thus , 
 many recent studies have sought to extract interaction information from 
 biomolecular text using natural language processing technology . however , we 
 have insufficient biomolecular data annotated with linguistic information . in 
 2005 , the icml05 workshop on learning language in logic ( lll05 ) task provided a 
 small training dataset annotated with pos - tags and syntactic relations . previous studies have insisted that linguistic information was 
 useful for improving the detection of gene interactions . however , the 
 experimental results for the lll05 data gave a reasonable precision but poor 
 recall . to improve recall without sacrificing precision , we propose a 
 three - phase method to detect gene interactions using syntactic relation 
 information , and apply it to a small training dataset lacking domain knowledge . 
 through experimentation , we show that our proposed method significantly 
 outperforms existing methods , and describe the contribution of each phase to 
 its performance . section 4 describes the 
 training and test data used for our experiments and presents experimental 
 results that demonstrate that our three - phase method is effective for detecting 
 gene interactions . the task of 
 relation mining in the biomedical domain has been studied extensively in recent 
 years . current research includes protein - protein interactions [ 2 , 3 ] , 
 subcellular locations , and disease - treatment relationships , and systems 
 based on sequence modeling and pattern- or rule - based extraction best detect 
 protein - protein interactions [ 2 , 6 , 7 ] . using text mining technology for 
 automatic protein(gene ) interactions resulted in high precision , but low recall 
 . many studies have used linguistic information to improve performance in 
 detecting gene interactions . to improve recall without sacrificing precision , 
 otasek et al .   expanded the diversity of sentence structures recognized by a 
 syntactic parser through additional training , and park et al .   presented a 
 method using bidirectional incremental parsing . experiments deduced 182 
 relations out of 492 sentences showing 48% recall and 80% precision . many linguistic processes 
 have been used to deduce gene interactions , including bidirectional incremental 
 parsing , combinatory categorical grammar ( ccg ) , coordination , 
 apposition , compound noun processing , and positive / negative predicate 
 learning . with these methods , assumed that sentences 
 derived from sets of abstracts contained a significant number of protein names 
 connected by verbs that indicate the type of relationship between them . they restricted 
 the problem domain and imposed several strong assumptions that included 
 prespecified protein names and a limited set of verbs to represent actions . then , they 
 learned patterns with finite - state automata based on a genetic algorithm . for 
 example , agent1 , target3 , pattern2  implies that agent1 interacts 
 with target3 via pattern2 . in biomolecular text , the agent or target can be 
 encapsulated in another term based on some conditions , for 
 example , apposition , modifying nouns , and so on . however , the 
 method in   can not deal with a situation in which genes are encapsulated in 
 other terms via syntactic relations . error analysis revealed that they wrongly detected 
 an agent and its target in a pair of genes , although they correctly detected 
 two genes that interact with each other . the nodes in the dependency trees from which patterns were derived were either 
 a lexical item or a semantic category , such as a gene , protein , agent , or 
 target . they extended the patterns based on eight seed patterns and trained the model 
 using the basic dataset without coreference , as provided by the lll05 challenge 
 organizers . the failure of the system 
 to extract meaningful relations can be traced back to the errors that 
 minipar introduced in the dependency trees . used gleaner as an 
 inductive logic programming approach and further applied brill tagger , a 
 shallow parser based on conditional random fields , and porter stemmer . they 
 also used much linguistic information , including sentence - structure predicates , 
 the frequencies of words , lexical properties , and semantic knowledge using 
 mesh . gleaner suffered from not distinguishing 
 between an agent and a target well because no syntactic structure 
 was used . popelinsky and blatak   used brill tagger 
 and wordnet , and katrenko et al .   created a simple ontology specifically 
 for use in the lll05 challenge riedel and 
 klein   obtained the best performance on the lll05 challenge task using 
 syntactic chains . therefore , they generated a set of clauses based on chains of 
 syntactic relations between two genes . the method achieved an f - measure of 52.6% on the dataset without coreferences , 
 demonstrating that using syntactic information from the annotated 
 datasets significantly improved performance . however , recall was only 46.2% , and the system 
 needs to improve recall . for genia and atcr data , they find agents and targets from the syntactic 
 patterns directly connected with interaction verbs with subject or object 
 functions . so , they do not consider the case that agent or target is 
 encapsulated in another term , and indirectly connected with interaction verbs . in addition , there is a limit that they find agents and targets only from the 
 subject and object relations . combining 
 syntactic dependency information with features based on word sequences could 
 lead to further improvements in performance , as demonstrated by the more recent 
 approaches to relation extraction [ 1921 ] . we build on the 
 conclusion of the previous work that linguistic information , especially 
 syntactic information , is an important key for detecting gene interactions . however , we need a more robust method to improve recall without sacrificing 
 precision . based on syntactic relation information , we propose a 
 three - phase - based method for detecting gene interactions . mentioned the failure of the system to extract meaningful relations 
 can be traced back to the errors of the applied syntactic analyzer . if we use 
 the annotated lll05 syntactic relation information , we can not testify the 
 robustness of our system in real time . so , we also experiment the performance 
 of our system based on a real - syntactic analyzer . to objectively 
 compare the performance of our system with that of previous systems , we use 
 lll05 data . in the next section , the first subset does not 
 include coreferences or ellipsis , unlike the second subset . the training set 
 without coreferences consists of 55 sentences , including 106 examples of genic 
 interactions . it contains 70 examples of action , 30 examples of binding and 
 promoter , and 6 examples of regulation . a syntactic 
 relation is important linguistic information for detecting the structure of 
 text . algorithm 1 shows one example of syntactic relations between two genes in 
 the lll05 data . the syntactic relations provided in lll05 were of the form 
 r , 
 w , 
 w ) , where 
 r is one of a 
 fixed set of syntactic relations between 
 w and 
 w assigned by the 
 lll parser . the chain of terms is spo0a(agent ) 
  protein(n ) 
  depend(v ) 
  transcription(n ) 
  gene(n ) 
  
 spoiig(target). 
