Text mining and data harvesting algorithms have become popular in the computational linguistics community . They employ patterns that specify the kind of information to be harvested , and usually bootstrap either the pattern learning or the term harvesting process ( or both ) in a recursive cycle , using data learned in one step to generate more seeds for the next . They therefore treat the source text corpus as a network , in which words are the nodes and relations linking them are the edges . The results of computational network analysis , especially from the world wide web , are thus applicable . Surprisingly , these results have not yet been broadly introduced into the computational linguistics community . In this paper we show how various results apply to text mining , how they explain some previously observed phenomena , and how they can be helpful for computational linguistics applications . Text mining / harvesting algorithms have been applied in recent years for various uses , including learning of semantic constraints for verb participants ( Lin and Pantel , 2002 ) related pairs in various relations , such as part-whole ( Girju et al . , 2003 ) , cause ( Pantel and Pennacchiotti , 2006 ) , and other typical information extraction relations , large collections of entities ( Soderland et al . , 1999 ; Etzioni et al . , 2005 ) , features of objects ( Pasca , 2004 ) and ontologies ( Carlson et al . , 2010 ) . They generally start with one or more seed terms and employ patterns that specify the desired information as it relates to the seed(s ) . Several approaches have been developed specifically for learning patterns , including guided pattern collection with manual filtering ( Riloff and Shepherd , 1997 ) automated surface-level pattern induction ( Agichtein and Gravano , 2000 ; Ravichandran and Hovy , 2002 ) probabilistic methods for taxonomy relation learning ( Snow et al . , 2005 ) and kernel methods for relation learning ( Zelenko et al . , 2003 ) . Generally , the harvesting procedure is recursive , in which data ( terms or patterns ) gathered in one step of a cycle are used as seeds in the following step , to gather more terms or patterns . This method treats the source text as a graph or network , consisting of terms ( words ) as nodes and inter-term relations as edges . Each relation type induces a different network1 . Text mining is a process of network traversal , and faces the standard problems of handling cycles , ranking search alternatives , estimating yield maxima , etc . The computational properties of large networks and large network traversal have been studied intensively ( Sabidussi , 1966 ; Freeman , 1979 ; Watts and Strogatz , 1998 ) and especially , over the past years , in the context of the world wide web ( Page et al . , 1999 ; Broder et al . , 2000 ; Kleinberg and Lawrence , 2001 ; Li et al . , 2005 ; Clauset et al . , 2009 ) . Surprisingly , except in ( Talukdar and Pereira , 2010 ) , this work has not yet been related to text mining research in the computational linguistics community . The work is , however , relevant in at least two ways . It sometimes explains why text mining algo-rithms have the limitations and thresholds that are empirically found ( or suspected ) , and it may suggest ways to improve text mining algorithms for some applications . In Section 2 , we review some related work . In Section 3 we describe the general harvesting procedure , and follow with an examination of the various statistical properties of implicit semantic networks in Section 4 , using our implemented harvester to provide illustrative statistics . In Section 5 we discuss implications for computational linguistics research . In this paper we describe the implicit ' hidden ' semantic network graph structure induced over the text of the web and other sources by the semantic relations people use in sentences . We describe how term harvesting patterns whose seed terms are harvested and then applied recursively can be used to discover these semantic term networks . Although these networks differ considerably from the web in relation density , type , and network size , we show , somewhat surprisingly , that the same power-law , smallworld effect , transitivity , and most other characteristics that apply to the web 's hyperlinked network structure hold also for the implicit semantic term graphs-certainly for the semantic relations and languages we have studied , and most probably for almost all semantic relations and human languages . This rather interesting observation leads us to surmise that the hyperlinks people create in the web are of essentially the same type as the semantic relations people use in normal sentences , and that they form an extension of normal language that was not needed before because people did not have the ability within the span of a single sentence to ' embed ' structures larger than a clause-certainly not a whole other page 's worth of information . The principal exception is the academic citation reference ( lexicalized as " see " ) , which is not used in modern webpages . Rather , the ' lexicalization ' now used is a formatting convention : the hyperlink is colored and often underlined , facilities offered by computer screens but not available to speech or easy in traditional typesetting .

Challenge: Corpora can be seen as a network in text mining and data harvesting however how recent network analysis methods would perform is unknown.
Approach: They study if recent network analysis tools can provide insights for computational linguistics by connecting two hidden semantic network graph structures in texts.
Outcome: They show that there are similarities between networks on the Web and ones induced from texts such as in power-law, small-world effect and transitivity.