Patent Publication Number: US-2004054677-A1

Title: Method for processing text in a computer and a computer

Description:
BACKGROUND INFORMATION  
       [0001] The present invention is directed to a method for processing text in a computer unit, and to a computer unit according to the definition of the species in the independent claims.  
       [0002] A method and a device for recognizing phrases in a text are already known from U.S. Pat. No. 5,819,260. In this context, a text to be scanned is partitioned into a plurality of text chunks of directly adjacent words, these text chunks being separated from one another by predefined text components in the form of words or punctuation marks from a special text component list. In addition, a list of key words is provided, into which these text chunks or parts thereof are entered when their frequency of occurrence in the text exceeds a given value.  
       SUMMARY OF THE INVENTION  
       [0003] It contrast, the method according to the present invention for processing text in a computer unit and the computer unit according to the present invention having the features of the corresponding independent claims have the advantage that, in a first step, all of those word groups are sought in the text chunks which include a first predefined number of directly adjacent words, that, subsequently, of these word groups in the text chunks, those are deleted whose frequency of occurrence in the first text exceed the first predefined value and which are, therefore, entered into the first list of key words, and that, in a second step, all word groups in the remaining text chunks are sought which include a second predefined number of directly adjacent words, the second predefined number of words being smaller than the first predefined number of words. In this way, a maximum number of key word groups, which are characteristic of the text, may be extracted quickly and efficiently from the scanned text. By limiting the preset number of directly adjacent words to a practical value, for example five, it is possible to further reduce the outlay required for searching for key word groups in the text. Typically, word groups having more than five directly adjacent words occur seldom and, therefore, as a rule, are not suited for characterizing texts. By evaluating all word groups having a predefined number of directly adjacent words in the text, it is additionally ensured that key word groups characteristic of the text and having this number of directly adjacent words do not go undetected.  
       [0004] Advantageous further refinements and improvements of the method for processing text in a computer unit and of the computer unit according to the independent claims are derived from the measures delineated in the dependent claims.  
       [0005] It is particularly advantageous that the second predefined number of words is selected to be smaller by one than the first predefined number of words. In this way, even after deleting the word groups entered into the first list of key words, it is possible to extract a maximum of key word groups from the word groups remaining in the text using the first predefined number of directly adjacent words from the text.  
       [0006] A further benefit is derived in that a plurality of documents is combined with the first text, and in that a word group is only entered into the first list of key words when its frequency of occurrence exceeds a second predefined value in at least one predefined number of documents. In this manner, only those word groups are entered into the first list of key words which are also characteristic of a plurality of documents. In this manner, certain word groups which appear in one document due to an author&#39;s preference, and which do not turn up in the remaining documents and, therefore, are also not characteristic of the entire text, may be masked out, thereby preventing them from being entered into the first list of key words.  
       [0007] A further advantage is attained in that the first text is expanded by a second text having a second list of key words, and a shared list of key words is generated into which a word group is entered when it is contained in the first list of key words or in the second list of key words. In this manner, when adding the second text to the first text, there is no need to scan the resulting total text again for key words, but rather only the second text being newly added. This makes it possible to reduce the outlay required for ascertaining key word groups. when adding a second text to the first text. In this way, the search for key word groups is substantially accelerated for the total text resulting from the first text and the second text. Yet another benefit is derived in that the frequency of occurrence of a word group in the first list of key words is added to the frequency of occurrence of the same word group in the second list of key words, and in that the thus formed total frequency of occurrence of this word group is entered into the shared list of key words in association with this word group. In this way, it is possible to recognize trends in the use of key word groups in texts. When the total frequency of occurrence of a key word group increases when new texts are continually added to a total text, then this indicates that this key word group is gaining in significance for characterizing the total text and, therefore, is also particularly well suited as a search word group for searching out other documents of the same specific field.  
       [0008] A further benefit is derived in that the first text is formed from a third text and a fourth text, the frequency of occurrence of an ascertained word group in the third text is added with the frequency of occurrence of the same word group in the fourth text, in order to ascertain the frequency of occurrence of this word group in the first text. In this manner, when generating the first key word list for the first text, those word groups are also considered which, neither in the third text nor in the fourth text, reach the predefined frequency of occurrence for inclusion in a key word list assigned to the third text or the fourth text. For this, however, it is not necessary to again conduct a search operation for the appropriate word groups in the third text and in the fourth text, rather the frequencies of occurrence ascertained for the found word groups in an earlier search operation for such word groups that had been conducted separately for the third text and the fourth text may be used. Another advantage is that only those word groups which end with a noun are selected for inclusion in the first list of key words. In this way, to the greatest extent possible, only meaningful word groups are selected. It turns out that word groups that do not end with a noun have little in terms of content. Thus is true, above all, in German and English. 
