language pairs indicate that
previous work does not scale to the problem of learning cross-lingual
representations beyond simple nouns
PLuTO: MT for online patent translation
PLuTO – Patent Language Translation Online – is a partially EU-funded commercialization project which specializes in the automatic retrieval and translation of patent documents. At the core of the PLuTO framework is a machine translation (MT) engine through which web-based translation services are offered. The fully integrated PLuTO architecture includes a translation engine coupling MT with translation memories (TM), and a patent search and retrieval engine. In this paper, we first describe the motivating factors behind the provision of such a service. Following this, we give an overview of the PLuTO framework as a whole, with particular emphasis on the MT components, and provide a real world use case scenario in which PLuTO MT services are exploited
MATREX: the DCU MT system for WMT 2009
In this paper, we describe the machine translation system in the evaluation campaign of the Fourth Workshop on Statistical Machine Translation at EACL 2009.
We describe the modular design of our multi-engine MT system with particular focus on the components used in this participation. We participated in the translation task
for the following translation directions: French–English and English–French, in which we employed our multi-engine architecture to translate. We also participated in the system combination task which was carried out by the MBR decoder and Confusion Network decoder.
We report results on the provided development and test sets
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification
End-to-end neural machine translation has overtaken statistical machine
translation in terms of translation quality for some language pairs, specially
those with large amounts of parallel data. Besides this palpable improvement,
neural networks provide several new properties. A single system can be trained
to translate between many languages at almost no additional cost other than
training time. Furthermore, internal representations learned by the network
serve as a new semantic representation of words -or sentences- which, unlike
standard word embeddings, are learned in an essentially bilingual or even
multilingual context. In view of these properties, the contribution of the
present work is two-fold. First, we systematically study the NMT context
vectors, i.e. output of the encoder, and their power as an interlingua
representation of a sentence. We assess their quality and effectiveness by
measuring similarities across translations, as well as semantically related and
semantically unrelated sentence pairs. Second, as extrinsic evaluation of the
first point, we identify parallel sentences in comparable corpora, obtaining an
F1=98.2% on data from a shared task when using only NMT context vectors. Using
context vectors jointly with similarity measures F1 reaches 98.9%.Comment: 11 pages, 4 figure
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