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2011, International Journal of Advanced Computer Science and Applications
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5 pages
1 file
In this paper we present an approach for extracting multiple connections or links between subject and object from natural language input (English), which can have one or more than one subject, predicate and object.
1995
Natural Language Processing (NLP) systems usually require large amounts of pre-coded domain knowledge to perform semantic analysis automatically. Until repositories of such background knowledge are widely available, these systems may not scale up to non-trivial applications of NLP. This paper describes the design and implementation of a system that uses surface-syntactic information to interpret interactively semantic relationships between clauses. English technical texts are analyzed by a domain-independent parser that produces detailed parse trees of the input. The system then examines clausal connectives and syntactic verb phrase features to determine what kinds of semantic relationships exist between clauses. The results of this activity are used in a large Knowledge Acquisition system that, by design, requires little a priori semantic knowledge. We present a set of semantic labels appropriate to syntactically connected clauses (Clause-Level Relationships) and a description of the theory behind assigning these labels to particular inputs (Clause-Level Relationship Analysis). We also discuss elements of the implementation of the Clause-Level Relationship Analyzer and look at its performance.
Lecture-38 Parsing Algorithms In the last lecture, we started parsing, which is also called syntactic processing. Parsing we said was probably one of the most well understood areas of language processing, ((Refer Time: 00:36)) of algorithms have been designed for obtaining the structure of a sentence, and this phrases very important, because from the parse tree one then moves to the stage of semantic processing, where the semantic roles, disambiguated words, disambiguated names, co references, all these difficult problems are solved. However, the first crucial step to all these challenging tasks is syntactic processing or parsing. So, you would like to take a detailed look at, how parsing is done, today's topic will be parsing algorithms. Last time, we described top down parsing in detail, will mention this briefly then moved to bottom-up parsing, and a very famous algorithm called chart parsing, top-down bottom-up chart parsing. Then, we will discussed, what happens when a sentence is ambiguous, multiple parses as possible, for such a sentence. We described last time, that parsing in it required, even though the meaning is more or less understood based on the word senses, and their arrangements, it is still a critical task to obtain the parse tree of the sentence, that itself resolves a large amount of ambiguity. For example, if we take the sentence, I saw the boy with a ponytail, now with a ponytail should be attached to the boy, because it is a qualifier, for the boy, and when we do this parse tree construction, the tree would reveal, that the whole preposition face, with the ponytail has these, attachment with the boy. At this stage of syntactic processing, it is possible to find these, attachment now, we also said that, the parsing is again a critical requirement; we took a look at one replacement test. So, I want a white horse, he wants a brown one, so this is known as the one replacement phenomena in language processing, one has a enough for reference to horse. So, these kinds of phenomena need deep parse trees, we have to know the structure of sentence, in pretty good detail. So, parsing is definitely necessary, we now proceed to, the algorithms for parsing, starting with the slides.
2012 International Symposium on Innovations in Intelligent Systems and Applications, 2012
Artificial Intelligence is one of the key concepts of today's technology. As it is known, AI's aim is to developing technology that can learn by itself. Also, Natural Language Processing is another key concept as a significant contributor to AI in the field of natural languages. Considering the AI and NLP together brings us to teach computers to learn on their own about the natural languages and human derived words with their relationship. This paper aims to transfer a considerable amount of information to computers' world by presenting a way to extract Subject-Object-Verb relation extraction from Turkish documents automatically. Through three main steps the goal is achieved: (1) morphological analysis, (2) dependency analysis, (3) triplet extraction. As a result, an independent triplets graph can be generated for each text input, and verbs-nouns relation can be viewed.
One of the primary goals of biological NLP, and a prerequisite for practical text mining, is automatic information e x t r a c t i o n. This refers to the process of translating a human-readable corpus into structured data for visualization, querying and mining, or indeed any other computational process. Although a rich syntactic representation of a sentence is not necessarily required for IE, this paper presents an experiment to test the hypothesis that using dependency graphs as an intermediate stage can facilitate the extraction of biological interaction from parse trees and provide a powerful and flexible pipeline from raw text to semantic relations. The experiment was initially performed in three parts, with three different solutions to the same problem. Each one was developed after the previous had been evaluated and all new results had been analyzed, so I will present them chronologically, after describing the LLL Challenge (Learning Language in Logic) which provided the opportunity.
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R1a-M420 is one of the most widely spread Y-chromosome haplogroups; however, its substructure within Europe and Asia has remained poorly characterized. Using a panel of 16 244 male subjects from 126 populations sampled across Eurasia, we identified 2923 R1a-M420 Y-chromosomes and analyzed them to a highly granular phylogeographic resolution. Whole Y-chromosome sequence analysis of eight R1a and five R1b individuals suggests a divergence time of B25 000 (95% CI: 21 300-29 000) years ago and a coalescence time within R1a-M417 of B5800 (95% CI: 4800-6800) years. The spatial frequency distributions of R1a sub-haplogroups conclusively indicate two major groups, one found primarily in Europe and the other confined to Central and South Asia. Beyond the major European versus Asian dichotomy, we describe several younger sub-haplogroups. Based on spatial distributions and diversity patterns within the R1a-M420 clade, particularly rare basal branches detected primarily within Iran and eastern Turkey, we conclude that the initial episodes of haplogroup R1a diversification likely occurred in the vicinity of present-day Iran.
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The author[s] declare that this article is their own work and to the best of their knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the published of any other published materials, except where due acknowledgement is made in the article.
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