     
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
     [0009] One exemplary embodiment of the present invention is illustrated in the drawing and is elucidated in the following description. FIG. 1 shows a block diagram of a computer unit according to the present invention;  
     [0010]FIG. 2 a flow chart of the method according to the present invention;  
     [0011]FIG. 3 a flow chart for selecting a key word group for a text composed of a plurality of documents; and FIG. 4, a flow chart for generating a common list of key words. 
    
    
     DESCRIPTION OF THE EXEMPLARY EMBODIMENT  
     [0012] In FIG. 1, 1 denotes a computer unit, for example a computer. Computer unit  1  include means  35  for partitioning a first text into a subdivided text  11 . They are designated in the following as partitioning means  35 . Connected to partitioning means  35  is a memory  15 , which, as illustrated in FIG. 1, is configured in computer unit  1 . Memory  15  could, however, also be configured outside of computer unit  1  and be assigned to computer unit  1 . Computer unit  1  also includes a search tool  50 , which searches for word groups in partitioned text  11 . Means  40  for ascertaining the frequency of occurrence of the word groups located by search tool  50  are linked to search tool  50 . Selection means  45  are connected to means  40  for ascertaining the frequency of occurrence. Selection means  45  are connected to a deletion device  55  which is used for deleting word groups in partitioned text  11 . Selection means  45  themselves are used for selecting a word group of partitioned text  11  for a first list  5  of key words, referred to in the following as first key word list  5 . In this context, first key word list  5  is stored in memory  15 . In the process, key word lists for a plurality of texts may be stored in memory  15 , such as a second key word list  25 , as shown in FIG. 1, for which selection means  45  may select word groups from a second text. To compile a plurality of texts, a shared key word list  30  is provided in memory  15 , for which selection means  45  likewise select word groups from the resultant total text. Connected to selection means  45  are summing means  60  which add the frequencies of occurrence of the same word groups in various key word lists of texts to be merged and likewise store them in shared key word list  30  in association with the particular corresponding word group. All key word lists  5 ,  25 ,  30  are stored in memory  15 . Also stored in memory  15  is a text component list  20 , which includes predefined text components used by partitioning means  35  for partitioning first text  10  specified in this example.  
     [0013] The method according to the present invention is first described for generating key word groups for first text  10  on the basis of the flow chart according to FIG. 2. At a program point  100 , first text  10  is fed to partitioning means  35 . Partitioning means  35  scan first text  10  for predefined text components stored in text component list  20 . These text components include punctuation marks and words that are meaningless in terms of content, such as articles, linking words, such as “and” and “or” and the like. These predefined text components are detected in first text  10  by partitioning means  35  and replaced by delimiter marks, such as hash symbols. In this manner, one obtains partitioned text  11 , in which text chunks are separated from one another by the mentioned delimiter marks. The program subsequently branches to a program point  105 .  
     [0014] At program point  105 , a first number n of directly adjacent words, for example n=5, is preset by search tool  50 . The program subsequently branches to a program point  110 .  
     [0015] At program point  110 , the remaining text chunks in partitioned text  11  are scanned by search tool  50  for all word groups composed of precisely n directly adjacent words, i.e., words which are not separated from one another by delimiter marks. The ascertained word groups are buffer stored, for example likewise in memory  15 , or, as assumed in this exemplary embodiment, in search tool  50  itself. The program subsequently branches to a program point  115 .  
