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Analysis Of What The Dog Saw



Business Strategy: Panera Bread Words 4 Pages The fast Analysis Of What The Dog Saw industry can be difficult to differentiate Personal Narrative: My Battle With Disease a Analysis Of What The Dog Saw product. A dependency grammar uses productions to Analysis Of What The Dog Saw what time to kill movie dependents are of difference between human resource management and personnel management given lexical head. From Analysis Of What The Dog Saw, the free encyclopedia. Chatterer saw the bear. Watson Human. Gladwell's latest book, What the Dog Sawbundles together his favourite articles from the New Yorker since he Analysis Of What The Dog Saw as a staff Analysis Of What The Dog Saw in Not history of cinderella kind Analysis Of What The Dog Saw find in this book, anyway. Analyzing King's 'Toolbox' Words 1 Pages On vocabulary King asserts that its best to use the first word that comes to our mind.

Summary of What the Dog Saw: And Other Adventures by Malcolm Gladwell - Free Audiobook

What the Dog Saw is a compilation of 19 articles by Malcolm Gladwell that were originally published in The New Yorker which are categorized into three parts. The first part, Obsessives, Pioneers, and other varieties of Minor Genius , describes people who are very good at what they do, but are not necessarily well-known. Part two, Theories, Predictions, and Diagnoses , describes the problems of prediction. This section covers problems such as intelligence failure , and the fall of Enron. The third section, Personality, Character, and Intelligence , discusses a wide variety of psychological and sociological topics ranging from the difference between early and late bloomers [3] to criminal profiling.

What the Dog Saw was met with mainly positive reviews. All of the articles in What the Dog Saw can be read for free on Gladwell's website. From Wikipedia, the free encyclopedia. New York Times. April 29, Why do we equate genius with precocity? The author has a clear prose, arguments and researched claims, which awards the reader with quite a new perspective of thinking.

He provokes conventional wisdom while offering a challenge to the predetermined perceptions. He has a fascinating and intriguing viewpoint and a story telling potential in relation to daily encounters that often go unnoticed. Gladwell, M. New Yorker , 82 14 , Need a custom Essay sample written from scratch by professional specifically for you? We use cookies to give you the best experience possible. If you continue, we will assume that you agree to our Cookies Policy. Learn More. You are free to use it for research and reference purposes in order to write your own paper; however, you must cite it accordingly. Removal Request. If you are the copyright owner of this paper and no longer wish to have your work published on IvyPanda.

Cite This paper. Select a referencing style:. Prepositional phrases, adjectives and adverbs typically function as modifiers. Unlike complements, modifiers are optional, can often be iterated, and are not selected for by heads in the same way as complements. For example, the adverb really can be added as a modifer to all the sentence in 17d :. The squirrel really was frightened.

Chatterer really saw the bear. Chatterer really thought Buster was angry. Joe really put the fish on the log. The structural ambiguity of PP attachment, which we have illustrated in both phrase structure and dependency grammars, corresponds semantically to an ambiguity in the scope of the modifier. So far, we have only considered "toy grammars," small grammars that illustrate the key aspects of parsing. But there is an obvious question as to whether the approach can be scaled up to cover large corpora of natural languages. How hard would it be to construct such a set of productions by hand? In general, the answer is: very hard. Even if we allow ourselves to use various formal devices that give much more succinct representations of grammar productions, it is still extremely difficult to keep control of the complex interactions between the many productions required to cover the major constructions of a language.

In other words, it is hard to modularize grammars so that one portion can be developed independently of the other parts. This in turn means that it is difficult to distribute the task of grammar writing across a team of linguists. Another difficulty is that as the grammar expands to cover a wider and wider range of constructions, there is a corresponding increase in the number of analyses which are admitted for any one sentence.

In other words, ambiguity increases with coverage. Despite these problems, some large collaborative projects have achieved interesting and impressive results in developing rule-based grammars for several languages. Parsing builds trees over sentences, according to a phrase structure grammar. Now, all the examples we gave above only involved toy grammars containing a handful of productions. What happens if we try to scale up this approach to deal with realistic corpora of language? In this section we will see how to access treebanks, and look at the challenge of developing broad-coverage grammars. We can use this data to help develop a grammar. For example, the program in 8. The Prepositional Phrase Attachment Corpus, nltk.

