Generating links by mining quotations

Kolak, Okan; Schilit, Bill N.

doi:10.1145/1379092.1379117

Cited by 25 publications

(38 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…on the chronological line. The only approach known to us that can be paralleled to ours is the one described in Kolak and Schilit (2008) for quotation mining within the Google Books corpus with algorithm searching for verbatim quotations only.…”

Section: Quotations and Their Definitionmentioning

confidence: 99%

“…1 That is why we call these links edges and not arcs, and possibly, the graph could be called a semi-directed graph. Kolak and Schilit (2008) observe that the standard plagiarism detection algorithms are useless for unmarked quotation mining and suggest straightforward and efficient algorithm for repeated passage extraction. The algorithm is suitable for modern English texts, since quotations are more or less verbatim and the word order is stable.…”

Section: The Grid and The Networkmentioning

confidence: 99%

“…The problem is that the decision whether the repeating text passage is a quotation or not is not a binary one. Kolak and Schilit (2008) note the problem and let their subjects evaluate results of their algorithm on a 1-5 scale. As we did not manage to do vast and proper evaluation of the outputs of our algorithm using various minimal lengths of the quotations and degrees of variability, we relied on our subjective experience.…”

Section: Quotations Extraction: Technical Descriptionmentioning

confidence: 99%

See 2 more Smart Citations

Quotations, Relevance and Time Depth: Medieval Arabic Literature in Grids and Networks

Zemánek

Milička²

2014

Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL)

View full text Add to dashboard Cite

This contribution deals with the use of quotations (repeated n-grams) in the works of medieval Arabic literature. The analysis is based on a 420 millions of words historical corpus of Arabic. Based on repeated quotations from work to work, a network is constructed and used for interpretation of various aspects of Arabic literature. Two short case studies are presented, concentrating on the centrality and relevance of individual works, and the analysis of a time depth and resulting impact of a given work in various periods. Quotations and Their DefinitionThe relevance of individual works in a given literature and the time depth of such relevance are of interest for many reasons. There are many methods that can reveal such relevance.The current contribution is based on quotation extraction. Quotations, both covert and overt, both from written and oral sources, belong to constitutive features of medieval Arabic literature.There are genres which heavily depend on establishing credible links among sources, especially the oral ones, where a trusty chain of tradents is crucial for the claims that such chains accompany. Other links may point to the importance of a given work (or its part) and may uncover previously unseen relations within a given literature or a given genre/register, or reveal connections among genres/registers within a given literature. As such, the results are interesting in a wide research range, from linguists or literature theorists to authors interested in the interactions of various subsets of a given literature.The research on quotations, their extraction and detection is rich in the NLP, but the algorihms used are based mainly on the quotation-marker recognition, e.g. Pareti et al. (2013), Pouliquen et al. (2007) and Fernandes et al. (2011), or on the metadata procesing (e.g. Shi et al. 2010), to name just a few examples. It can be said that most of the contributions focus on issues different from the one described in this contribution and choose a different approach.Our understanding of quotations in this project is limited to similar strings of words, i.e. the quotations are very close to borrowings or repetition of verbatim or almost verbatim passages. Technically, it can be viewed as an n-gram that is being repeated in at least two works. These repeated n-grams create links that exhibit some hierarchy, e.g. on the chronological line. The only approach known to us that can be paralleled to ours is the one described in Kolak and Schilit (2008) for quotation mining within the Google Books corpus with algorithm searching for verbatim quotations only.In a different context and without direct inspiration we developed an algorithm that is tolerant to a certain degree of lexical and morphological variation and word order variability. The reason for this tolerance is both the type of the Arabic language (flective morphology and free word order), but also the fact that the quotations in medieval Arabic literature tend not to be very strict. Despite of the fact that the matching is not so rigo...

show abstract

Section: Quotations and Their Definitionmentioning

confidence: 99%

Section: The Grid and The Networkmentioning

confidence: 99%

Section: Quotations Extraction: Technical Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

Quotations, Relevance and Time Depth: Medieval Arabic Literature in Grids and Networks

Zemánek

Milička²

2014

Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL)

View full text Add to dashboard Cite

show abstract

“…From human being perspective, such research assumes a particular interest when the involved data are natural language documents and the relationships are defined between entities described in text, e.g. [23,24,34,47].…”

Section: Introductionmentioning

confidence: 99%

Supervised semantic relation mining from linguistically noisy text documents

Giannone

Basili

Naggar

et al. 2010

IJDAR

View full text Add to dashboard Cite

In this paper, we present models for mining text relations between named entities, which can deal with data highly affected by linguistic noise. Our models are made robust by: (a) the exploitation of state-of-the-art statistical algorithms such as support vector machines (SVMs) along with effective and versatile pattern mining methods, e.g. word sequence kernels; (b) the design of specific features capable of capturing long distance relationships; and (c) the use of domain prior knowledge in the form of ontological constraints, e.g. bounds on the type of relation arguments given by the semantic categories of the involved entities. This property allows for keeping small the training data required by SVMs and consequently lowering the system design costs. We empirically tested our hybrid model in the very complex domain of business intelligence, where the textual data are constituted by reports on investigations into criminal enterprises based on police interrogatory reports, electronic eavesdropping and wiretaps. The target relations are typically established between entities, as they are mentioned in these information sources. The experiments on mining such relations show that our approach with small training data C. Giannone (B) · P. Naggar CM Sistemi s.p

show abstract

“…for the automatic generation of hyperlinks between related entities across documents [15] or digital media indexing and integration [24]. In bioinformatics, studies on relation mining are carried out on three main different data types: natural language texts, molecular structures expressed in text format (e.g.…”

Section: Introductionmentioning

confidence: 99%

Generalized Framework for Syntax-Based Relation Mining

Coppola

Moschitti

Pighin³

2008

2008 Eighth IEEE International Conference on Data Mining

View full text Add to dashboard Cite

Supervised approaches to Data Mining are particularly appealing as they allow for the extraction of complex relations from data objects. In order to facilitate their application in different areas, ranging from protein to protein interaction in bioinformatics to text mining in computational linguistics research, a modular and general mining framework is needed. The major constraint to the generalization process concerns the feature design for the description of relational data.In this paper, we present a machine learning framework for the automatic mining of relations, where the target objects are structurally organized in a tree. Object types are generalized by means of the use of roles, whereas the relation properties are described by means of the underlying tree structure. The latter is encoded in the learning algorithm thanks to kernel methods for structured data, which represent structures in terms of their all possible subparts. This approach can be applied to any kind of data disregarding their very nature.Experiments with Support Vector Machines on two text mining datasets for relation extraction, i.e. the PropBank and FrameNet corpora, show both that our approach is general, and that it reaches state-of-the-art accuracy.

show abstract

Generating links by mining quotations

Cited by 25 publications

References 23 publications

Quotations, Relevance and Time Depth: Medieval Arabic Literature in Grids and Networks

Quotations, Relevance and Time Depth: Medieval Arabic Literature in Grids and Networks

Supervised semantic relation mining from linguistically noisy text documents

Generalized Framework for Syntax-Based Relation Mining

Contact Info

Product

Resources

About