     [0016] At program point  115 , means  40  for ascertaining the frequency of occurrence, determine the frequency of occurrence of one of the word groups buffer stored in search tool  50  and having n directly adjacent words in partitioned text  11 . Means  39  for detecting a noun may additionally be provided before means  40  for determining frequency of occurrence, as indicated by dotted lines in FIG. 1. Prior to ascertaining the frequency of occurrence, means  39  for detecting a noun verify, in this context, at program point  115 , whether, in each instance, the last word of the word groups buffer stored in search tool  50  is a noun. The word groups, which end with a noun, subsequently undergo the frequency-of-occurrence determination, as described. The remaining word groups are erased from the intermediate memory of search tool  50  and from partitioned text  11  by deletion device  55 , for which purpose, means  39  for detecting a noun are linked, for this option, to deletion device  55 , as likewise illustrated by a dotted line in FIG. 1.  
     [0017] A noun may be recognized as the last word of a word group in a German text when the scan shows that the word begins with an upper case letter. If this is the case, then, in all probability, it is a noun. When the word does not begin with an upper case letter, then it is certainly not a noun, leaving spelling errors out of consideration.  
     [0018] Alternatively, using a lexicographical method, one may check whether the word is listed in a dictionary as a noun-positive selection- or as an adjective, adverb, or verb-negative selection. To this end, in its memory  15 , computer unit  1  may include a dictionary memory. Negative selection means: the word is a noun when it does not match the entries in a dictionary memory which include adjectives, adverbs, and verbs. For a positive selection, it holds that: when the word to be examined matches an entry of the dictionary memory characterized as a noun at the time the word to be examined is compared to the entries of the dictionary memory, then the word is recognized as being a noun.  
     [0019] This method is, in fact, somewhat more intricate, but is, however, the more precise and less faulty, the greater the number of nouns there are listed in the dictionary memory. It is not only suited for German-language texts, but particularly also for texts in those languages in which nouns do not differ in their form from the other words, thus, for example, not by upper case letters at the beginning of the word.  
     [0020] If indicated, the dictionary memory should also include, i.e., as a function of the language used, all possible declension forms of the nouns, in order to be able to recognize a word to be scanned independently of its declension form. Another possibility involves reducing the word to be scanned to its stem, for example by lemmatization, as known, for example, from the publication,  “ Development of a Stemming Algorithm” by Lovins, B. J., Mechanical Translation and Computational Linguistics, 11, 22-31 (1968).  
     [0021] The entries of the dictionary memory should then likewise be available as words that have been reduced to their stem. In the case of nouns, this is the same for all possible declension forms. In this case, the word stem of the word to be scanned is compared to the word stems in the dictionary memory and recognized as a noun when it agrees with a word stem characterized as a noun in the dictionary memory.  
     [0022] For example, the word stem of the word “Krankenhäuser” (hospitals) is “Krankenhaus” (hospital) and, in this case, also corresponds to the basic form of this word. Typically, however, the number of letters of the word stem of a word is less than the number of letters of the basic form of the word. In the case of a negative selection, the explanations apply accordingly.  
     [0023] When a lexicographical method based on the word stem principle is applied, it is possible to economize on memory space in the dictionary memory, since the need is eliminated for storing all declension forms existing in the language being used. The program subsequently branches to a program point  120 . At program point  120 , selection means  45  check whether the ascertained frequency of occurrence of the particular word group is greater than a first predefined value. If this is the case, the program branches to program point  125 , otherwise the program branches to a program point  130 .  
     [0024] At program point  125 , the appropriate word group is entered into first key word list  5  in memory  15  and thus forms a key word group that characterizes first text  10 . The program subsequently branches to program point  130 .  
     [0025] At program point  130 , selection means  45  prompt deletion device  55  to delete the last scanned word group from the intermediate memory of search tool  50 , for which purpose, search tool  50  is linked to deletion device  55 , as shown in FIG. 1. The program subsequently branches to a program point  133 .  
     [0026] At program point  133 , means  40  for ascertaining the frequency of occurrence check whether a further word group is stored in the intermediate memory of search tool  50 . If this is the case, then the program branches back to program point  115 , and the program is run through again, from program point  115  on, using, in each instance, the new word group extracted from the intermediate memory of search tool  50 . Otherwise, the program branches to a program point  135 .  
     [0027] At program point  135 , selection means  45  prompt deletion device  55  to delete all word groups entered in first key word list  5  from partitioned text  11 . The program subsequently branches to a program point  140 .  
     [0028] At program point  140 , the first predefined number of directly adjacent words is decremented in search tool  50  by one, so that a second predefined number of directly adjacent words is derived which is smaller by one than the first predefined number of words. A decrementation by more than one could, of course, also follow, for example. In the following, however, one shall assume a decrementation of the first predefined number of directly adjacent words, by one. The program subsequently branches to a program point  145 .  