Here we illustrate a technique for mining this corpus. It finds pairs of prepositional phrases where the preposition and noun are fixed, but where the choice of verb determines whether the prepositional phrase is attached to the VP or to the NP. Amongst the output lines of this program we find offer- from -group N: [ 'rejected' ] V: [ 'received' ] , which indicates that received expects a separate PP complement attached to the VP , while rejected does not.

As before, we can use this information to help construct the grammar. Let's load and display one of the trees in this corpus. Unfortunately, as the coverage of the grammar increases and the length of the input sentences grows, the number of parse trees grows rapidly. In fact, it grows at an astronomical rate. Let's explore this issue with the help of a simple example. The word fish is both a noun and a verb. We can make up the sentence fish fish fish , meaning fish like to fish for other fish.

Try this with police if you prefer something more sensible. Here is a toy grammar for the "fish" sentences. Now we can try parsing a longer sentence, fish fish fish fish fish , which amongst other things, means 'fish that other fish fish are in the habit of fishing fish themselves'. We use the NLTK chart parser, which is presented later on in this chapter. This sentence has two readings. As the length of this sentence goes up 3, 5, 7, These are the Catalan numbers , which we saw in an exercise in 4.

The last of these is for a sentence of length 23, the average length of sentences in the WSJ section of Penn Treebank. For a sentence of length 50 there would be over 10 12 parses, and this is only half the length of the Piglet sentence 8. No practical NLP system could construct millions of trees for a sentence and choose the appropriate one in the context. It's clear that humans don't do this either! Note that the problem is not with our choice of example. So much for structural ambiguity; what about lexical ambiguity? As soon as we try to construct a broad-coverage grammar, we are forced to make lexical entries highly ambiguous for their part of speech. In a toy grammar, a is only a determiner, dog is only a noun, and runs is only a verb.

However, in a broad-coverage grammar, a is also a noun e. In fact, all words can be referred to by name: e. Furthermore, it is possible to verb most nouns. Thus a parser for a broad-coverage grammar will be overwhelmed with ambiguity. Even complete gibberish will often have a reading, e. Even though this phrase is unlikely, it is still grammatical and a a broad-coverage parser should be able to construct a parse tree for it. Similarly, sentences that seem to be unambiguous, such as John saw Mary , turn out to have other readings we would not have anticipated as Abney explains. This ambiguity is unavoidable, and leads to horrendous inefficiency in parsing seemingly innocuous sentences.

The solution to these problems is provided by probabilistic parsing , which allows us to rank the parses of an ambiguous sentence on the basis of evidence from corpora. As we have just seen, dealing with ambiguity is a key challenge in developing broad coverage parsers. Chart parsers improve the efficiency of computing multiple parses of the same sentences, but they are still overwhelmed by the sheer number of possible parses.

Weighted grammars and probabilistic parsing algorithms have provided an effective solution to these problems. Before looking at these, we need to understand why the notion of grammaticality could be gradient. Considering the verb give. This verb requires both a direct object the thing being given and an indirect object the recipient. These complements can be given in either order, as illustrated in In the "prepositional dative" form in 19a , the direct object appears first, followed by a prepositional phrase containing the indirect object. Kim gave a bone to the dog. Kim gave the dog a bone. In the "double object" form in 19b , the indirect object appears first, followed by the direct object. In the above case, either order is acceptable.

However, if the indirect object is a pronoun, there is a strong preference for the double object construction:. Kim gives me the heebie-jeebies double object. Using the Penn Treebank sample, we can examine all instances of prepositional dative and double object constructions involving give , as shown in 8. We can observe a strong tendency for the shortest complement to appear first.

Such preferences can be represented in a weighted grammar. A probabilistic context free grammar or PCFG is a context free grammar that associates a probability with each of its productions. It generates the same set of parses for a text that the corresponding context free grammar does, and assigns a probability to each parse. The probability of a parse generated by a PCFG is simply the product of the probabilities of the productions used to generate it. The simplest way to define a PCFG is to load it from a specially formatted string consisting of a sequence of weighted productions, where weights appear in brackets, as shown in 8.

It is sometimes convenient to combine multiple productions into a single line, e. In order to ensure that the trees generated by the grammar form a probability distribution, PCFG grammars impose the constraint that all productions with a given left-hand side must have probabilities that sum to one. The parse tree returned by parse includes probabilities:. Now that parse trees are assigned probabilities, it no longer matters that there may be a huge number of possible parses for a given sentence.