 in the chain , node depend(v ) is the verb that indicates the interaction between spo0a(agent ) and spoiig(target ) . however , depend(v ) has direct 
 syntactic relations with protein(n ) and transcription(n ) , not with spo0a(agent ) 
 or spoiig(target ) . in other words , spo0a(agent ) was encapsulated 
 in protein(n ) with the relation (  mod_att  ) , and spoiig(target ) was 
 encapsulated in transcription(n ) with the relation (  mod_att  ) and (  comp_of  ) . without any 
 domain knowledge of biomolecular text , we automatically detect gene 
 interactions using syntactic relations annotated in the lll05 data . in the 
 first phase , to improve recall , we detect the relations that encapsulate an 
 agent or target . in the second phase , next , to improve 
 precision , we must determine which of the two genes is the agent and which is 
 the target . to determine the agent and target for two genes , we learn direction 
 rules on the relations from agent to target in the third phase . an agent or 
 target gene is usually encapsulated in another term , and the verb that 
 indicates the interaction between two genes has syntactic relations with two 
 terms that encapsulate the genes . to improve recall for gene interactions first , we find the syntactic chain from an agent to its target . in figure 1 , 
 depend(v ) is the verb that indicates an interaction between spo0a(agent ) 
 and spoiig(target ) . in this paper , we call the verb that indicates the 
 interaction between an agent and its target an  interaction verb . as mentioned 
 above , depend(v ) has syntactic relations with protein(n ) and transcription(n ) , 
 but not with spo0a(agent ) or spoiig(target ) . in a syntactic 
 chain from an agent to its target , we call the node preceding an interaction 
 verb a  metaagent ,  and the node following an interaction verb a  metatarget .  
 in figure 1 , protein(n ) is a metaagent , and transcription(n ) is a metatarget . we define the 
 syntactic categories connecting an agent(target ) and a metaagent(metatarget ) 
  syntactic encapsulation categories .  in figure 1 , mod_att and comp_of are examples of the syntactic encapsulation categories . to detect a metaagent 
 and a metatarget however , in the automatically obtained syntactic chains , we do not know 
 which verb is an interaction verb . to overcome the problem , we extract the 
 syntactic encapsulation categories from the syntactic chains that include only 
 one verb in the training dataset . to detect gene 
 interactions , we must recognize the interaction verbs . in the second phase , we 
 retrieve the interaction verbs that indicate an interaction between two genes . if we consider 
 only the syntactic chains that contain only one verb , the size of the 
 interaction verbs becomes very small . since the lll05 training dataset is 
 small , we collect all the verbs in the syntactic chains from an agent to its 
 target . according to 
 the first and second phases previous 
 studies made many errors in attempts to recognize which of two genes was the 
 agent or target . therefore , a new method is required to recognize an agent and 
 its target correctly in a pair of genes . in the third phase , we propose 
 learning the directions of the syntactic relations in the syntactic path from 
 an agent to its target . if we do not permit the reverse direction , the agent 
 and target will not be detected wrongly and thus improve the precision . we learn the 
 direction of a syntactic relation related with an interaction verb . for a 
 syntactic relation , if a syntactic relation is relation(syntactic category , current node , next node ) , the direction is 
  right ,  since the next node is written to the right of the current node . if a 
 syntactic relation is relation(syntactic category , next node , current node ) , the direction is  left  because the next node is written to the left of the 
 current node . figure 1 also shows an example of direction information of a 
 syntactic path . among the directions , we retrieve only the direction 
 information of an interaction verb . the direction 
 information is dependent on the syntactic category of the relation and the 
 lexical word of the current node . in learning , we retrieve a syntactic category 
 ( a lexical word ) and direction information for an interaction verb , and we make 
 a template lexical word , syntactic category , direction. we construct 
 direction information for all relations concerning interaction verbs in the 
 training data . based on the direction information , we learn direction rules . let us explain the direction rule - learning algorithm , which is shown in algorithm 3 . one is a positive rule set obtained by learning the 
