TW202242848A - Knowledge entity identification method and knowledge entity identification device - Google Patents

Abstract

A knowledge entity identification method includes steps of receiving an object text to be analyzed and metadata, which the object text includes a candidate word; comparing the candidate word with words in a knowledge database to obtain a plurality of entity name associated with the candidate word, which each entity name includes entity description data; comparing the entity description data in the knowledge database with the metadata to obtain a comparing result; and setting the entity name associated with the candidate word according to the comparing result as an output classification of the candidate word in the object text.

Description

Knowledge entity recognition method and knowledge entity recognition device

This case is about an electronic device and its method, and in particular about a knowledge entity recognition device and method.

The traditional knowledge management method is to manually mark all the files one by one by experts. With the development of technology, the current data labeling method can analyze grammar and semantics through natural language technology. However, such corpus analysis cannot enable machines to understand new words, but needs to be labeled by experts. The existing training process using labeled data is too lengthy and inflexible, and it is difficult for established knowledge management systems to train knowledge management systems in new fields based on existing data. The construction of knowledge management systems for different fields It requires a fairly high training cost.

In view of this, the knowledge management system is a tool with considerable management momentum, but there is still a lack of efficient construction methods, and there is still room for improvement in the execution accuracy of the knowledge management system. Accordingly, how to provide an efficient system construction method and high-precision knowledge management is a technical problem that those skilled in the art want to solve.

According to one embodiment of this case, a knowledge entity recognition method is disclosed, which includes the following steps: receiving the target text to be parsed and metadata, wherein the target text includes candidate words; using the candidate words to compare in the knowledge base to obtain from obtaining a plurality of entity names associated with the candidate word in the knowledge base, wherein each of the entity names has corresponding entity description data; comparing the entity description data and metadata in the knowledge base to obtain a comparison result; and according to the comparison result Set the entity name associated with the candidate word in the knowledge base as the output category of the candidate word in the target text.

According to another embodiment, a knowledge entity recognition device is disclosed, which includes a knowledge entity candidate generation module, a knowledge entity verification and enhancement module, and a knowledge entity classification module. The knowledge entity candidate generation module is configured to receive the target text and metadata to be parsed, and use the candidate words of the target text for comparison in the knowledge base to obtain a plurality of entities associated with the candidate words from the knowledge base Name, where each entity name has corresponding entity description data. The knowledge entity verification and enhancement module is coupled to the knowledge entity candidate generation module, wherein the knowledge entity verification and enhancement module is configured to compare entity description data and metadata in the knowledge base to obtain a comparison result. The knowledge entity classification module is coupled to the knowledge entity verification and enhancement module, wherein the knowledge entity classification module is configured to set the entity name associated with the candidate word in the knowledge base as the output of the candidate word in the target text according to the comparison result Classification.

The following disclosure provides many different embodiments in order to implement the different features of the present disclosure. Embodiments of components and arrangements are described below to simplify the present case. Of course, these examples are exemplary only and are not intended to be limiting. For example, the use of terms such as "first" and "second" in this case to describe components is only used to distinguish the same or similar components or operations. These terms are not used to limit the technical components of this case, nor are they intended to Restricts the order or sequence of operations.

Please refer to FIG. 1 , which shows a block diagram of a knowledge entity recognition device 100 according to an embodiment of the present invention. The knowledge entity recognition device 100 is used for recognizing a target object in an input data 102 and providing a recognized output data 104 . For example, the knowledge entity recognition device 100 analyzes an input text, sentence, paragraph and other data to perform named entity recognition (Named Entity Recognition). In one embodiment, the input data 102 received by the knowledge entity recognition device 100 includes target text and domain metadata. The target text is the data to be parsed. Metadata is data used to assist the analysis of the target text, which may be categories and keywords designed by the user in advance.

In one embodiment, the knowledge entity recognition device 100 is coupled to the knowledge base 500 . The knowledge base 500 is coupled to an external common knowledge base 600 . The external general knowledge base 600 is, for example, a database with different formats and domain content, such as Wikipedia, special dictionary, domain expert knowledge, etc. The knowledge base 500 may store knowledge defined internally and/or knowledge data established through data from the external general knowledge base 600 . For example, the knowledge base 500 is provided with an analysis and storage module 502 . The parsing and storage module 502 can read the data of the external general knowledge base 600, and convert the external data into a data structure in a specific format, such as formally processing the external data and domain expert knowledge data, so that the data stored in the knowledge base 500 It can be provided for use when the knowledge entity recognition device 100 recognizes the target text.

