How To Form Compound Nouns in Spanish

A puzzle in Spanish is a head-breaker (rompecabezas), and someone who reads books a lot is a book-warmer (calientalibros). These two words are among the more colorful compound words that have entered the Spanish vocabulary. Most compound words are more mundane and self-explanatory (a dishwasher, lavaplatos, for example, is just that). Compound  words, known in Spanish as palabras compuestas, are quite common. They are frequently coined, sometimes for humorous effect, although not all impromptu compound words survive or become widely known. An example is comegusanos, a worm eater, which you wont find in a dictionary but will find in occasional use through an Internet search. How To Form Compound Words As you may have  noticed, the compound words being discussed in this lesson are formed by taking a verb in the third-person singular indicative and following it with a plural noun (or, rarely, a singular noun when it makes more sense to do so). For example, cata (he/she tastes) followed by vinos (wines) gives us catavinos, a winetaster or barhop, depending on the context. Often, these words are the equivalent of the English verb followed by a noun and -er, as in rascacielos, skyscraper. (Rascar means to scrape, and the skies are the cielos.)  In English, such words can be written as one word, a hyphenated word or two words, but in Spanish these compound words always form one unit. Words formed in this way are masculine, with rare exceptions, although they are sometimes used in the feminine if they refer to women or girls. Also, the plural of these words is the same as the singular: a can opener is un abrelatas, but two or more are los abrelatas. If the noun part of the word begins with an r, it is typically changed to an rr, as in quemarropa from quema ropa. Although no collection of compound words can be complete, on the following page is a list of some of the most common along with many that have been included merely because theyre humorous or otherwise interesting. Where the English translation doesnt convey the origin of the Spanish word, a literal translation of the Spanish is included in parentheses. Note that in some cases not all possible meanings of the Spanish words are included. List of Compound Words These are among the most common (or, in a few cases, humorous) compound words in Spanish. It is far from a complete list. abrecartas — letter openerabrelatas — can openerapagavelas — candle snufferbuscapià ©s — firecracker (it looks for feet)calientalibros — bookworm (he/she warms books)calientamanos — handwarmercalientapià ©s — footwarmercalientaplatos — dish warmercascanueces — nutcrackercomecocos — something that confuses or brainwashes (it eats coconuts)cortacuitos — circuit breakercortalà ¡pices — pencil sharpener (it cuts pencils)cortapapel — paper knife (it cuts paper)cortaplumas — penknife (it cuts feathers)cortapuros — cigar cuttercuentagotas — medicine dropper (it counts drops)cuentakilà ³metros — speedometer, odometer (it counts kilometers)cuentapasos — pedometer (it counts steps)cuentarrevoluciones, cuentavueltas — counting machine (it counts revolutions)cuidanià ±os — babysitter (he/she cares for children)cumpleaà ±os — birthday (it fulfill s years)dragaminas — minesweeper (it dredges mines)elevalunas — window openerescarbadientes — toothpick (it scratches teeth)escurreplatos — dish rack (it drains dishes)espantapà ¡jaros — scarecrow (it scares birds)guardarropas — clothes closet (it keeps clothing)lanzacohetes — rocket launcherlanzallamas — flame throwerlanzamisiles — missile launcherlavadedos — finger bowl (it cleans fingers)lavamanos — bathroom sink (it washes hands)lavaplatos, lavavajillas — dishwasherlimpiabarros — scraper (it cleans mud)limpiabotas — shoeshine (he/she cleans boots)limpiachimeneas — chimneysweep (he/she cleans chimneys)limpiacristales — window cleanerlimpiametales — metal polish (it cleans metal)limpiaparabrisas — windshield wiper (it cleans windshields)limpiapipas — pipe cleanerlimpiauà ±as — fingernail cleanera matacaballo — at breakneck speed (in a way that it kills the horse)matafuegos — fire extinguisher (it kills fires)matamoscas — fly swatter (it kills flies)matarratas — rat poison (it kills rats)matasanos — medical quack (he/she kills healthy people)matasellos — postmark (it kills stamps)pagaimpuestos — taxpayerparabrisas — windshield (it stops breezes)paracaà ­das — parachute (it stops falls)parachoques — bumper (it stops crashes)paraguas — umbrella (it stops water)pararrayos — lightning rod (it stops lightning)parasol — sunshade (it stops sun)pesacartas — letter scale (it weighs letters)pesapersonas — scale for people (it weighs people)picaflor — hummingbird, lady-killer (he/she pecks flowers)picapleitos — shyster lawyer (he/she encourages