     [0029] At program point  145 , search tool  50  checks whether the second predefined number is smaller than or equal to zero. If this is the case, the program is exited, and the first key word list  5  is complete. Otherwise, the program branches back to program point  110 , and the program is run through again from program point  110  on. However, this is done for word groups composed of exactly the second predefined number of directly adjacent words. In the process, the program is repeated from program point  110  on using a decremented number of directly adjacent words at program point  140 , for the word groups to be found in partitioned text  11 , until it is exited via the yes branch at program point  145 , i.e., until condition n smaller or equal to zero is fulfilled.  
     [0030] It may be provided, together with the key word or with the key word group, to also store the corresponding ascertained frequency of occurrence in memory  15  or in first key word list  5 , in association with the corresponding key word or the corresponding key word group. However, this represents merely one option.  
     [0031] The program execution may be coordinated by a control (not shown in FIG. 1) of computer unit  1 , which is linked to memory  15 , to partitioning means  35 , to search tool  50 , to means  40  for ascertaining frequency of occurrence, to deletion device  55 , to selection means  45 , and to summing means  60 .  
     [0032] Summing means  60  are only optionally necessary.  
     [0033] It may be provided for first text  10  to include a plurality of documents. For such a case, program points  120  and  125  in accordance with FIG. 2 are replaced with the sequence in accordance with FIG. 3. In this context, means  40  for ascertaining frequency of occurrence check at a program point  200  whether the frequency of occurrence of the word group just scanned exceeds a second predefined value in at least one predefined number of the documents in partitioned text  11 . If this is the case, the program branches to a program point  205 . Otherwise, the program is exited in accordance with FIG. 3 and branches further to program point  130  in accordance with FIG. 2. Program point  205  then corresponds to program point  120  in accordance with FIG. 2, i.e., means  40  for ascertaining frequency of occurrence check whether the word group just scanned in the total text occurs with a frequency that is greater than the first predefined value. If this is the case, the program branches to a program point  210 . Otherwise, the program is exited in accordance with FIG. 3 and branches further to program point  130  in accordance with FIG. 2. Program point  210  in accordance with FIG. 3 corresponds then, in turn, to program point  125  in accordance with FIG. 2, where selection means  45  enter the word group just selected into first key word list  5 . The program is subsequently exited in accordance with FIG. 3, and branches over further to program point  130  in accordance with FIG. 2. The predefined number of documents for the test procedure at program point  200  is selected to be smaller than or equal to the total number of documents in first text  10 . The second predefined value for the test at program point  200  may be selected to equal zero, for example. This means that a word group that occurs with a frequency of occurrence in first text  10 , thus the entire text, which is greater than the first predefined value, is only entered into first key word list  5  when it also occurs at least once in the predefined number of documents. In the process, the second predefined value may also be selected to be greater than zero. This, prevents a word group from being entered into first key word list  5  for the sole reason that the author of one of the documents has a preference for this word group, while it does not turn up in the remaining documents and is, therefore, also not representative or characteristic of the entire text, thus, of first text  10 .  
     [0034] The following describes how first key word list  5  may be adapted when a second text is added to first text  10  after first key word list  5  was already completely formed. It is a question, therefore, of updating first key word list  5 . For this case, a flow chart is shown in FIG. 4. Before executing the program for updating first key word list  5  in accordance with FIG. 4, it is necessary to generate a second key word list  25  for the second text. This second key word list may likewise be stored in memory  15 , as shown in FIG. 1. In the process, second key word list  25  may be formed from the second text in the same manner as described for first text  10 . First key word list  5  is updated by forming a shared key word list  30 , which may likewise be stored in memory  15  in accordance with FIG. 1 and which includes key words or.key word groups that are characteristic or representative of first text  10  and of the second text. During this updating process, a list number m is first set to  1  at a program point  300  in accordance with FIG. 4. The program subsequently branches to a program point  305 . At program point  305 , from the key word list having list number m equals  1 , selection means  45  extract a key word or a key word group from first key word list  5 . The program subsequently branches to a program point  310 . At program point  310 , selection means  45  check whether the key word taken from first key word list  5  is also contained in the key word list having the list number incremented by  1 , thus in second key word list  25 . If this is the case, the program branches to program point  315 , otherwise the program branches to program point  320 .  