In one embodiment, the knowledge entity identification device 100 includes a knowledge entity candidate generation module 112 , a knowledge entity verification and enhancement module 114 , and a knowledge entity classification module 116 . The knowledge entity candidate generation module 112 is electrically coupled to the knowledge entity verification and enhancement module 114 . The knowledge entity verification and enhancement module 114 is electrically coupled to the knowledge entity classification module 116 . In order to facilitate understanding of the content of this case, please refer to Figure 1 and Figure 2 for the following description. FIG. 2 shows a flow chart of a knowledge entity recognition method 200 according to an embodiment of the present application. The knowledge entity recognition method 200 can be executed by the knowledge entity recognition device 100 in FIG. 1 .

In step S210, the knowledge entity candidate generation module 112 receives the target text and metadata to be parsed.

In one embodiment, the target text to be analyzed is text data to be analyzed, including one or more sentences or paragraphs. On the other hand, the metadata includes a plurality of categories (keys), and each category includes a plurality of key words (values). The user can define all categories of metadata and keywords of each category in advance, and input them into the knowledge entity recognition device 100 simultaneously with the target text. To facilitate the description of the content of this case, the target text mentioned below is an example of the sentence "An apple a day keeps the doctor away." and the metadata shown in Table 1. It should be noted that this case is not limited to the content of this example.

Table 1: Metadata category (key) Key words (value) FRUIT fruit, juicy, tree, … MEAT animal, hunt, … DESSERT sugar, sweet, …

In one embodiment, the knowledge entity candidate generation module 112 executes natural language processing technology to extract nouns or noun phrases of the target text. These extracted nouns or noun phrases are used as candidate words of the target text. Following the above example of the target text "An apple a day keeps the doctor away.", the candidate words extracted from the target text include "apple", "day" and "doctor". The number of candidate words for the target text varies depending on the content of the target text. In one embodiment, the target text includes one or more candidate words. In the example of the target text, the number of candidate words is 3.

In step S220 , the knowledge entity candidate generating module 112 uses the candidate words of the target text to perform comparison in the knowledge base 500 to obtain a plurality of entity names associated with the candidate words from the knowledge base 500 .

In one embodiment, the knowledge entity verification and enhancement module 114 compares these candidate words in the knowledge base 500 one by one. The knowledge base 500 records a plurality of entity data. The data structure of each entity data includes but not limited to number, entity name, entity description, entity type, etc., as shown in Table 2.

Table 2: Knowledge Base serial number entity name entity description Entity type 0 Apple Inc. Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Mobile phone manufacturers; Companies in the NASDAQ-100 Index. 1 Apple An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the genus Malus. Fruits; Malus; Plants. 2 Pineapple The pineapple (Ananas comosus) is a tropical plant with an edible fruit and the most economically significant plant in the family Bromeliance. Fruits; Ananas; Plants. 3 Apple, Oklahoma Apple, Oklahoma is an unincorporated community located near Hugo Lake and State Highway 93 in Choctaw County, Oklahoma. Oklahoma geography stubs. … N Orange The orange is the fruit of various citrus species in the family Rutaceae (see list of plants known as orange) Fruits; Tropical agriculture.

Following the above example, when the candidate word is "apple", the knowledge entity verification and enhancement module 114 compares the candidate word "apple" in the knowledge base 500 in Table 2, and obtains the plural associated with "apple". entity names, such as "Apple Inc." for number 0, "Apple" for number 1, "Pineapple" for number 2, and "Apple, Oklahoma" for number 3. In an embodiment, the obtained entity names numbered 0 to 3 may be recorded in the candidate list of the candidate word "apple". On the other hand, since the entity name "Orange" with number N is not identical/similar to the candidate word "apple", the entity name "Orange" with number N will not be recorded in the candidate list for the candidate word "apple" middle.

In one embodiment, the information retrieval method for searching and comparing the candidate words in the knowledge base 500 may be a term frequency-inverse document frequency (term frequency-inverse document frequency, tf-idf) method or other data mining/term frequency statistics method, this case is not limited to this.

