Publication Routing. The core issue in achieving scalability for streaming short textmatching within a data center environment is the routing of publica- tions and placement of subscriptions to the machines where the matching is to be performed. Previous at- tempts at this have been limited to publication unicast – subscription broadcast, publication broadcast – sub- scription unicast, or a combination of these two fun- damental approaches . However, in order to achieve good scalability as the workload (and thus the number of machines) increases, we need to avoid any kind of broadcast. To address this challenge, we take advan- tage of the problem domain. In particular, the word based publications and subscriptions in micro-blogging enable us to apply hashing to multicast (as opposed to broadcast) publications to the machines responsible for matching the words they contain. This way, subscrip- tions can be placed on any one of the machines that are responsible for one of the words forming the sub- scription. However, this brings an additional challenge, which is to minimize the number of machines a publica- tion is multicast to, which we refer to as the spread. To address this challenge, we develop effective word par- titioning algorithms (which replace the hashing based partitioning) that keep the spread low.
Bilingual Text, Matching using Bilingual Dictionary and Statistics Bilingual Text, Matching using Bilingual Dictionary and Statistics Takehito Utsuro t IIiroshi Ikeda ~ Masaya Yamane* Yuji M a t s u m[.]
As we can see in Figure 2a, contextual features, represented by the output of the encoder, are indis- pensable when predicting entailment. These fea- tures connect with the first stage of textmatching. The sequence encoder, implemented by convolu- tional networks, models local and phrase-level se- mantics, which helps to build correct alignment for each position. For example, consider the pair “A red car is next to a green house” and “A red car is parked near a house”. If the noun phrases in the two sentences are not correctly modeled by the contextual encoding and “green” is incorrectly aligned with another color word “red”, the pair looks much less like entailment.
The freestanding o kshops e e odelled o a o se sus o fe e e app oa h Cu eto : a cumulative process whereby participants are presented with a prompt (task or information) for discussion in the whole group before the next prompt is introduced. Because we expected larger numbers of participants than typical for a consensus conference, we invited students to respond to each prompt individually via the virtual learning environment (VLE) survey tool. Comments were then anonymously displayed to facilitate further whole-group discussion. The sequence of workshop activities is outlined in Figure 1. Raw data from the workshop surveys (mainly in the form of free- text responses) were independently analysed by the two researchers to establish themes and sub- themes. Once these were agreed, the researchers independently coded each response. Salient quotations from the surveys were used to guide the semi-structured interviews.
In this study to evaluate the student answer Natural Language Processing (NLP) algorithm and Artificial Neural Networks are used. The process starts by first staff creates answer sheet and keyword dataset for the examination process. These dataset stored in data storage and students enter their answer in the examination page. Once the student has submitted an answer text, the system will automatically calculate result using two algorithms of NLP and ANN. Before this evaluation process the pre-processing technique in undergone for the answer. Here we used Artificial Neural Networks algorithm for the normal answer comparison and stores mark for this in database and also evaluates the same answer using Natural language processing [NLP] algorithm to check grammar mistakes and stores the marks for this in database. Some basic linguistic analysis is performed in a natural language parser is respectively used to perform POS tagging of the student’s answer text. After linguistic analysis, the student’s answer text is processed by the artificial neural networks algorithm it will compares the student’s answer text with the staff answer and with keywords.
Text sequence matching is a task whose objec- tive is to predict the degree of match between two or more text sequences. For example, in natu- ral language inference, a system has to infer the relationship between a premise and a hypothesis sentence, and in paraphrase identification a sys- tem should find out whether a sentence is a para- phrase of the other. Since various natural language processing problems, including answer sentence selection, text retrieval, and machine comprehen- sion, involve text sequence matching components, building a high-performance textmatching model plays a key role in enhancing quality of systems for these problems (Tan et al., 2016; Rajpurkar et al., 2016; Wang and Jiang, 2017; Tymoshenko and Moschitti, 2018).
In this paper we try to implement the two way communication bridge i.e. dumb can speak using gestures and deaf can understand what the normal person is trying to say. The application combines two lead modules. One half of the application implicates conversion of gestures into its corresponding text and then reading the same aloud via speech api. This enables a dumb person to put forward his views. On the other hand the a deaf can understand a normal person using the other half of the application where the speech is converted to text and then the textmatching the database shows corresponding gestures or images stored in the database.
