A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text

García-Hernández, René Arnulfo; Martínez-Trinidad, José Fco.; Carrasco-Ochoa, Jesús Ariel

doi:10.1007/978-3-540-30463-0_60

Cited by 14 publications

(10 citation statements)

References 5 publications

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“…We selected this system because it was one of the best in the Spanish QA task at the 2005 edition of the CLEF. We also used the data-mining tool described in [2] in order to compute the maximal frequent word sequences required by one of the methods. In this case, we established a threshold σ = 2, which indicated that a word sequence was frequent if it was contained in at least two different translations.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Cross-Language Question Answering by Combining Multiple Question Translations

Aceves-Pérez

Montes-y-Gómez

Villaseñor-Pineda

2007

Computational Linguistics and Intelligent Text Processing

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Abstract.One major problem of state-of-the-art Cross Language Question Answering systems is the translation of user questions. This paper proposes combining the potential of multiple translation machines in order to improve the final answering precision. In particular, it presents three different methods for this purpose. The first one focuses on selecting the most fluent translation from a given set; the second one combines the passages recovered by several question translations; finally, the third one constructs a new question reformulation by merging word sequences from different translations. Experimental results demonstrated that the proposed approaches allow reducing the error rates in relation to a monolingual question answering exercise.

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Section: Methodsmentioning

confidence: 99%

Enhancing Cross-Language Question Answering by Combining Multiple Question Translations

Aceves-Pérez

Montes-y-Gómez

Villaseñor-Pineda

2007

Computational Linguistics and Intelligent Text Processing

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“…FSs that are not parts of any other FS are called Maximal Frequent Sequences (MFSs) [13,14]. For example, in the following text…”

Section: Maximal Frequent Sequencesmentioning

confidence: 99%

“…In any case, MFSs represent all FSs in a compact way: all FSs can be obtained from all MFSs by bursting each MFS into a set of all its subsequences. García [13] proposed an efficient algorithm to find all MFSs in a text, which we also used to efficiently obtain and store all FSs of the document.…”

Section: … Mona Lisa Is the Most Beautiful Picture Of Leonardo Da Vinmentioning

confidence: 99%

Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

Ledeneva

García-Hernández

Gelbukh

2014

Computational Linguistics and Intelligent Text Processing

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Abstract. We suggest a new method for the task of extractive text summarization using graph-based ranking algorithms. The main idea of this paper is to rank Maximal Frequent Sequences (MFS) in order to identify the most important information in a text. MFS are considered as nodes of a graph in term selection step, and then are ranked in term weighting step using a graphbased algorithm. We show that the proposed method produces results superior to the-state-of-the-art methods; in addition, the best sentences were found with this method. We prove that MFS are better than other terms. Moreover, we show that the longer is MFS, the better are the results. If the stop-words are excluded, we lose the sense of MFS, and the results are worse. Other important aspect of this method is that it does not require deep linguistic knowledge, nor domain or language specific annotated corpora, which makes it highly portable to other domains, genres, and languages.

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“…FSs that are not parts of any other FS are called Maximal Frequent Sequences (MFSs) [10,11]. For example, in the following text…”

Section: Frequent Sequencesmentioning

confidence: 99%

“…In any case, MFSs represent all FSs in a compact way: all FSs can be obtained from all MFSs by bursting each MFS into a set of all its subsequences. García [10] proposed an efficient algorithm to find all MFSs in a text, which we also used to efficiently obtain and store all FSs of the document.…”

Section: … Mona Lisa Is the Most Beautiful Picture Of Leonardo Da Vinmentioning

confidence: 99%

Terms Derived from Frequent Sequences for Extractive Text Summarization

Ledeneva

Gelbukh

García-Hernández

Computational Linguistics and Intelligent Text Processing

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Abstract. Automatic text summarization helps the user to quickly understand large volumes of information. We present a language-and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that repeat more than once in the text are good terms to describe the text's contents, and so are also so-called maximal frequent sentences. We also show that the frequency of the term as term weight gives good results (while we only count the occurrences of a term in repeating bigrams).

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A Fast Algorithm to Find All the Maximal Frequent Sequences in a Text

Cited by 14 publications

References 5 publications

Enhancing Cross-Language Question Answering by Combining Multiple Question Translations

Enhancing Cross-Language Question Answering by Combining Multiple Question Translations

Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

Terms Derived from Frequent Sequences for Extractive Text Summarization

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