In step S230 , the knowledge entity verification and enhancement module 114 compares the entity description data and metadata in the knowledge base 500 to obtain a comparison result.

In one embodiment, words are searched in the entity description data to obtain more content description information, which is used as enhanced information of candidate words for subsequent use by the knowledge entity classification module 116 .

In the above example, the entity names recorded in the candidate list of the candidate word "apple" are 4 pieces of data including "Apple Inc.", "Apple", "Pineapple" and "Apple, Oklahoma". Further, the entity description material corresponding to each entity name in the candidate list is retrieved one by one according to the metadata received in the aforementioned step S210. Take the metadata category "FRUIT" and its keywords "fruit, juice, tree" as an example (as shown in Table 1 above). The knowledge entity verification and enhancement module 114 uses the entity description data corresponding to the keyword "fruit" and the entity name "Apple" as "An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the genus Malus." to search and compare to determine whether any word matches "fruit", and accumulate once when a matching word is obtained. In this embodiment, the category "FRUIT" has three keywords, and the same retrieval and matching are performed on the three keywords respectively, and finally the sum of matching times of this category is obtained. For example, the keyword "fruit" of the category "FRUIT" can get 1 match in the entity description data corresponding to the entity name "Apple"; the keyword "juicy" of the category "FRUIT" can be found in the entity name There are 0 matching times in the entity description data corresponding to "Apple"; the keyword "tree" of the category "FRUIT" can get 2 matching times in the entity description data corresponding to the entity name "Apple". Therefore, the total number of matches for the category "FRUIT" against the entity name "Apple" is 3.

By analogy, the total matching times of the keywords "animal, hunt" of the category "MEAT" in the entity description data corresponding to the entity name "Apple" is 0. The total matching times of the keyword "sugar, sweet" of the category "DESSERT" in the entity description data corresponding to the entity name "Apple" is 0. It can be seen that among the three categories of metadata input in step S210, the category "FRUIT" has the largest total number of matches. Therefore, the category "FRUIT" of the metadata is the comparison result of the target text. At the same time, the entity name "Apple" which is most relevant to the category "FRUIT" is set as the most relevant entity name.

In one embodiment, the comparison between metadata and description data of each entity may be a similarity comparison method (Cosine similarity). By using the metadata to search the entity description data in the knowledge base 500, the entity name closest to the metadata is screened out by the similarity comparison method.

In step S240 , the knowledge entity classification module 116 sets the entity name associated with the candidate word in the knowledge base 500 as the output classification of the candidate word in the target text according to the comparison result.

In the above example, the comparison result of the candidate word category in the target text is "FRUIT". Further, the knowledge entity classification module 116 compares the comparison result "FRUIT" with the entity category (ie "Fruits; Malus; Plants") corresponding to the most relevant entity name (ie "Apple") in the knowledge base 500 . Since the word "Fruits" matching the comparison result "FRUIT" can be found in the entity category, it can be verified that the comparison result "FRUIT" found above is the output classification of the candidate word in the target text.

In one embodiment, the data enhancement results of the candidate words obtained in step S230 and the categories and keywords defined by the user in the metadata can be input into a text classification model (not shown in FIG. 1 ) to Classification can be used to determine that the candidate words are the knowledge entities of the target text, and classify the knowledge entities into corresponding categories to obtain the final knowledge entities and their categories.

Please refer to Figure 1 and Figure 3 together. FIG. 3 shows a flowchart of a knowledge entity recognition method 300 according to an embodiment of the present application. The knowledge entity recognition method 300 can be executed by the knowledge entity recognition device 100 in FIG. 1 .

In step S310 , the knowledge entity verification and enhancement module 114 uses the candidate words of the target text to perform comparison in the knowledge base 500 , and obtains a plurality of entity names sorted according to the degree of similarity.

In one embodiment, the target text can be extracted by the knowledge entity candidate generation module 112 using a natural language processing technique to extract candidate words. Following the example of the above target text "An apple a day keeps the doctor away.", the candidate word "apple" is compared with all entity names in the knowledge base 500 in Table 2. The entity name in knowledge base 500 that is most similar to the candidate word "apple" has the highest rank. Based on the similarity of each entity name, the sorted entity names can be obtained by sorting from high to low. The sorted entity names are shown in Table 3, the entity name with the number 1 is the first rank, the entity name with the number 0 is the second rank, and so on. After the similarity comparison, there are 4 sorted entity names screened from the knowledge base 500, which are the same/similar to the candidate words.