     [0035] At program point  315 , selection means  45  prompt summing means  60  to add the frequencies of occurrence of the key word, i.e., of the key word group just scanned, from the two key word lists  5 ,  25 . The precondition for this is the presence of summing means  60  and the storing of the frequency-of-occurrence values in association with the corresponding key word or key word group in the corresponding key word list or in memory  15 . The program subsequently branches to program point  320 .  
     [0036] At program point  320 , the key word or key word group just. scanned is entered into shared key word list  30 , along with its frequency of occurrence, the frequency of occurrence being either the cumulative frequency of occurrence ascertained at program point  315  or, in the case that program point  315  was skipped, the frequency of occurrence associated with the key word or key word group in first key word list  5 . In addition, at program point  320 , the key word just scanned is marked in first key word list  5  and, in the same manner, in second key word list  25 , in the case that it is present there as well. This is prompted by selection means  45 . The program subsequently branches to a program point  325 .  
     [0037] At program point  325 , selection means  45  check whether a key word or a key word group without any marking is present in first key word list  5 . If this is the case, this key word or this key word group is selected, and the program branches back to program point  310 . Otherwise, the program branches to a program point  330 .  
     [0038] At program point  330 , list number m is incremented by one. The program subsequently branches to a program point  335 .  
     [0039] At program point  335 , selection means  45  check whether a key word or a key word group without marking is present in the key word list having the list number incremented by one, thus, in this case, in second key word list  25 . If this is the case, the program branches to program point  340 , otherwise the program is exited. At program point  340 , selection means  45  prompt the selection of such a key word or such a key word group without marking, from second key word list  25 , and enter them into shared key word list  30  with the corresponding frequency of occurrence that is likewise stored in second key word list  25  or in memory  15 . This key word or this key word group is subsequently marked in second key word list  25 , and the program branches back to program point  335 .  
     [0040] In this manner, by generating the shared key word list  30  in the manner described, first key word list  5  is able to be updated very quickly when adding a second text to first text  10 , since there is no need to generate key words for the total text formed by first text  10  and the second text. Prior to updating first key word list  5 , it is merely necessary to generate second key word list  25  for the second text.  
     [0041] The control (not shown in FIG. 1) may coordinate computer unit  1  for the sequence illustrated in FIGS. 3 and 4, as well. Also in the case of shared key word list  30 , the frequency of occurrence associated with the corresponding key words or key word groups is also stored in memory  15  or in shared key word list  30 , in association with the corresponding key word or key word group. In this context, in the method described in accordance with FIG. 4, the frequency of occurrence of a key word or of a key word group in shared key word list  30  always represents the sum of the frequencies of occurrence of this key word or of this key word group of first key word list  5  and of second key word list  25 .  
     [0042] Word groups in partitioned text  11  may be deleted by deletion device  55  in that these word groups are likewise replaced by one or more delimiter marks. The text components predefined by the text component list may likewise by replaced by delimiter marks in that such text components are replaced with more than one symbol by one or more delimiter mark(s) for partitioning the text to be scanned, in order to generate the correspondingly partitioned text.  
     [0043] In the described method, longer word groups are entered into the corresponding key word list with a higher priority than the shorter ones.  
     [0044] It may be provided for the first predefined value and the first predefined number of directly adjacent words to be assigned to fixed memory in computer unit  1  or to be input by a user via an input unit (not shown in FIG. 1). The same holds for the decrementation increment for the predefined number of directly adjacent words. The predefined number of documents to be checked at program point  200  in accordance with FIG. 3, as well as the second predefined value may either be stored in fixed memory in computer unit  1  or entered by the user at the input unit.  
     [0045] When parameters to be preset may be input at an input unit of computer unit  1  for the sequence of the method according to the present invention in accordance with FIGS. 2, 3, and  4 , then various values for these parameters may also be predefined for various texts, for each of which a key word list is to be generated. It may also be provided for a plurality of key word lists to be created for one single text. For these lists, various values are selected for the parameters to be preset, for example a variable entry for the first predefined value. When this is reduced following. generation of a first key word list for this text, then a second key word list including rarer key words or key word groups may be created for this text, by increasing the first predefined value. The texts to be scanned may be available, for example, as ASCII files or as HTML pages of the Internet.  