lawsuits)pintamonas — bad painter, an incompetent person (he/she paints copycats)portaaviones — aircraft carrier (it carries aircraft)portacartas — le tter bag (it carries letters)portamonedas — purse, handbag (it carries coins)portanuevas — one who brings newsportaplumas — pen holdera quemarropa — at point-blank range (in a way that burns clothing)quitaesmalte — enamel or nail polish removerquitamanchas — dry cleaner, stain remover (it removes stains)quitamotas — flatterer (he/she removes defects)quitanieve, quitanieves — snowplow (it removes snow)quitapesares — consolation (it takes away sorrow)quitasol — sunshade (it removes the sun)quitasueà ±os — anxiety (it takes away sleep)rascacielos — skyscrapera regaà ±adientes — unwillingly (in a manner that causes the snarling of teeth)rompecabezas — puzzle (it breaks heads)rompeimà ¡genes — iconoclast (he/she breaks icons)rompeolas — jetty (it breaks waves)sabelotodo — know-it-all (he/she knows it all)sacabocados — punch tool (it takes out bites)sacaclavos à ¢â‚¬â€ nail removersacacorchos — corkscrew (it pulls out corks)sacadineros — trinket, small scam (it takes money)sacamanchas — dry cleaner (it takes away stains)sacamuelas — dentist, quack (he/she pulls teeth)sacapotras — medical quack (he/she removes hernias)sacapuntas — pencil sharpener (it sharpens points)saltamontes — grasshopper (it jumps hills)salvavidas — certain safety devices (it saves lives)secafirmas — blotting pad (it dries signatures)tientaparedes — one who gropes his/her way (he/she feels walls)tirabotas — boot hook (it stretches boots)tiralà ­neas — drawing pen (it draws lines)tocacasetes — cassette playertocadiscos — record playertrabalenguas — tongue twister (it ties tongues)tragahombres — bully (he/she swallows men)tragaleguas — long-distance or fast runner (he/she swallows leagues; a league is a little-used measurement of distance, equal to abou t 5.6 kilometers)tragaluz — skylight (it swallows light)tragamonedas, tragaperras — slot machine, vending machine (it swallows coins) Key Takeaways A common type of compound noun is formed in Spanish by using a third-person singular indicative present-tense verb and following it with a plural noun attached to the verb.Such compound nouns are often the equivalent of noun verb -er in English.Such compound nouns are masculine, and the plural form is identical to the singular.

Common Failings of Big Data Analysis - Free Essay Example

Sample details Pages: 4 Words: 1239 Downloads: 3 Date added: 2017/06/26 Category IT Essay Type Analytical essay Did you like this example? Common failings of Big Data analysis: There are numerous examples of how Big Data can be used to forecast the publics reaction when it comes to box office receipts, sale of consumer goods and the outcome of certain events such as American Idol. However, even in the case of the of predicting something as ridiculously inane as American Idol there are qualifications that need to be made about the use of the data collected. à ¢Ã¢â€š ¬Ã…“As many authors have pointed out, there are several challenges one must face when dealing with data of this nature: intrinsic biases, uneven sampling across location of interest etc.à ¢Ã¢â€š ¬Ã‚  American Idol. Don’t waste time! Our writers will create an original "Common Failings of Big Data Analysis" essay for you Create order While the experiment in american idol is largely viewed as success it concludes that the open source data available on the web can be used to make educated guesses on the outcome of societal events. Surely an educated guess is nothing to get excited about. This section of the paper points attempts to bring to light the failings in analysis of data sourced from social media such as twitter or from search terms used in Google searches. We focus on three distinct areas which have attempted to use these sources of information to predict future outcomes of some event. These areas are: Elections Flu Trends Stock Market trends Elections: Shortly after the 2010 US general elections flamboyant statements made it to the news media headlines, from those arguing that Twitter is not a reliable predictor to those claiming the opposite (How not to predict elections). It has been claimed that Twitter can predict the outcome of elections with great accuracy. Given the significant differences in the demographics between likely voters and users of social networks questions arise on what is the underlying operating principle enabling these predictions (How not to predict elections). As is reported by à ¢Ã¢â€š ¬Ã…“How not to predict electionsà ¢Ã¢â€š ¬Ã‚  the degree of accuracy in these claims is recorded in terms of percentage of correctly guessed electoral races without any further qualification at all. When these predictions are reported they are often not compared against results which were arrived at by more traditional means. For instance in the 2008 US congressional elections the incumbent in w on 91.6 % of the time and in 2010 they won 84% of the time. By using this parameter that the incumbent wins about 9 times out of ten any random member of the public could walk off the street and predict 90% of US congressional elections at very little cost. A Livne, M Simmons, E Adar and L Adamic, à ¢Ã¢â€š ¬Ã…“The Party is over here structure and content in the 2010 Electionà ¢Ã¢â€š ¬Ã‚  used tweets sent by electoral candidates to build a model that was claimed would predict à ¢Ã¢â€š ¬Ã…“a candidate will win with accuracy of 88%.