Table three serial number entity name Entity description data Entity type 1 Apple An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the genus Malus. Fruits; Malus; Plants. 0 Apple Inc. Apple Inc. is an American multinational technology company headquartered in Cupertino, California. Mobile phone manufacturers; Companies in the NASDAQ-100 Index. 3 Apple, Oklahoma Apple, Oklahoma is an unincorporated community located near Hugo Lake and State Highway 93 in Choctaw County, Oklahoma. Oklahoma geography stubs. 2 Pineapple The pineapple (Ananas comosus) is a tropical plant with an edible fruit and the most economically significant plant in the family Bromeliance. Fruits; Ananas; Plants.

In step S320 , the knowledge entity verification and enhancement module 114 compares the keywords in each category of the metadata with the words in the entity description data corresponding to the sorted entity names to obtain a comparison result. In some embodiments, the knowledge entity verification and enhancement module 114 will compare the keywords of each category of metadata with the words in the entity description data corresponding to the sorted entity names to calculate the entity description data The number of word matches that are the same or similar to the keywords in each category, so that each category has a corresponding number of word matches.

In one embodiment, the metadata includes a plurality of categories, and each category includes a plurality of keywords. For example, the metadata includes a first category "FRUIT" and a second category "MEAT". The first category "FRUIT" includes key words "fruit", "juicy" and "tree". The second category "MEAT" includes the key words "animal" and "hunt". The third category "DESSERT" includes the key words "sugar" and "sweet".

In one embodiment, the keyword "fruit" is included in the sorted first entity description data "An apple is an edible fruit produced by an apple tree (Malus domestica). Apple trees are cultivated worldwide and are the most widely grown species in the genus Malus." and got 1 match. Similarly, the keywords "juicy" and "tree" are compared in the description data of the first entity respectively, and 0 and 2 matching words are obtained. In other words, the sum of matching terms for the first category "FRUIT" associated with the first entity name is 3. By analogy, the sum of matching words of the second category "MEAT" associated with the first entity name is 0. Table 4 shows the sum of matching words between keywords of each category and the entity description data of the first entity name "Apple".

Table four: metadata Term Matches for First Entity Description Data The first category "FRUIT" 3 The second category "MEAT" 0 The third category "DESSERT" 0

In step S330 , the knowledge entity verification and enhancement module 114 sets the category with the largest number of word matches as the output category of the candidate word in the target text.

Following the above example, the first category has the largest number of word matches (ie 3), therefore, the first category "FRUIT" will be set as the output category of the candidate word in the target text.

It is worth mentioning that step S320 and step S330 will also calculate the sum of the word matching numbers of the second entity names sorted by the first category, the second category and the third category of the metadata, and use the metadata of the first category Sum of the number of word matches for the third entity name sorted by category 1, category 2, and category 3, and the name of a fourth entity sorted by category 1, category 2, and category 3 of metadata Calculate the sum of its word matches. In other words, all categories of metadata are matched against each sorted entity name to get the sum of word matches for all categories for each entity name. For the sake of brevity, the description of the matching steps is not repeated here.

In step S340 , the knowledge entity verification and enhancement module 114 compares the output classification with the entity type corresponding to the sorted entity name in the knowledge base 500 to verify whether the output classification of the candidate word in the target text is correct.

Following the above example, the first category "FRUIT" has the largest number of word matches, so the output category of the candidate word in the target text is set as "FRUIT". In step S340 , to verify whether the output classification is correct, the output classification “FRUIT” is further compared with the first entity type. As shown in Table 3, the first entity type includes “Fruits”, “Malus” and “Plants”. Since "Fruits" of the first entity category can match the output classification "FRUIT", it can be verified that this output classification is a correct result.

In one embodiment, knowledge entity recognition 100 can be implemented as but not limited to portable electronic devices, mobile phones, tablet computers (tablet computers), personal digital assistants (PDA, personal digital assistant), wearable devices or notebook computers and other devices.