     [0046] It may be provided to reproduce key word lists  5 ,  25 ,  30  of memory  15  on an optical and/or acoustical reproduction device (not shown in FIG. 1). The generated key word lists may be used, for example, for searching for new texts which relate to the same special field as the already scanned first text  10  and for which, therefore, the same key words or key word groups are representative. For that reason, key word lists of this kind may be used, for example, when conducting patent searches. To conduct such a search, key words or key word groups may also be used directly from memory  15 . For this purpose, computer unit  1  may be connected to the Internet, for example. This eliminates the need for the user to input the key words or key word groups. Provision may be made for the user to select the key words or key word groups offered to him on the reproduction device using a menu driven interface, for example a mouse pointer or cursor control, and to confirm the same using a confirmation key when searching for further texts for which the key words or key word groups from the stored key word list(s)  5 ,  25 ,  30  are characteristic.  
     [0047] The key word lists in memory  15  may be stored in memory  15  in association with an identification which characterizes the corresponding text. In this way, the key word groups may also be reproduced in association with the corresponding text. Thus, on the reproduction device, the user is able to discern which text the key word list just reproduced belongs to. By updating first key word list  5  using shared key word list  30  in accordance with the exemplary embodiment of FIG. 4, it is possible to recognize and follow trends in the characterization of texts which belong to one special field, for example, by ascertaining new accesses into shared key word list  30 , or by determining key words or key word groups in shared key word list  30  whose frequency of occurrence increases in response to the addition of new texts in such a way that the increase exceeds a predefined value. When updating first key word list  5  by generating shared key word list  30 , as described, it is ensured that key words or key word groups located once in first key word list  5  or second word list  25  are retained and are stored in shared key word list  30 .  
     [0048] One further application provides for reproducing a text on a display device of the computer unit and for marking in color key words or key word groups of the corresponding key word list in this text, it being possible for the user to select previously single or all key words or key word groups from this key word list to be marked in color at the input unit of computer unit  1 .  
     [0049] It may also be provided for the frequency-of-occurrence values ascertained by means  40  for determining the frequency of occurrence for the word groups ascertained by search tool  50  from partitioned text  11 , to be stored in the form of a frequency-of-occurrence table in memory  15  or in means  40  for determining the frequency of occurrence or in search tool  50  itself and, in fact, in association with the corresponding word group.  
     [0050] It may also be alternatively provided that first text  10  is generated from a third text and a fourth text, a frequency-of-occurrence table having been stored for the third text and the fourth text, respectively, in the described manner for the word groups ascertained by search tool  50 , in means  40  for ascertaining frequency of occurrence in search tool  50 . In this context, the assumption should be, for example, that the frequency-of-occurrence table for the third text was created before the frequency-of-occurrence table for the fourth text, and that first key word list  5  was created for the third text. Shared key word list  30  shall, at this point, be ascertained for first text  10  encompassing the third text and the fourth text. This is achieved in that the two frequency-of-occurrence tables are cumulatively superposed to form one shared frequency-of-occurrence table which may likewise be stored in memory  15  or in means  40  for ascertaining the frequency of, occurrence. In the process, the frequencies of occurrence of word groups are added together when the word groups are listed both in the first frequency-of-occurrence table assigned to, the third text, as well as. in the second frequency-of-occurrence table assigned to the fourth text, and the cumulative frequency of occurrence is assigned in each instance in the shared frequency-of-occurrence table to the corresponding word group. The particular frequency of occurrence of the word groups, which are only stored in the first frequency-of-occurrence table or only in the second frequency-of-occurrence table, are entered, unchanged, in the shared frequency-of-occurrence table in association with the corresponding word group. Shared key word list  30  is then generated in the manner described in accordance with FIGS.  2  or  3 , on the basis of the shared frequency-of-occurrence table for first text  10 . The advantage over the specific embodiment described in accordance with FIG. 4 lies in that, when using the shared frequency-of-occurrence table, those word groups, which yield the frequency of occurrence necessary for inclusion in shared key word list  30 , when the third text and fourth text are jointly considered, may also be entered into shared key word list  30 , and whose frequency of occurrence in the third and in the fourth text, each considered individually, lies below the required frequency-of-occurrence threshold, thus the first predefined value, however.