Search (63 results, page 1 of 4)

0.10851419 = product of:
  0.27128547 = sum of:
    0.23174728 = weight(_text_:3a in 562) [ClassicSimilarity], result of:
      0.23174728 = score(doc=562,freq=2.0), product of:
        0.41234848 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04863741 = queryNorm
        0.56201804 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
    0.039538182 = weight(_text_:22 in 562) [ClassicSimilarity], result of:
      0.039538182 = score(doc=562,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.23214069 = fieldWeight in 562, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.046875 = fieldNorm(doc=562)
  0.4 = coord(2/5)

Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

0.08376864 = product of:
  0.4188432 = sum of:
    0.4188432 = weight(_text_:grams in 6501) [ClassicSimilarity], result of:
      0.4188432 = score(doc=6501,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        1.0685225 = fieldWeight in 6501, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.09375 = fieldNorm(doc=6501)
  0.2 = coord(1/5)

0.06980721 = product of:
  0.34903604 = sum of:
    0.34903604 = weight(_text_:grams in 5206) [ClassicSimilarity], result of:
      0.34903604 = score(doc=5206,freq=8.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.89043546 = fieldWeight in 5206, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5206)
  0.2 = coord(1/5)

Abstract

Khoo, Dai, and Loh examine new statistical methods for the identification of two and three character words in Chinese text. Some meaningful Chinese words are simple (independent units of one or more characters in a sentence that have independent meaning) but others are compounds of two or more simple words. In their segmentation they utilize the Modern Chinese Word Segmentation for Application of Information Processing, with some modifications to focus on meaningful words to do manual segmentation. About 37% of meaningful words are longer than 2 characters indicating a need to handle three and four character words. Four hundred sentences from news articles were manually broken into overlapping bi-grams and tri-grams. Using logistic regression, the log of the odds that such bi/tri-grams were meaningful words was calculated. Variables like relative frequency, document frequency, local frequency, and contextual and positional information, were incorporated in the model only if the concordance measure improved by at least 2% with their addition. For two- and three-character words relative frequency of adjacent characters and document frequency of overlapping bi-grams were found to be significant. Using measures of recall and precision where correct automatic segmentation is normalized either by manual segmentation or by automatic segmentation, the contextual information formula for 2 character words provides significantly better results than previous formulations and using both the 2 and 3 character formulations in combination significantly improves the 2 character results.

0.06980721 = product of:
  0.34903604 = sum of:
    0.34903604 = weight(_text_:grams in 5310) [ClassicSimilarity], result of:
      0.34903604 = score(doc=5310,freq=8.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.89043546 = fieldWeight in 5310, product of:
          2.828427 = tf(freq=8.0), with freq of:
            8.0 = termFreq=8.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5310)
  0.2 = coord(1/5)

Abstract

The novelty of knowledge claims in a research paper can be considered an evaluation criterion for papers to supplement citations. To provide a foundation for research evaluation from the perspective of innovativeness, we propose an automatic approach for extracting innovative ideas from the abstracts of technology and engineering papers. The approach extracts N-grams as candidates based on part-of-speech tagging and determines whether they are novel by checking the Scopus® database to determine whether they had ever been presented previously. Moreover, we discussed the distributions of innovative ideas in different abstract structures. To improve the performance by excluding noisy N-grams, a list of stopwords and a list of research description characteristics were developed. We selected abstracts of articles published from 2011 to 2017 with the topic of semantic analysis as the experimental texts. Excluding noisy N-grams, considering the distribution of innovative ideas in abstracts, and suitably combining N-grams can effectively improve the performance of automatic innovative idea extraction. Unlike co-word and co-citation analysis, innovative-idea extraction aims to identify the differences in a paper from all previously published papers.

0.05923338 = product of:
  0.2961669 = sum of:
    0.2961669 = weight(_text_:grams in 2941) [ClassicSimilarity], result of:
      0.2961669 = score(doc=2941,freq=4.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.7555595 = fieldWeight in 2941, product of:
          2.0 = tf(freq=4.0), with freq of:
            4.0 = termFreq=4.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.046875 = fieldNorm(doc=2941)
  0.2 = coord(1/5)

Abstract

In this paper we present a language-independent approach for conflation that does not depend on predefined rules or prior knowledge of the target language. The proposed unsupervised method is based on an enhancement of the pure n-gram model that can group related words based on various string-similarity measures, while restricting the search to specific locations of the target word by taking into account the order of n-grams. We show that the method is effective to achieve high score similarities for all word-form variations and reduces the ambiguity, i.e., obtains a higher precision and recall, compared to pure n-gram-based approaches for English, Portuguese, and Arabic. The proposed method is especially suited for conflation approaches in Arabic, since Arabic is a highly inflectional language. Therefore, we present in addition an adaptive user interface for Arabic text retrieval called araSearch. araSearch serves as a metasearch interface to existing search engines. The system is able to extend a query using the proposed conflation approach such that additional results for relevant subwords can be found automatically.