See the Select Graphic Rendition (SGR) section in the documentation of your text terminal for permitted values and their meaning as character attributes. These substring values are integers in decimal representation and can be con- catenated with semicolons. grep takes care of assembling the result into a com- plete SGR sequence (‘\33[’...‘m’). Common values to concatenate include ‘1’ for bold, ‘4’ for underline, ‘5’ for blink, ‘7’ for inverse, ‘39’ for default foreground color, ‘30’ to ‘37’ for foreground colors, ‘90’ to ‘97’ for 16-color mode foreground colors, ‘38;5;0’ to ‘38;5;255’ for 88-color and 256-color modes foreground col- ors, ‘49’ for default background color, ‘40’ to ‘47’ for background colors, ‘100’ to ‘107’ for 16-color mode background colors, and ‘48;5;0’ to ‘48;5;255’ for 88-color and 256-color modes background colors.
LEXICO SEMANTIC PATTERN MATCHING AS A COMPANION TO PARSING IN TEXT UNDERSTANDING L E X I C O S E M A N T I C P A T T E R N M A T C H I N G A S A C O M P A N I O N T O P A R S I N G I N T E X T U N D E[.]
Abstract - Now days there are different methods are present for evaluation of features of images, but there is no common effort developed for the comparison of the quality of image. Here we highlight the similarity based matching technique which will help in matching of the images. This method involves the comparison of points in one image to other, which reduces the complex algorithm implementation. The frames are extracted 1per/sec and frames are taken into a folder for reference match. Key image defines the image to be matched along frames extracted from videos. The image with the high similarity index is displayed as matched image. This would help in analyzing the image in greater depth.
Another challenge in bacteria categorization is that a bacteria species can have numerous sub- types, each corresponding to a different category in the taxonomy. This makes it hard to match a bacteria mention in text with its correspond- ing category in the taxonomy. Special rules are designed by analyzing the provided training and development data to enhance matching in such cases. For example, the word “type” is removed from a bacteria mention in text before match- ing against the NCBI Taxonomy. This enables matching “Escherichia coli type a” in text with “Escherichia coli a” in the taxonomy. In cases where sub-types are denoted with semi-colon, the sub-string following the semi-colon in the bacte- ria mention is removed before matching with the terms in the taxonomy. This allows “Escherichia Coli O8:K88” in text to match with the category “Escherichia Coli O8” in the taxonomy. Other transformations performed to enhance sub-type matching are converting the “ssp” abbreviation to “subsp.” and the “ara+” sub-string to “ara+ bio- type” in the bacteria mentions in text. We did not remove these sub-species denoting abbreviations, since keeping them resulted in better performance. We converted these abbreviations to their versions occurring in the names tagged as “scientific name” or “authority” in the taxonomy.
In general, a text attribute transfer system must be able to: (1) produce sentences that conform to the target attribute, (2) preserve the source sen- tence’s content, and (3) generate fluent language. Satisfying these requirements is challenging due to the typical absence of supervised parallel cor- pora exemplifying the desired attribute transfor- mations. With access to only non-parallel data, most existing approaches aim to tease apart con- tent and attribute in a latent sentence representa- tion. For instance, Shen et al. (2017) and Fu et al. (2018) utilize Generative Adversarial Networks (GANs) to learn this separation. Although these elaborate models can produce outputs that con- form to the target attribute (requirement 1), their sentences are fraught with errors in terms of both content preservation and linguistic fluency. This is exemplified in Table 1, where the model of Shen
The main purpose of this work is to construct a framework for event matching in temporal database. Also there were many existing works that are done on the event matching process in several databases. In fact, the framework for event matching in temporal database had been proposed with new few works. Bearing in mind the issues in the existing system, this work focuses on most sophisticated region on the event matching. With the emergence of new technologies, multimedia has gained more popularity among the users. So there comes the need to find new ways for retrieving those data. The retrieval procedure can be improved with the use of neural-genetic network and human’s assistance as described. This new framework is flexible and powerful for the design of effective event matching. The effectiveness results demonstrate that the framework can find better similarity functions than the ones obtained from the individual descriptors. Our experiments also demonstrate that the neuro-genetic framework yields better results than any other existing systems. Experimental results have shown that the performance of the proposed approach is appreciable.