à ¢Ã¢â€š ¬Ã‚  Taken out of context this might seem strong but compared with the strike rate for using incumbency as the only parameter it seems a lot of work for little in the way of tangible results, or as à ¢Ã¢â€š ¬Ã…“How not to predict elections put ità ¢Ã¢â€š ¬Ã‚  à ¢Ã¢â€š ¬Ã…“even when predictions were better than chance they were not competent when compared to the trivial method of predicting through incumbencyà ¢Ã¢â€š ¬Ã‚ . T amasjan et al who carried similar work out in Germany found that twitter is used to spread political opinion discuss politics and that sentiment profiles of politicians and parties reflect nuances of the election campaign and that the mere volume of messages reflects the election result and à ¢Ã¢â€š ¬Ã…“even comes close to traditional election polesà ¢Ã¢â€š ¬Ã‚ . It seems as if pollsters have nothing to worry about in terms of employment. A major issue with social media data where general elections are concerned is that the people tweeting cannot be identified as likely voters. To identify likely voters a correct sample from Twitter would have to should be able to identify the age range, voting eligibility and prior voting patterns (How not to predict elections). Obtaining this information is not possible without violating the privacy of the users, a particularly hot topic of debate for social media providers at the moment. There are certainly voters who do not tweet and giv en the age range of likely voters in the US (in 2000 36% of citizens aged between 18 and 24 voted, 50% of citizens between 25 and 50 voted and 68% of those over 35 voted) while we have no supporting information weà ¢Ã¢â€š ¬Ã¢â€ž ¢ll put our reputations on the line and say as age increases in todays population the proportion of social media users probably declines while the exact opposite happens to the proportion of likely voters as age increases. This cannot be good for the accuracy of election prediction by data gathered from social media. It should also be noted that it is easy to manipulate social media data. Far be it from me to suggest that politicians are capable of sucas this headline and exerpt from the Technology Review June 2012 demonstrates there are those who will stop at nothing to win. à ¢Ã¢â€š ¬Ã…“Twitter Mischief Plagues Mexicoà ¢Ã¢â€š ¬Ã¢â€ž ¢s General Election, The top contenders in Mexicoà ¢Ã¢â€š ¬Ã¢â€ž ¢s presidential campaign are engaged in a Twitter s pam war, with armies of à ¢Ã¢â€š ¬Ã…“botsà ¢Ã¢â€š ¬Ã‚  programmed to cast aspersions on opposing candidates and disrupt their social-media efforts. This large-scale political spamming could foreshadow online antics that campaigners may increasingly resort to in other countries.à ¢Ã¢â€š ¬Ã‚  Flu Trends: Google Flu Trends (GFT) was launched in November 2008 and is based on the fact that Google users regularly use google to search for advice on health issues. By analysing the search terms from users Google attempts to predict flu trends. The Swine Flu pandemic of 2009 provided the first opportunity to evaluate the performance of GFT models during a non-seasonal influenza outbreak. GFT missed it. As well as this GFT overestimated the prevalence of flu in the 2012à ¢Ã¢â€š ¬Ã¢â‚¬Å"2013 season and overshot the actual level in 2011à ¢Ã¢â€š ¬Ã¢â‚¬Å"2012 by more than 50%. From 21 August 2011 to 1 September 2013, GFT reported overly high flu prevalence 100 out of 108 weeks (T he Parable of Google Flu Trends). In February 2013, Google Flu Trends (GFT) made headlines but not for a reason that Google Executives or the creator of the flu tracking system (The parable of the Google Flu Trends). Nature reported that GFT was predicting more than double the proportion of doctor visits for influenza-like illness than the Centers for Disease Control and Prevention (CDC), which bases its estimates on surveillance reports from laboratories across the United States (D. Butler, Nature 494, 155 (2013) D. R. Olson et al., PLOS Comput. Biol. 9, e1003256 (2013)). This happened despite the fact that GFT was built to predict CDC reports (Parable of Google Flu Trends). In the Parable of Google Flu Trends, Lazer et al