Object

n-grams

0.048865046 = product of:
  0.24432522 = sum of:
    0.24432522 = weight(_text_:grams in 3922) [ClassicSimilarity], result of:
      0.24432522 = score(doc=3922,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.6233048 = fieldWeight in 3922, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0546875 = fieldNorm(doc=3922)
  0.2 = coord(1/5)

Abstract

WordHoard defines a multiword unit as a special type of collocate in which the component words comprise a meaningful phrase. For example, "Knight of the Round Table" is a meaningful multiword unit or phrase. WordHoard uses the notion of a pseudo-bigram to generalize the computation of bigram (two word) statistical measures to phrases (n-grams) longer than two words, and to allow comparisons of these measures for phrases with different word counts. WordHoard applies the localmaxs algorithm of Silva et al. to the pseudo-bigrams to identify potential compositional phrases that "stand out" in a text. WordHoard can also filter two and three word phrases using the word class filters suggested by Justeson and Katz.

0.048865046 = product of:
  0.24432522 = sum of:
    0.24432522 = weight(_text_:grams in 1123) [ClassicSimilarity], result of:
      0.24432522 = score(doc=1123,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.6233048 = fieldWeight in 1123, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0546875 = fieldNorm(doc=1123)
  0.2 = coord(1/5)

Abstract

WordHoard defines a multiword unit as a special type of collocate in which the component words comprise a meaningful phrase. For example, "Knight of the Round Table" is a meaningful multiword unit or phrase. WordHoard uses the notion of a pseudo-bigram to generalize the computation of bigram (two word) statistical measures to phrases (n-grams) longer than two words, and to allow comparisons of these measures for phrases with different word counts. WordHoard applies the localmaxs algorithm of Silva et al. to the pseudo-bigrams to identify potential compositional phrases that "stand out" in a text. WordHoard can also filter two and three word phrases using the word class filters suggested by Justeson and Katz.

0.04634946 = product of:
  0.23174728 = sum of:
    0.23174728 = weight(_text_:3a in 862) [ClassicSimilarity], result of:
      0.23174728 = score(doc=862,freq=2.0), product of:
        0.41234848 = queryWeight, product of:
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.04863741 = queryNorm
        0.56201804 = fieldWeight in 862, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.478011 = idf(docFreq=24, maxDocs=44218)
          0.046875 = fieldNorm(doc=862)
  0.2 = coord(1/5)

Source

https%3A%2F%2Farxiv.org%2Fabs%2F2212.06721&usg=AOvVaw3i_9pZm9y_dQWoHi6uv0EN

0.034903605 = product of:
  0.17451802 = sum of:
    0.17451802 = weight(_text_:grams in 4277) [ClassicSimilarity], result of:
      0.17451802 = score(doc=4277,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.44521773 = fieldWeight in 4277, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4277)
  0.2 = coord(1/5)

Abstract

This chapter reviews research and applications in statistical language modeling for information retrieval (IR), which has emerged within the past several years as a new probabilistic framework for describing information retrieval processes. Generally speaking, statistical language modeling, or more simply language modeling (LM), involves estimating a probability distribution that captures statistical regularities of natural language use. Applied to information retrieval, language modeling refers to the problem of estimating the likelihood that a query and a document could have been generated by the same language model, given the language model of the document either with or without a language model of the query. The roots of statistical language modeling date to the beginning of the twentieth century when Markov tried to model letter sequences in works of Russian literature (Manning & Schütze, 1999). Zipf (1929, 1932, 1949, 1965) studied the statistical properties of text and discovered that the frequency of works decays as a Power function of each works rank. However, it was Shannon's (1951) work that inspired later research in this area. In 1951, eager to explore the applications of his newly founded information theory to human language, Shannon used a prediction game involving n-grams to investigate the information content of English text. He evaluated n-gram models' performance by comparing their crossentropy an texts with the true entropy estimated using predictions made by human subjects. For many years, statistical language models have been used primarily for automatic speech recognition. Since 1980, when the first significant language model was proposed (Rosenfeld, 2000), statistical language modeling has become a fundamental component of speech recognition, machine translation, and spelling correction.

0.034903605 = product of:
  0.17451802 = sum of:
    0.17451802 = weight(_text_:grams in 3426) [ClassicSimilarity], result of:
      0.17451802 = score(doc=3426,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.44521773 = fieldWeight in 3426, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3426)
  0.2 = coord(1/5)

Abstract

Root extraction is one of the most important topics in information retrieval (IR), natural language processing (NLP), text summarization, and many other important fields. In the last two decades, several algorithms have been proposed to extract Arabic roots. Most of these algorithms dealt with triliteral roots only, and some with fixed length words only. In this study, a novel approach to the extraction of roots from Arabic words using bigrams is proposed. Two similarity measures are used, the dissimilarity measure called the Manhattan distance, and Dice's measure of similarity. The proposed algorithm is tested on the Holy Qu'ran and on a corpus of 242 abstracts from the Proceedings of the Saudi Arabian National Computer Conferences. The two files used contain a wide range of data: the Holy Qu'ran contains most of the ancient Arabic words while the other file contains some modern Arabic words and some words borrowed from foreign languages in addition to the original Arabic words. The results of this study showed that combining N-grams with the Dice measure gives better results than using the Manhattan distance measure.

