3.6 The Viterbi Algorithm (HMM)
The Viterbi algorithm analyzes English text. Probabilities are assigned to each word in the context of the sentence. A Hidden Markov Model for English syntax is used in which the probability of the word is dependent on the previous word or words. The probability of word or words followed by a word in the sentence the probability is calculated for bi-gram, tri-gram and 4-gram.Depending on the length of the sentence the probability is calculated for n-grams [1].
3.6.1 What are N-grams:
N-grams of text are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-gram we move forward. We can move X-words forward in more advanced scenarios. When N=1, it is referred as unigram, when N=2, it is bi-gram, when N=3, it is tri-gram. When N>3, it is 4-gram, 5-gram and so on.
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The Viterbi Algorithm estimates the sequence of states , that maximizes ) for a given sequence of observations (feature vectors) derived from the M-words in a sentence. The observations x0 and xM+1 as well as the states Z0 and ZM+1 designate the beginning and the end of the sentence.
From Bayes rule we obtain (1)
Since P(Z) is independent of the sequence maximized over all to discriminant function needs to be maximized over all sequences
Johnston, F., Bear, D., & Invernizzi, M. (2004). Words their way: Word sorts for letter
In library and information science controlled vocabulary is a carefully selected list of words and phrases, which are used to tag units of information (document or work) so that they may be more easily retrieved by a search.
Fraizer and Rayner (1982) put forward the garden-path model as a method of sentence processing, used when encountering ambiguous sentences. The model states that when a reader or listener comes across an ambiguous sentence only one syntactical structure is primarily considered. When reaching a key point in the sentence, if the meaning attributed does not work, we must backtrack and rebuild the structure of the sentence. After reparsing the sentence we can then arrive at the right explanation of the sentence (Harley, 2008). A vast amount of investigative research has been conducted to support the garden-path model and specifically the main principles it uses (Harley 2008). Studies looking at eye-movements (Ferreira & Clifton, 1986) and word-by-word self paced reading (Ferreira & Henderson, 1990) have also found evidence that we use the garden-path model of parsing when we come across ambiguous sentences. However since the garden-path model was first introduced, there has been a great amount of evidence that disputes the major claims the model adheres to. Studies using EEG data have found that we use world knowledge and word meaning very early in sentence processing, which strongly contradicts the distinct ideas of the garden-path model (Hagoort, Hald, Bastiaansen & Petersson, 2004). There are also many theories that state we parse difficult sentences in a different way to the one the garden-path model suggests. For example the constraint based theory suggests that we compute more than one syntactical solution at once, (MacDonald, Pearlmutter & Seidenburg, 1994) and the unrestricted race model, (Van Gompel, Pickering & Traxler, 2000) proposes that semantic information is also used in sentence processing. Therefore this essay will d...
Technologies like word prediction can compare a typed word with a word in the dictionary list and recognize a mismatch as a misspelled word. This helps the writers who unconsciously reorder the letters a lot while typing. It can also assist a writer in guessing the spelling of a word.
Abstract—Computational problems have significance from the early civilizations. These problems and solutions are used for the study of universe. Numbers and symbols have been used for different fields e.g. mathematics, statistics. After the emergence of computers the number and objects needs to be arranged in a particular order i.e. ascending and descending orders. The ordering of these numbers is generally referred to as sorting. Sorting gained a lot of importance in computer sciences and its applications are in file systems etc. A number of sorting algorithms have been proposed with different time and space complexities. In this paper author will propose a new sorting algorithm i.e. Relative Split and Concatenate Sort, implement the algorithm and then compared results with some of the existing sorting algorithms. Algorithm’s time and space complexity will also be the part of this paper.
Crookes (1986) pointed out that it may be due to one or a combination of the following reasons. First, the framework or move model used in the study may not adequately account for all the functions and purposes of all the texts in a corpus. Second, the coders may not be sufficiently trained. Third, some texts in the corpus may not fit or conform to the framework. Fourth, the descriptions of move and step and/or definitions within the framework may not be explicitly inadequate. Fifth, boundaries of moves and steps in the corpus cannot be consistently and unanimously identified and agreed. Sixth, lexicogrammatical signals used in the move and step identification may be misleading and/or unclear.
Imagine asking your computer to do something in the same way you would ask a friend to do it. Without having to memorize special commands that only it could understand. For computer scientists this has been an ambitious goal; that can further simplify computers. Artificial Intelligence, a system that can mimic human intelligence by performing task that usually only a human can do, usually has to use a form of natural language processing. Natural language processing, a sub-field of computer science and artificial intelligence, concerns the successfully interaction between a computer and a human. Currently one of the best examples of A.I.(Artificial Intelligence) is IBM 's Watson. A machine that gained popularity after appearing on the show
Schachter, Jacquelyn. Some semantic prerequisites for a model of language. Brain & Language. Vol 3(2) 292-304, Apr 1976.
Secure yet Unsecure, is an age that we live in. However, in today’s time of information and digital media, vulnerabilities will happen. Valuable assets cannot be hidden forever, nor can any locks prevent its theft. At some point, because of the vulnerabilities existing in our system, the secrets and our data will be accidentally revealed or actively discovered or unintentionally be given away by someone we trust. My passion to secure the cyber environment drives me intensely to research and delve deeper into the flaws and vulnerabilities and find their infallible remedies.
Co-hyponymy occurs when two or more lexical items used in a text as subordinate members of a superordinate class (Eggins, 2004). If class/sub-classes relation is between the general item and its specific items, co-hyponymy relation is between two or more specific items. To illustrate co-hyponymy, a simple example is taken from The Story of an Hour (Eggins, 2004).
Artificial neural networks are systems implemented on computer systems as specialized hardware or sophisticated software that loosely model the learning and remembering functions of the human brain. They are an attempt to simulate the multiple layers of processing elements in the brain, called neurons. These elements are implemented in such a way so that the layers can learn from prior experience and remember their outputs. In this way, the system can learn to recognize certain patterns and situations and apply these to certain priorities and output appropriate results. These types of neural networks can be used in many important situations such as priority in an emergency room, for financial assistance, and any type of pattern recognition such as handwritten or text-to-speech recognition.
Jurafsky, D. & Martin, J. H. (2009), Speech and Language Processing: International Version: an Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed, Pearson Education Inc, Upper Saddle River, New Jersey.
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Text linguistics is a “discipline which analyses the linguistic regularities and constitutive features of texts” (Bussmann, 1996: 1190). According to this definition, text linguistics is mainly concerned with studying the features that every piece of writing should have in order to be considered as a text. It is also defined by Noth (1977 in Al-Massri, 2013:33) as “the branch of linguistics in which the methods of linguistic analysis are extended to the level of text.” This means that text linguistics aims at producing rules and methods that can be used to analyze the whole text. This approach has been put forward by the two scholars Robert-Alain de Beaugrande and Wolfgang U. Dressler in their seminal book “Introduction to Text Linguistics”, in 1981. The study of texts in linguistic studies starts in
Syntax is the study of how words are combined to create phrases and causes in the sentences of a specific language (Freeman and Freeman, 2014). Syntax helps us to make clear sentences that “sound right,” where words, phrases, and clauses each serve their function and are correctly ordered to form and communicate a complete sentence with meaning. The rules of syntax combine words into phrases and phrases into sentences. Not only does it focus on the correct word order for a language, but it also helps show the relationship between the meaning of a group of words. Without proper syntax, a sentence can be meaningless. It is key to understand that while every language does have certain syntax, the syntax does vary from language to language. It