 direction from an agent to its target . the other is a negative rule set 
 obtained by learning the direction from a target to its agent in reverse order . figure 2 shows the reverse syntactic path from a target to its agent of the 
 sentence in figure 1 . the positive and negative rules for the sentence in 
 figure 1 are shown in table 1 . from the positive and negative rule sets , we 
 construct direction rules according to the following subsections . align  means the modification of 
 any conflict in a rule set . for any lexical word a and relation b , if a 
 conflict of two direction rules exists in a rule set , then we remove both 
 rules , and add a modified rule a , b , any. because the direction 
 information is not trustworthy , we set direction  any . the process for aligning a rule set is shown in 1 > and 
 2 > of algorithm 3 . after alignment 
 of positive and negative rule sets , we construct direction rules from the two 
 rule sets . the algorithm used to obtain direction rules is shown in 3 > of 
 algorithm 3 . consider every 
 rule a , b , c in the positive rule set , for any lexical word a and relation b , and direction c. in algorithm 3 , 
 ( 3.1 ) case indicates that direction information c is changed to any . since the 
 same direction exists in both the positive and negative rule sets , the 
 direction information is not trustworthy . therefore , we change the direction 
 information into any . in ( 3.2 ) case , 
 the direction information c in the positive rule is still used in the obtained 
 direction rule . if c is  right ,  then  opposite c  means  left .  otherwise , if c is 
  left ,  then  opposite c  means right .  since the direction in the negative 
 rule set is opposite with that in the positive rule set , the direction information 
 in the template is trustworthy . case 
 indicates that the negative rule set does not have any rule concerning a and b. 
 the obtained direction rule is same with the original template in the positive 
 rule set . for an 
 interaction verb a , the relations not learned in the training data can appear 
 in the test data . so , we add a default rule a , otherwise , any as 
 described in table 2 . the default rule permits any direction is okay for other 
 relations not appearing in the training data . because the training data is so 
 small , the default rule can resolve data sparseness problem . we detect agent 
 candidates from the test set using the gene dictionary provided by lll05 . the obtained syntactic encapsulation categories , interaction verbs , and direction 
 rules through three phases are applied to test data according to the following 
 procedure . for each syntactic chain , we repeat the following 
 procedure . 
 if a current 
 node is a gene and syntactic chain contains any interaction verb , then we determine 
 that the current node is a target , and stop the extension of the syntactic 
 chain . otherwise , if 
 the category of the syntactic relation of the next node candidate is a 
 syntactic encapsulation category , we extend the syntactic chain by adding the 
 next node candidate.otherwise , if 
 the current lexical word is an interaction verb and the direction of the next 
 node candidate is consistent with the direction rules , then we extend the 
 syntactic chain . 

 in the finally 
 obtained syntactic chains , we determine that the 
 first node is an agent and the last node is its target . if a current 
 node is a gene and syntactic chain contains any interaction verb , then we determine 
 that the current node is a target , and stop the extension of the syntactic 
 chain . otherwise , if 
 the category of the syntactic relation of the next node candidate is a 
 syntactic encapsulation category , we extend the syntactic chain by adding the 
 next node candidate . otherwise , if 
 the current lexical word is an interaction verb and the direction of the next 
 node candidate is consistent with the direction rules , then we extend the 
 syntactic chain . with more and 
 more biomedical datasets becoming publicly available , there has been some 
 research effort on corpus design issues and usage in biomedical natural 
 language processing [ 22 , 23 ] . for a reasonable comparison with previous methods as 
 mentioned before , the lll05 training dataset without coreference consists of 55 
 sentences , including 106 genic interactions , and the test data consist of 144 
 sentences . based on the 
 lll05 syntactic tags , the performance of our three - phase method versus that of 
 previous methods . based on a 
 real - syntactic analyzer , the performance of our three - phase method versus that 
 of previous methods . the change in 
 performance when each phase is removed . 
