2,819 research outputs found
Polyglot: Distributed Word Representations for Multilingual NLP
Distributed word representations (word embeddings) have recently contributed
to competitive performance in language modeling and several NLP tasks. In this
work, we train word embeddings for more than 100 languages using their
corresponding Wikipedias. We quantitatively demonstrate the utility of our word
embeddings by using them as the sole features for training a part of speech
tagger for a subset of these languages. We find their performance to be
competitive with near state-of-art methods in English, Danish and Swedish.
Moreover, we investigate the semantic features captured by these embeddings
through the proximity of word groupings. We will release these embeddings
publicly to help researchers in the development and enhancement of multilingual
applications.Comment: 10 pages, 2 figures, Proceedings of Conference on Computational
Natural Language Learning CoNLL'201
Catchment Care - Developing an Auction Process for Biodiversity and Water Quality Gains. Volume 1 - Report
This report describes the design, development and trial of catchment care. Catchment Care is an auction-based system which aims to increase the cost effectiveness of funds for private on-ground natural resource management actions.Water;Australia;Natural Resource Management;Catchment Care; auction.
Catchment Care - Developing an Auction Process for Biodiversity and Water Quality Gains. Volume 2 - Appendices
A Market-Based Instrument Pilot Project. Report to the Onkaparinga Catchment Water Management Board.Water;Australia;Natural Resource Management;Catchment Care; auction, market-based instruments.
The Expressive Power of Word Embeddings
We seek to better understand the difference in quality of the several
publicly released embeddings. We propose several tasks that help to distinguish
the characteristics of different embeddings. Our evaluation of sentiment
polarity and synonym/antonym relations shows that embeddings are able to
capture surprisingly nuanced semantics even in the absence of sentence
structure. Moreover, benchmarking the embeddings shows great variance in
quality and characteristics of the semantics captured by the tested embeddings.
Finally, we show the impact of varying the number of dimensions and the
resolution of each dimension on the effective useful features captured by the
embedding space. Our contributions highlight the importance of embeddings for
NLP tasks and the effect of their quality on the final results.Comment: submitted to ICML 2013, Deep Learning for Audio, Speech and Language
Processing Workshop. 8 pages, 8 figure
The Tango Tokio
[Verse 1] Way out West, all over the golden gate, Theyâve a tango, gee, but itâs simply great! Oh! oh! oh! oh! itâs the nicest tune, I know, It is called the Japanese glide away; You should see those Japanese slide away, When they play it, evârybody starts to sway it:
[Chorus] Oh, oh, you Jap, little Jap, little Jap, little Japanese! Oh, oh, you cute little yap, little yap, little yapanese! How we live to see you prance, When they play that tango dance; It just puts us in a trance Oh, pinky panky poo, pinky panky poo! Oh, oh, you sly little, sly little, sly little Japaneses! You are a fly little, fly little, fly little Japanese! Thoâ you sometimes make us mad, If you want to make us glad, Do that teasing Tango Tokio.
[Verse 2] When you hear that Tokio Tango tune, Youâll go dip, dip, dippy and pretty soon Youâll start swaying, just like this and just like that; Youâll imagine you are in Tokio, You will go clean clean off your kokio, If you know it, all day long youâd want to do it:
[Chorus
Sometime, Someday, Somewhere
https://digitalcommons.library.umaine.edu/mmb-vp/4050/thumbnail.jp
I Didn\u27t Raise My Boy to Be a Soldier
VERSE 1Ten million soldiers to the war have gone,Who may never return again.Ten million mothersâ hearts must breakFor the ones who died in vain.Head bowed down in sorrowIn her lonely years,I heard a mother murmur throâ her tears:
CHORUSâI didnât raise my boy to be a soldier,I brought him up to be my pride and joy,Who dares to place a musket on his shoulder,To shoot some other motherâs darling boy?Let nations arbitrate their future troubles,Itâs time to lay the sword and gun away,Thereâd be no war today,If mothers all would say,âI didnât raise my boy to be a soldier.â
VERSE 2What victory can cheer a motherâs heart,When she looks at her blighted home?What victory can bring her backAll she cared to call her own.Let each mother answerIn the years to be,Remember that my boy belongs to me!
CHORU
Billy, Billy, Bounce Your Baby Doll
https://digitalcommons.library.umaine.edu/mmb-vp/4336/thumbnail.jp
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