In one embodiment, the knowledge entity identifier 100 at least includes a processor (not shown in FIG. 1 ), a storage medium (not shown in FIG. 1 ), and an input/output interface (not shown in FIG. 1 ). The processor is used to operate and control the knowledge entity candidate generation module 112 , the knowledge entity verification and enhancement module 114 and the knowledge entity classification module 116 . The storage medium is used to store a plurality of program instructions and temporary data during the execution of the instructions. The I/O interface is coupled to the processor for receiving an input data 102 and sending an output data 104 .

The processor can be implemented as but not limited to a central processing unit (central processing unit, CPU), a system single chip (System on Chip, SoC), an application processor, an audio processor, a digital signal processor (digital signal processor, DSP) ) or a processing chip or controller for a specific function.

The storage medium can be implemented as but not limited to random access memory (Random Access Memory, RAM) or non-volatile memory (such as flash memory (Flash memory), read only memory (Read Only Memory, ROM) , hard disk drive (Hard Disk Drive, HDD), solid state drive (Solid State Drive, SSD) or optical storage, etc.

In one embodiment, the text classification model can be established by an artificial intelligence model that can be used in multiple sub-algorithms, including artificial neural network (Artificial Neural Network, ANN), supervised learning in machine learning (Machine learning) ( Supervised learning), where supervised learning includes support vector machine (Support Vector Machine, SVM), regression analysis and statistical classification algorithms.

In one embodiment, the present application provides a non-transitory computer-readable recording medium capable of storing a plurality of program codes. After the program code is loaded into the processor of the knowledge entity recognition device 100 as shown in FIG. 1 , the processor executes the program code and performs the steps as shown in FIG. 2 and FIG. 3 .

Compared with the prior art, the knowledge entity recognition method and device in this case can analyze more knowledge entities and achieve a high recall rate under the premise of the same number of knowledge entities to be analyzed. And, on the premise that the number of analyzed knowledge entities is the same, in this case, more correct knowledge entities can be obtained to achieve high precision.

To sum up, in this case, by inputting metadata while inputting the target text to be marked, and after searching the entity name in the knowledge base, further retrieving the entity description data of the entity name as verification can improve the recognition The accuracy of the classification of knowledge entities in the target text. In addition, the knowledge entity recognition device and method of this case can be applied to the annotation of a large number of documents. When the document to be annotated is changed to a different field, only the corresponding knowledge base needs to be switched to achieve the switching of the field. Moreover, in terms of expansion, it can be updated only by adding new vocabulary to the knowledge base. In addition, such a method can reduce the cost of human labeling and the burden of experts, save a lot of manual labeling work, and have various subsequent applications (entering articles can automatically label their categories and keywords).

The above content summarizes the characteristics of several embodiments, so that those skilled in the art can better understand the aspect of the present application. Those skilled in the art should understand that without departing from the spirit and scope of the present application, the above content can be easily used as a basis for designing or modifying other changes, so as to achieve the same purpose and/or realize the embodiments described herein. same advantage. The above content should be understood as an example of this case, and the scope of protection should be based on the scope of the patent application.

100: Knowledge entity recognition device 102: Input data 104: Output data 112: Knowledge entity candidate generation module 114: Knowledge entity verification and enhancement module 116: Knowledge entity classification module 200, 300: knowledge entity recognition method 500: knowledge base 502: Analysis and storage module 600: external general knowledge base S210~S240, S310~S340: steps

When the following detailed description is read in conjunction with the accompanying drawings, it will help to better understand the aspect of the disclosed document. It should be noted that, as required by illustrative practice, features in the drawings are not necessarily drawn to scale. In fact, the dimensions of the various features may be arbitrarily increased or decreased for clarity of discussion. FIG. 1 is a block diagram of a knowledge entity recognition device according to an embodiment of the present application. FIG. 2 shows a flow chart of a knowledge entity recognition method according to an embodiment of the present application. FIG. 3 is a flow chart of a knowledge entity recognition method according to an embodiment of the present application.

