Content introduction chapter parsing in traditional English Sentence analysis chapter the Traditional scheme of sentences parsing in English 1 The Traditional scheme of sentences parsing in English conclusion summary references

CONCLUSION
I have gained a lot of information that I need and that is relevant to me during this course work. I also learned the following on the topic of my course work: The basic connection between a sentence and the grammar it derives from is the parse tree, which describes how the grammar was used to produce the sentence. For the reconstruction of this connection we need a parsing technique .Natural Language processing provides us with two basic parsing techniques viz; Top-Down and Bottom-Up. Their name describes the direction in which parsing process advances. We have a Basic-Top-Down parsing which is the fusion of top-down and bottom-up parsing.
“Using Top-Down technique, parser searches for a parse tree by trying to build from the root node S down to the leaves. The algorithm starts by assuming the input can be derived by the designated start symbol S. The next step is to find the tops of all the trees which can start with S, by looking on the grammar rules with S on left hand side, all the possible trees are generated .Top down parsing is a goal directed search

It tries to imitate the original production process by rederiving the sentence from the start symbol ,and the production tree is reconstructed from the top downwards .Top-Down parsing can be viewed as an expansion process which begins with starting symbol S, and then advances by replacing S with the Left hand side production. The common search strategy implemented in this approach is Top-Down, left-to-right and backtracking. The search starts from the root node labeled S i.e. starting symbol, construct the child nodes by applying the rules with left hand side equals to S, further expands the internal nodes using next productions with left hand side equals to internal node, if nonterminal, and continues until leaves are Part-of-speech (terminals).If the leaf nodes i.e. Part-of-speech do not matches the input string, we need to backtrack to the latest node processed and apply another production. Top-Down parsing is viewed as generation of parse tree in preorder.

The advantage of Top-Down strategy is that it never wastes time exploring trees that cannot result in S, means it also never explores subtrees that cannot find a place in some S-rooted tree.Considering the other side of this approach, it has its own demerits, it leads to backtracking. The Top-Down approach spends considerable effort and time on S trees that are not consistent with the input. This weakness in Top-Down parser arises from the fact that they can generate trees before examining the input[8][6].While expanding the nonterminals it becomes difficult to decide which Right hand side production should be selected i.e. to select the appropriate starting production and further productions to avoid backtracking.Predictive parsing is the solution for backtracking problem faced in top-Down Strategy. Predictive Parsing is characterized by its ability to use at most next (k) tokens to select which production to apply, referred to as lookahead .Making the right decision without backtracking . Sentences that comprise a single word are called word sentences, and the words themselves sentence words. The 1980s saw a renewed surge in interest in sentence length, primarily in relation to "other syntactic phenomena".
One definition of the average sentence length of a prose passage is the ratio of the number of words to the number of sentences. [unreliable source?] The textbook Mathematical linguistics, by András Kornai, suggests that in "journalistic prose the median sentence length is above 15 words". The average length of a sentence generally serves as a measure of sentence difficulty or complexity. In general, as the average sentence length increases, the complexity of the sentences also increases.
Another definition of "sentence length" is the number of clauses in the sentence, whereas the "clause length" is the number of phones in the clause.
“Research by Erik Schils and Pieter de Haan by sampling five texts showed that two adjacent sentences are more likely to have similar lengths than two non-adjacent sentences, and almost certainly have a similar length when in a work of fiction. This countered the theory that "authors may aim at an alternation of long and short sentences". Sentence length, as well as word difficulty, are both factors in the readability of a sentence; however, other factors, such as the presence of conjunctions, have been said to "facilitate comprehension considerably".”⁸

Summary
To parse this sentence, we first classify each word by its part of speech: the (article), man (noun), opened (verb), the (article), door (noun). The sentence has only one verb (opened); we can then identify the subject and object of that verb. In this case, since the man is performing the action, the subject is man and the object is door. Because the verb is opened—rather than opens or will open—we know that the sentence is in the past tense, meaning the action described has already occurred. This example is a simple one, but it shows how parsing can be used to illuminate the meaning of a text. Traditional methods of parsing may or may not include sentence diagrams. Such visual aids are sometimes helpful when the sentences being analyzed are especially complex.

Discourse Analysis
Unlike simple parsing, discourse analysis refers to a broader field of study concerned with the social and psychological aspects of language. Those who perform discourse analysis are interested in, among other topics, genres of language (those with certain set conventions within different fields) and the relationships between language and social behavior, politics, and memory. In this way, discourse analysis goes far beyond the scope of traditional parsing, which is limited to that individual texts.

REFERENCES

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   Masaru Tomita (6 December 2012). Generalized LR Parsing. Springer Science & Business Media. ISBN 978-1-4615-4034-2.
   
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   Christopher D.. Manning; Christopher D. Manning; Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. MIT Press. ISBN 978-0-262-13360-9.
   
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   Jurafsky, Daniel (1996). "A Probabilistic Model of Lexical and Syntactic Access and Disambiguation". Cognitive Science. 20 (2): 137–194. CiteSeerX 10.1.1.150.5711. doi:10.1207/s15516709cog2002_1.
   
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   Klein, Dan, and Christopher D. Manning. "Accurate unlexicalized parsing." Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003.
   
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   Charniak, Eugene. "A maximum-entropy-inspired parser." Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000.
   
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   Chen, Danqi, and Christopher Manning. "A fast and accurate dependency parser using neural networks." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.
   
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    Jia, Robin; Liang, Percy (2016-06-11). "Data Recombination for Neural Semantic Parsing". arXiv:1606.03622 [cs.CL].
    
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    Berant, Jonathan, and Percy Liang. "Semantic parsing via paraphrasing." Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2014.
    
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1^ "Parse". dictionary.reference.com. Retrieved 27 November 2010.

2^ Masaru Tomita (6 December 2012). Generalized LR Parsing. Springer Science & Business Media. ISBN 978-1-4615-4034-2

3^ Christopher D.. Manning; Christopher D. Manning; Hinrich Schütze (1999). Foundations of Statistical Natural Language Processing. MIT Press. ISBN 978-0-262-13360-9.

4^ Aho, A.V., Sethi, R. and Ullman, J.D. (1986) " Compilers: principles, techniques, and tools." Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA.

5^ Berant, Jonathan, and Percy Liang. "Semantic parsing via paraphrasing." Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

6^ Klein, Dan, and Christopher D. Manning. "Accurate unlexicalized parsing." Proceedings of the 41st Annual Meeting on Association for Computational Linguistics-Volume 1. Association for Computational Linguistics, 2003

7^ Charniak, Eugene. "A maximum-entropy-inspired parser." Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference. Association for Computational Linguistics, 2000.

88 Chen, Danqi, and Christopher Manning. "A fast and accurate dependency parser using neural networks." Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). 2014.