 in the experiments , we obtained the following five 
 results . based on the 
 lll05 syntactic tags , the performance of our three - phase method versus that of 
 previous methods . based on a 
 real - syntactic analyzer , the performance of our three - phase method versus that 
 of previous methods .   the change in 
 performance when each phase is removed . our three - phase 
 detection method for gene interactions achieved an f - measure of 67.2% using lll05-annotated 
 syntactic relations , and 44.0% using a real - syntactic analyzer 
 ( see tables 3 and 4).using lll05 syntactic tags , our three - phase 
 method achieved an improvement of 14.6% to 37.6% over previous methods 
 ( see table 3).our method significantly outperformed greenwood et al . , 
 which also used minipar ( see table 4 ) . when the second 
 or third phase was removed , the precision became significantly worse ( see table 5).when the first 
 phase was removed , there were no interaction results . it means the first phase 
 is important for the improvement of recall ( see table 5 ) . as shown in 
 table 3 , of the systems evaluated , our system performed the best with a precision of 67.9% , recall of 66.6% , and an f - measure of 67.2 
 percent . our three - phase 
 detection method for gene interactions achieved an f - measure of 67.2% using lll05-annotated 
 syntactic relations , and 44.0% using a real - syntactic analyzer 
 ( see tables 3 and 4 ) . using lll05 syntactic tags , our three - phase 
 method achieved an improvement of 14.6% to 37.6% over previous methods 
 ( see table 3 ) . when the second 
 or third phase was removed , the precision became significantly worse ( see table 5 ) . it means the first phase 
 is important for the improvement of recall ( see table 5 ) . we will 
 summarize the significance of each phase introduced in section 3 . as shown in 
 table 5 , every phase is important for its performance . without the first phase , if no syntactic relations are considered encapsulation categories , then no 
 pairs of genes are generated . only this result shows the decrease of recall 
 among three results in table 5 . it demonstrates that the syntactic 
 encapsulation categories contribute to the improvement of recall . without the second phase , if all the verbs are 
 considered interaction verbs , the precision is very low , which results from the 
 generation of too many wrong syntactic paths . without the third phase , if we do 
 not consider direction information , then the recall increases and the precision 
 significantly decreases , which also result from the construction of many wrong 
 syntactic paths . the experiments prove that the second and third phases 
 contribute to the improvement of precision , and the first phase to the improvement 
 of recall . we conclude that all three phases are important for detecting gene 
 interactions . to experiment the robustness of our method in real 
 time , we have used minipar , an existing syntactic analyzer . the system based on 
 annotated syntactic relations in lll05 significantly outperforms that using 
 minipar . this is because of the errors in syntactic relations and pos - tags that 
 minipar produced . to improve recall without sacrificing precision , this 
 paper proposes a three - phase method for the automatic detection of gene 
 interactions using syntactic relations . the proposed method does not require 
 domain knowledge . to improve recall , in the first phase , we construct syntactic 
 encapsulation categories of agent and target . in the second phase , we construct 
 interaction verbs that connect pairs of genes that interact with each other . to 
 improve precision , in the third phase , we learn direction information to detect 
 which of the two genes is the agent or target . the experimental results show that 
 our three - phase method performs significantly better than previous methods . our 
 method achieved a precision of 67.9% , a recall of 66.6% , an f - measure of 67.2% using lll05 syntactic 
 relations . we conclude that our proposed three - phase method is effective 
 for detecting gene interactions . furthermore , we demonstrated that every 
 phase is important for performance . in the future , we need to expand the size of the training dataset and 
 experiment with a large dataset .
interactions between proteins and genes are considered essential in 
 the description of biomolecular phenomena , and networks of interactions 
 are applied in a system 's biology approach . 
 recently , many studies have 
 sought to extract information from biomolecular text using natural language 
 processing technology . 
 previous studies have asserted that linguistic 
 information is useful for improving the detection of gene interactions . 
 in particular , syntactic relations among linguistic information are good 
 for detecting gene interactions . however , previous systems give a reasonably 
 good precision but poor recall . to improve recall without sacrificing 
 precision , 
 this paper proposes a three - phase method for detecting gene 
 interactions based on syntactic relations . in the first phase , 
 we retrieve 
 syntactic encapsulation categories for each candidate agent and target . 
 in the second phase , 
 we construct a verb list that indicates the nature of 
 the interaction between pairs of genes . in the last phase , 
 we determine 
 direction rules to detect which of two genes is the agent or target . even 
 without biomolecular knowledge , 
 our method performs reasonably well using 
 a small training dataset . while the first phase contributes to improve 
 recall , the second and third phases contribute to improve precision . in 
 the experimental results using icml 05 workshop on learning language 
 in logic ( lll05 ) data , our proposed method gave an f - measure of 67.2% for the test data , significantly outperforming previous methods . 
 we also 
 describe the contribution of each phase to the performance .