Domestic deposit information (please note in order of depositor, date, and number) none Overseas storage information (please note in order of storage country, institution, date, and number) none

100: Knowledge entity recognition device

102: Input data

104: Output data

112: Knowledge entity candidate generation module

114: Knowledge entity verification and enhancement module

116: Knowledge entity classification module

500: knowledge base

502: Analysis and storage module

600: external general knowledge base

Claims (10)

A knowledge entity recognition method, comprising: receiving a target text to be parsed and metadata, wherein the target text includes a candidate word; using the candidate word for comparison in a knowledge base to obtain a plurality of entity names associated with the candidate word from the knowledge base, wherein each of the entity names has a corresponding entity description data; comparing the entity description data in the knowledge base with the metadata to obtain a comparison result; and The entity name associated with the candidate word in the knowledge base is set as an output classification of the candidate word in the target text according to the comparison result. The knowledge entity identification method as described in Claim 1, wherein the metadata includes multiple categories, and each category includes multiple keywords, wherein the knowledge entity identification method includes: Comparing the key words of each category of the metadata with the words in the entity description data corresponding to the entity name, so as to obtain the comparison result. The knowledge entity identification method as described in claim 2 further includes: performing a comparison in the knowledge base using the candidate word of the target text, and obtaining the sorted entity name based on the similarity of the comparison; Compare the keywords of each category in the metadata with the words in the entity description data corresponding to the sorted entity name, so as to calculate the same or similar words in the entity description data for each category such that each category has a corresponding number of word matches; and The category with the largest number of matches for the word is set as the output category for the candidate word in the target text. The knowledge entity identification method described in claim 3 further includes: The output classification is compared with an entity category corresponding to the entity name in the knowledge base to verify whether the output classification of the candidate word in the target text is correct. The knowledge entity identification method as described in claim 2 further includes: comparing the candidate words of the target text in the knowledge base, and obtaining the sorted entity names based on the similarity; According to each keyword in these categories, compare and sort the words in the entity description data of the entity name to obtain a matching number respectively, wherein the sum of the matching numbers of each category is the number of matches for that term for the corresponding category; and The category corresponding to the word with the largest number of matches is taken as the output category. A knowledge entity recognition device, comprising: A knowledge entity candidate generation module configured to receive a target text to be parsed and a metadata, and use the candidate word of the target text to compare in a knowledge base to obtain from the knowledge base Multiple entity names of candidate words, each of which has a corresponding entity description; A knowledge entity verification and enhancement module coupled to the knowledge entity candidate generation module, wherein the knowledge entity verification and enhancement module is configured to compare the entity description data in the knowledge base with the metadata to obtain a comparison to the results; and A knowledge entity classification module, coupled to the knowledge entity verification and enhancement module, wherein the knowledge entity classification module is configured to set the entity name associated with the candidate word in the knowledge base according to the comparison result as An output classification of the candidate word in the target text. The knowledge entity recognition device as described in claim 6, wherein the metadata includes a plurality of categories, each of which includes a plurality of keywords, wherein the knowledge entity candidate generation module is further configured to use the candidate of the target text Perform comparison of words in the knowledge base to obtain the entity name, and compare the keywords in each category of the metadata with the words in the entity description data corresponding to the entity name, to obtain the comparison result. The knowledge entity recognition device as described in claim 7, wherein the knowledge entity verification and enhancement module uses the candidate words of the target text to perform comparison in the knowledge base, and obtains the sorted results based on the similarity of the comparison The name of the entity, and compare the key words of each category of the metadata with the words in the entity description data corresponding to the sorted entity name, so as to calculate the same or a number of word matches similar to the keyword words of each category, so that each category has a corresponding number of word matches, and the category with the largest number of word matches is set as the target text The output classification for the candidate word. The knowledge entity recognition device as described in claim 8, wherein the knowledge entity classification module is further configured to compare the output classification with an entity type corresponding to the entity name in the knowledge base to verify the target Whether the output classification of the candidate word in the text is correct. The knowledge entity identification device as described in Claim 7, wherein the knowledge entity verification and enhancement module is further configured to: comparing the candidate words of the target text in the knowledge base, and obtaining the sorted entity names based on the similarity; According to each keyword in these categories, compare and sort the words in the entity description data of the entity name to obtain a matching number respectively, wherein the sum of the matching numbers of each category is the number of matches for that term for the corresponding category; and The category corresponding to the word with the largest number of matches is taken as the output category.

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