refer to à ¢Ã¢â€š ¬Ã…“Big Data Hubrisà ¢Ã¢â€š ¬Ã‚  as being the implicit assumption that Big Data are a substitute for rather than a supplement to traditional data collection and go on to highlight that quantity alone does not mean one can ignore the foundational issues such as measurement, construct validity and dependencies among data. Like in the previous section on elections it seems that data gathered through social media does not yet compare to the tried and tested methods. Lazer et al took GFTà ¢Ã¢â€š ¬Ã¢â€ž ¢s main problem appears to be that it relies on the public to know what the symptoms of the flu are. If someone googles flu symptoms they may just have a cold. For conclusion: While people tweeting, expressing an opinion or searching about a product or a movie are more than likely the target market and a good indication of a future purchase the same cannot be said of elections. Where the customer has self selected as a customer a voter has not.

The Government of Spain - 1094 Words

The Government of Spain Political System Spain is a parliamentary monarchy. Chief of state is the hereditary monarch and the head of government is the President of the Government. The President designates the cabinet which is called the Council of Ministers. Spain is also has a bicameral legislative branch. The General Courts or National Assembly or Las Cortes Generales consists of the Senate or Senado and the Congress of Deputies or Congreso de los Diputados (CIA World Factbook). In order for legislation to pass the two chambers must agree. Proposals of laws issued by the Senate are discussed at Congress in a Plenary Session in order to be accepted or tabled veto or be amended. The proposal of the law passes to the study of†¦show more content†¦President of Government Rodriguez Zapatero is from the party Spanish Socialist Workers Party. The President s party does not have a majority of the seats in both chambers of parliament. 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Influencing and Decision Making

Question: Discuss about the Influencing and Decision Making. Answer: As the job offered is of a senior position, the initial gut instinct to this call will be positive, due to the growth perspective and innovation. The leadership position has a lot of potential. The general response should also be based on the qualifications and relevant work experience needed for the job. The job experience and academic background should be in line with the required criteria for selection. Although the offered role would be different, prior to experiences, an appointment with the HR should be made to clarify all the details. For an individual, job or employment is the source of his/her income and economic stability. Therefore, the employment related decisions should be based on the economic benefits and financial security aspects. Therefore, the Behavioral Economic Model can be regarded as the most appropriate model for the employment related decision making process. Behavioral economics use to observe the impacts of social, emotional and psychological factors on the economical decisions of a person or an institution. It helps to ascertain the decision on the basis of human psychology, macroeconomic elements and neuroscience. The model clarifies how an individual analyze any financial decision by the various economical factors and in accordance to his own psychology. As discussed above, employment or job is the main financial foundation of an individual. In this context, Behavioral economic model is more effective from other models, as it uses to give more focus on those economic factors, which are more relevant for the situation rather than other common economic and social features. Thus, the outcomes of the job related decision-making would be more realistic and suitable for the individual by applying economic behavioral model. According to the researchers of behavioral economic theory, it has been observed that regarding to the employer selection, an individual uses to be more concern on the fixed remuneration and other cost benefits. He/she also wish have knowledge about the employing company, its financial status and feedbacks of the existing employees or ex-employees about the financial stability of the company and also about the job security in that particular company. The individual also gives focus on the his/her future financial growth in that institution. The individual may also compare the job with any other job offers. After selecting the appropriate criteria and rating those criteria as per the alternatives, the individual can evaluate the prosperity of the job. Now, it depends on the individuals psychology on which alternative he/she will give more focus. Worksheet for Decision Matrix Analysis:- Worksheet for Hierarchy Analysis:-