Many important applications require that the execution time of string matching to be short. Hence, GPU accelerators are used to achieve this goal. The researchers in  developed a GPU based parallel implementation of the AC algorithm. Their solution carefully places and caches the input data and the pattern in the on-chip shared memories of the GPU. They implemented the AC algorithm as a Deterministic Finite Automata (DFA). The DFA represents all possible states of the machine along with information about the acceptable state transitions of the system. Input data is copied to the global memory and the state transition table is copied to the texture memory. The effective texture memory latencies for the random access of the state transition table are reduced because of the texture cache. Their approach suffers from the problem that patterns occurring at the boundaries of adjacent segments cannot be detected, as depicted in Figure 1. To overcome this problem, our approach uses overlapping as shown in Figure 2. This solution uses portions of the state machine on different threads. In , Tumeo and Villa used a technique that is similar to the one used in  with addition of GPU and message passing (MP) features. In this technique, they divided the state machine among a number of GPUs and then compared the performance with a system that
Pattern Matching in a Linguistically Motivated Text Understanding System Pattern Matching in a Linguistically Motivated Text Understanding System Damaris M Ayuso and the PLUM Research Group BBN System[.]
Early studies in this research is to enhance the performance of processing the digital non- text information (in our case study is fingerprint image) by introducing a parallel compression system. During the process we found out that the results based on the early studies has already been achieved by using a compression tools available with less hassle. Therefore, we divert our application development towards the unsolved research on fingerprint matching processes but still maintaining the same method of solution by doing a parallel processing for the data. Details of this research will be further explained throughout every modules in this report.
The system was evaluated on TAC 2014 Biomedi- cal Summarization track training dataset. It consists of 20 topics, each of which contains between 10 to 20 citing articles and 1 reference article. For each topic, four domain experts were asked to identify the appropriate reference spans for each citance in the reference text. To better understand the dataset, we analyzed the agreement between annotators (ta- ble 1). This table shows that the overall agreement is relatively low.
The key step for entity disambiguation is the similarity computation between mention-entity and entity-entity pairs. Early studies focused on modeling the similarity between local context that computes the similarity between mention con- text and relevant candidate entities (Bunescu and Pas¸ca, 2006; Mihalcea and Csomai, 2007). Re- cent state-of-the-art methods consider global co- herence that is the relatedness between all can- didate entities in the same document (Milne and Witten, 2008; Kulkarni et al., 2009; Ratinov et al., 2011). These methods depend on well-defined link structures as seen in Wikipedia to compute global coherence. After the emergence of word embeddings (Mikolov et al., 2013), it facilitates to produce more generalized coherence computa- tions without using hand-crafted features. Hence, the dependency of well-defined knowledge bases has decreased and knowledge base agnostic ap- proaches become revealed (Zwicklbauer et al., 2016). Most recent deep learning approaches have been presented as a way to support better general- ization for the similarity measurement of context, mention and entity (Sun et al., 2015). Also, men- tions and entities are combined into the same con- tinuous vector space for the entity disambiguation (Yamada et al., 2016). From a different perspec- tive, the entity disambiguation should be trans- formed into as a sequence learning task to capture more generalized semantics between candidate en- tities and also mentions.
Analysis of semantic similarity can be ap- proached from different angles. A basic approach is to use string similarity measures such as the Levenshtein distance or the Jaccard similarity co- efficient. Although cheap and fast, this fails to account for less obvious cases such as synonyms or syntactic paraphrasing. At the other extreme, we can perform a deep semantic analysis of two expressions and rely on formal reasoning to de- rive a logical relation between them. This ap- proach suffers from issues with coverage and ro- bustness commonly associated with deep linguis- tic processing. We therefore think that the middle ground between these two extremes offers the best option. In this paper we present a new method for analysing semantic similarity in comparable text. It relies on a combination of morphologi- cal and syntactic analysis, lexical resources such as word nets, and machine learning from exam- ples. We propose to analyse semantic similarity between sentences by aligning their syntax trees, where each node is matched to the most similar node in the other tree (if any). In addition, we label these alignments according to the type of similarity relation that holds between the aligned phrases. The labeling supports further processing. For instance, Marsi & Krahmer (2005b; 2008) de- scribe how to generate different types of sentence fusions on the basis of this relation labeling.
Automatic processing of multiword expressions in- cludes two distinct (but interlinked) tasks. Most of the effort has been put into acquisition of MWEs appearing in a particular text corpus into a lexi- con of MWEs (types) not necessarily linked with their occurrences (instances) in the text. The best- performing methods are usually based on lexical as- sociation measures that exploit statistical evidence of word occurrences and co-occurrences acquired from a corpus to determine degree of lexical asso- ciation between words (Pecina, 2005). Expressions that consist of words with high association are then