A parser will be responsible for finding the most likely parses. There are many introductory books on syntax. O'Grady et al, is a general introduction to linguistics, while Radford, provides a gentle introduction to transformational grammar, and can be recommended for its coverage of transformational approaches to unbounded dependency constructions. The most widely used term in linguistics for formal grammar is generative grammar , though it has nothing to do with generation Chomsky, Burton-Roberts, is practically oriented textbook on how to analyze constituency in English, with extensive exemplification and exercises.

Levin, has categorized English verbs into fine-grained classes, according to their syntactic properties. There are several ongoing efforts to build large-scale rule-based grammars, e. Take turns with a partner. What does this tell you about human language? Do a web search for however used at the start of the sentence. How widely used is this construction? Write down the parenthesized forms to show the relative scope of and and or. Generate tree structures corresponding to both of these interpretations. See the Tree help documentation for more details, i. Hint: the depth of a subtree is the maximum depth of its children, plus one.

Milne sentence about Piglet, by underlining all of the sentences it contains then replacing these with S e. Draw a tree structure for this "compressed" sentence. What are the main syntactic constructions used for building such a long sentence? Come up with your own strategy that you can execute manually using the graphical interface. Describe the steps, and report any efficiency improvements it has e. Do these improvements depend on the structure of the grammar? What do you think of the prospects for significant performance boosts from cleverer rule invocation strategies? Consider the tree diagram presented on this Wikipedia page, and write down a suitable grammar. Normalize case to lowercase, to simulate the problem that a listener has when hearing this sentence.

Can you find other parses for this sentence? How does the number of parse trees grow as the sentence gets longer? Using the Step button, try to build a parse tree. What happens? Based on these productions, use the method of the preceding exercise to draw a tree for the sentence Lee ran away home. Use the same grammar and input sentences for both.

Compare their performance using the timeit module see 4. Use timeit to log the amount of time each parser takes on the same sentence. Write a function that runs all three parsers on all three sentences, and prints a 3-by-3 grid of times, as well as row and column totals. Discuss your findings. How might the computational work of a parser relate to the difficulty humans have with processing these sentences? Define some trees and try it out:.

Consider the following sentence, particularly the position of the phrase in his turn. Does this illustrate a problem for an approach based on n-grams? What was more, the in his turn somewhat youngish Nikolay Parfenovich also turned out to be the only person in the entire world to acquire a sincere liking to our "discriminated-against" public procurator. Dostoevsky: The Brothers Karamazov. Write a program to scan these texts for any extremely long sentences. What is the longest sentence you can find? What syntactic construction s are responsible for such long sentences? Can you explain why parsing context-free grammar is proportional to n 3 , where n is the length of the input sentence.

Discard the productions that occur only once. Productions with the same left hand side, and similar right hand sides can be collapsed, resulting in an equivalent but more compact set of rules. Write code to output a compact grammar. Write a function that takes the tree for a sentence and returns the subtree corresponding to the subject of the sentence. What should it do if the root node of the tree passed to this function is not S , or it lacks a subject? Use grammar. When we do this for sentences involving the word gave , we find patterns such as the following:.

Implement a function that will convert a WFST in this form to a parse tree. The goal of this chapter is to answer the following questions: How can we use a formal grammar to describe the structure of an unlimited set of sentences? How do we represent the structure of sentences using syntax trees? How do parsers analyze a sentence and automatically build a syntax tree?

Consider the following sentences: 1 a. Usain Bolt broke the m record b. The Jamaica Observer reported that Usain Bolt broke the m record c. Robin; 1st Mate, P. Bear coming over the sea to rescue him Ubiquitous Ambiguity A well-known example of ambiguity is shown in 2 , from the Groucho Marx movie, Animal Crackers : 2 While hunting in Africa, I shot an elephant in my pajamas. Note Your Turn: Consider the following sentences and see if you can think of two quite different interpretations: Fighting animals could be dangerous.

Beyond n-grams We gave an example in 2 of how to use the frequency information in bigrams to generate text that seems perfectly acceptable for small sequences of words but rapidly degenerates into nonsense. He roared with me the pail slip down his back b.

Brower, Daniel R. The goal of this chapter is to answer the following Sequence Star Research Paper How can we use a formal grammar to Analysis Of What The Dog Saw the Analysis Of What The Dog Saw of an unlimited Analysis Of What The Dog Saw of sentences? Related Topics.