0.034903605 = product of:
  0.17451802 = sum of:
    0.17451802 = weight(_text_:grams in 2688) [ClassicSimilarity], result of:
      0.17451802 = score(doc=2688,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.44521773 = fieldWeight in 2688, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=2688)
  0.2 = coord(1/5)

Object

n-grams

0.034903605 = product of:
  0.17451802 = sum of:
    0.17451802 = weight(_text_:grams in 3221) [ClassicSimilarity], result of:
      0.17451802 = score(doc=3221,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.44521773 = fieldWeight in 3221, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=3221)
  0.2 = coord(1/5)

Abstract

Traditionally, many information retrieval models assume that terms occur in documents independently. Although these models have already shown good performance, the word independency assumption seems to be unrealistic from a natural language point of view, which considers that terms are related to each other. Therefore, such an assumption leads to two well-known problems in information retrieval (IR), namely, polysemy, or term mismatch, and synonymy. In language models, these issues have been addressed by considering dependencies such as bigrams, phrasal-concepts, or word relationships, but such models are estimated using simple n-grams or concept counting. In this paper, we address polysemy and synonymy mismatch with a concept-based language modeling approach that combines ontological concepts from external resources with frequently found collocations from the document collection. In addition, the concept-based model is enriched with subconcepts and semantic relationships through a semantic smoothing technique so as to perform semantic matching. Experiments carried out on TREC collections show that our model achieves significant improvements over a single word-based model and the Markov Random Field model (using a Markov classifier).

0.034903605 = product of:
  0.17451802 = sum of:
    0.17451802 = weight(_text_:grams in 5043) [ClassicSimilarity], result of:
      0.17451802 = score(doc=5043,freq=2.0), product of:
        0.39198354 = queryWeight, product of:
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.04863741 = queryNorm
        0.44521773 = fieldWeight in 5043, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          8.059301 = idf(docFreq=37, maxDocs=44218)
          0.0390625 = fieldNorm(doc=5043)
  0.2 = coord(1/5)

Abstract

This paper is concerned with paraphrase detection, i.e., identifying sentences that are semantically identical. The ability to detect similar sentences written in natural language is crucial for several applications, such as text mining, text summarization, plagiarism detection, authorship authentication and question answering. Recognizing this importance, we study in particular how to address the challenges with detecting paraphrases in user generated short texts, such as Twitter, which often contain language irregularity and noise, and do not necessarily contain as much semantic information as longer clean texts. We propose a novel deep neural network-based approach that relies on coarse-grained sentence modelling using a convolutional neural network (CNN) and a recurrent neural network (RNN) model, combined with a specific fine-grained word-level similarity matching model. More specifically, we develop a new architecture, called DeepParaphrase, which enables to create an informative semantic representation of each sentence by (1) using CNN to extract the local region information in form of important n-grams from the sentence, and (2) applying RNN to capture the long-term dependency information. In addition, we perform a comparative study on state-of-the-art approaches within paraphrase detection. An important insight from this study is that existing paraphrase approaches perform well when applied on clean texts, but they do not necessarily deliver good performance against noisy texts, and vice versa. In contrast, our evaluation has shown that the proposed DeepParaphrase-based approach achieves good results in both types of texts, thus making it more robust and generic than the existing approaches.

0.021087032 = product of:
  0.105435155 = sum of:
    0.105435155 = weight(_text_:22 in 337) [ClassicSimilarity], result of:
      0.105435155 = score(doc=337,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.61904186 = fieldWeight in 337, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.125 = fieldNorm(doc=337)
  0.2 = coord(1/5)

Source

Annual review of information science and technology. 22(1987), S.79-108

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 3164) [ClassicSimilarity], result of:
      0.09225576 = score(doc=3164,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 3164, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=3164)
  0.2 = coord(1/5)

Source

Computational linguistics. 22(1996) no.2, S.217-248

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 4506) [ClassicSimilarity], result of:
      0.09225576 = score(doc=4506,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 4506, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=4506)
  0.2 = coord(1/5)

Date

8.10.2000 11:52:22

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 6672) [ClassicSimilarity], result of:
      0.09225576 = score(doc=6672,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 6672, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=6672)
  0.2 = coord(1/5)

Date

31. 7.1996 9:22:19

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 1179) [ClassicSimilarity], result of:
      0.09225576 = score(doc=1179,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 1179, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=1179)
  0.2 = coord(1/5)

Date

31. 7.1996 9:22:19

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 3117) [ClassicSimilarity], result of:
      0.09225576 = score(doc=3117,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 3117, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=3117)
  0.2 = coord(1/5)

Date

28. 2.1999 10:48:22

0.018451152 = product of:
  0.09225576 = sum of:
    0.09225576 = weight(_text_:22 in 1079) [ClassicSimilarity], result of:
      0.09225576 = score(doc=1079,freq=2.0), product of:
        0.17031991 = queryWeight, product of:
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.04863741 = queryNorm
        0.5416616 = fieldWeight in 1079, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.5018296 = idf(docFreq=3622, maxDocs=44218)
          0.109375 = fieldNorm(doc=1079)
  0.2 = coord(1/5)

Date

27. 1.2023 16:22:55