Syntactic and Semantic Disambiguation of Numeral Strings Using an N-Gram Method

Min, Kyongho; Wilson, William H.; Moon, Yoo-Jin

doi:10.1007/11589990_11

Cited by 4 publications

(6 citation statements)

References 7 publications

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“…In addition to author name disambiguation, Woákowicz made an n-gram from musical notes and utilized it to recognize music composers [8]. Further, Min et al proposed generating n-gram frequency profiles to process text strings, including number(s), syntactically and semantically [9]. In basic applications, n-gram data have been used to correct typos according to given contexts and to predict subsequent words [10][11][12][13].…”

Section: Related Workmentioning

confidence: 99%

“…In accordance with this objective, we are constructing an n-gram data system that is specialized for scholarly utilization and which will help such researchers reduce the time spent putting their thoughts into words. N-gram data is used extensively in many research fields and applications such as text categorization [5,6], author profiling [7], composer recognition [8], syntactic and semantic disambiguation [9], sentence prediction [10], spelling correction [11], and text editors [12,13]. The results from these research areas indicate that the data is very useful and reliable.…”

Section: Introductionmentioning

confidence: 98%

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Construction of Scholarly n-Gram from Huge Text Data

Hwang

Yeom

Hwang

et al. 2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

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The ultimate goal of this research is to provide ngram data that is specialized for scholarly utilization. To this end, this paper outlines the construction of a scholarly n-gram through the processing of large text documents. Many researchers, especially non-native English language speakers, find it difficult to construct sentences and paragraphs with appropriate and disambiguated words. One of the methods that can assist them is the provision of n-gram data. A representative n-gram known as Web 1T 5-Gram Version 1, which was constructed by processing virtually all documents retrieved using Google, already exists. However, this data contain unfocused word recommendations, therefore, they are not suitable. Consequently, we are constructing a scholarly ngram. In this paper, we demonstrate the efficiency of n-gram using Web 1T unigram and introduce and discuss the specifics of our research plan related to scholarly n-gram.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Construction of Scholarly n-Gram from Huge Text Data

Hwang

Yeom

Hwang

et al. 2014

2014 Eighth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing

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“…Table 6 shows the performance comparison between rule-based methods [13] and bigrams [14] and our current results with trigrams and pentagrams. The system in [13] was based on a manually generated rule-based method and the system in [14] was based on an automatically generated tabular feature-based method based on two types of bigrams (2L1R0 and 2L0R1). The current systems were based on word trigrams (3L1R1), and word pentagrams (5L2R2).…”

Section: Resultsmentioning

confidence: 99%

“…However, there is no system that interprets numeral strings systematically; they are frequently treated as either numerals or nominal entities. In this paper, we discussed two N-gram methods and compared their performance with a manually obtained rulebased method [13] and a word bigrams method [14]. The word trigrams method with five features performs better than rule-based and word bigrams methods in terms of the F-measurement ratio.…”

Section: Discussionmentioning

confidence: 99%

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Effectiveness of Methods for Syntactic and Semantic Recognition of Numeral Strings: Tradeoffs Between Number of Features and Length of Word N-Grams

Min

Wilson

Kang

AI 2007: Advances in Artificial Intelligence

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Abstract. This paper describes and compares the use of methods based on Ngrams (specifically trigrams and pentagrams), together with five features, to recognise the syntactic and semantic categories of numeral strings representing money, number, date, etc., in texts. The system employs three interpretation processes: word N-grams construction with a tokeniser; rule-based processing of numeral strings; and N-gram-based classification. We extracted numeral strings from 1,111 online newspaper articles. For numeral strings interpretation, we chose 112 (10%) of 1,111 articles to provide unseen test data (1,278 numeral strings), and used the remaining 999 articles to provide 11,525 numeral strings for use in extracting N-gram-based constraints to disambiguate meanings of the numeral strings. The word trigrams method resulted in 83.8% precision, 81.2% recall ratio, and 82.5% in F-measurement ratio. The word pentagrams method resulted in 86.6% precision, 82.9% recall ratio, and 84.7% in F-measurement ratio.

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Comparison of Numeral Strings Interpretation: Rule-Based and Feature-Based N-Gram Methods

Min

Wilson

2006

Lecture Notes in Computer Science

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Syntactic and Semantic Disambiguation of Numeral Strings Using an N-Gram Method

Cited by 4 publications

References 7 publications

Construction of Scholarly n-Gram from Huge Text Data

Construction of Scholarly n-Gram from Huge Text Data

Effectiveness of Methods for Syntactic and Semantic Recognition of Numeral Strings: Tradeoffs Between Number of Features and Length of Word N-Grams

Comparison of Numeral Strings Interpretation: Rule-Based and Feature-Based N-Gram Methods

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