CN110781685A - Method for automatically marking correctness of semantic analysis result based on user feedback - Google Patents
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Abstract
The invention discloses a method for automatically marking the correctness of semantic analysis results based on user feedback, which effectively utilizes mass data generated by semantic analysis, automatically marks the correctness of the semantic analysis results based on different feedback behaviors made by a user in response to a system, saves a large amount of manual marking cost and improves the marking efficiency through big data analysis and weight processing.
Description
Technical Field
The invention relates to the technical field of semantic annotation, in particular to a method for automatically annotating the correctness of a semantic analysis result based on user feedback.
Background
Semantic analysis is an important direction in the field of computer natural language processing, aiming at mapping natural language used by human beings into a complete, computer-executable formal meaning representation. The method is a core research field of natural language processing, and is also a key technology for realizing various intelligent systems, such as a human-computer interaction system, voice control of intelligent equipment, robot action control and the like. The main flow of semantic analysis work is to classify the text data into domains, then to adopt the corresponding models of each domain to identify intentions, identify entities, analyze syntax, disambiguate semantics and so on according to the domain classification result, and finally to synthesize the analysis result to form the semantic analysis result.
Most of the semantic analysis systems actually falling to the ground are continuous iterative items, and need to be continuously optimized and adjusted. The optimization work mainly aims at analyzing data generated by a user in the using process, and the analysis efficiency can be improved by carrying out model or process optimization on the analyzed correct data. And analyzing the data with the analysis error can find the new requirements of the system BUG and the user in time, thereby improving the accuracy and the coverage of the system analysis. The judgment of the correctness of the analysis data comes from manual labeling, and the method consumes a large amount of manpower and is not beneficial to development and iteration. At present, the research of the industry on semantic automatic labeling focuses on the level of part of speech labeling and syntax labeling, and an efficient and accurate method for carrying out correct and wrong labeling on analyzed data is lacked.
Disclosure of Invention
Most of the current floor-type semantic analysis systems are terminal-oriented, and after a user initiates a semantic analysis request once, a semantic analysis result is finally fed back to the user through the terminal. The correct response, the incorrect response, the delayed response or the non-response generated by the system can lead the user to have continuous semantic request behavior to feed back to the system through visual perception, and the correctness of each semantic analysis result of each user can be marked by analyzing the semantic analysis result difference, the time difference, the field difference and the emotion difference among the feedback of the users in the same round of conversation (the same round of conversation in the patent refers to the conversation with the same field in a certain time). Because each user has different feedback habits, the correctness of the semantic analysis result can be finally marked by combining the mass data analysis and the weight calculation. The method and the device utilize data generated in the actual using process of the user, and realize automatic correct and wrong labeling of semantic analysis results according to feedback of the user to different responses, save a large amount of manual labeling cost, and improve the labeling efficiency. And simultaneously, a large amount of marked data can be generated for scientific research and production.
The invention realizes the purpose through the following technical scheme:
the method for automatically marking the correctness of the semantic analysis result based on the user feedback comprises the following steps:
step 1, determining the tolerable error response times of a user in the same round of conversation;
step 2, collecting data generated by a semantic analysis system, summarizing all field intentions related to the data, and determining that two adjacent semantic requests are the longest time interval of the same round of conversation and field groups which can be regarded as the same round of conversation according to different field intentions;
step 3, aggregating the data according to different users, and arranging the data of the same user in an ascending order according to the request time;
step 4, traversing the data which are sequenced by each user, sequentially taking out each piece of data, and comparing and analyzing the data with the latest piece of data in the cache region;
(1) judging whether the data in the current cache area is empty, if so, caching the current data and acquiring the next piece of data;
(2) taking out the data closest to the current data in the cache region, judging the field difference and the time difference of the two data, determining whether the two data are in the same round of conversation, if so, jumping to (3), otherwise, jumping to (4);
(3) whether the current request is clear evaluation on the semantic analysis result is analyzed, if the current request is positive evaluation, skipping (5) is carried out, if the current request is negative evaluation, skipping (6) is carried out, and if the current request is not clear evaluation, the current data is cached;
(4) and at the moment, the current data and the data in the cache are dialogues in different turns, firstly, whether the semantic analysis results of the data in the cache are consistent or not is judged, and if so, skipping (6). Otherwise, judging the quantity of the data in the cache, if the quantity is larger than the tolerable error response times of the user in the same conversation, marking all the data in the cache as errors, emptying the cache, then putting the current data in the cache, and otherwise, skipping (5);
(5) at this moment, the last piece of data is correct, namely one piece of data adjacent to the current data in the cache is marked as correct, and the rest pieces of data are marked as errors, emptying the cache and storing the current data;
(6) and at this moment, all the previous data are wrong, namely all the data in the cache are marked as wrong, and the cache is emptied and the current data is stored.
Step 5, defining a mark identified as the same semantic analysis data, summarizing all marked single data according to the mark, and counting the marked correct frequency t and the error frequency f of the same semantic analysis data;
step 6, if the accuracy of the semantic analysis system exists, the semantic analysis data can be finally labeled by adopting a strategy;
if not, the correct and wrong frequencies can be directly compared, and the larger frequency is selected as the label.
Further, in the step 6, the strategy adopted for finally labeling the semantic analysis data is as follows:
tp is more than or equal to f (1-p) is marked as correct;
tp < f (1-p) is marked as error;
wherein p is the accuracy of the semantic analysis system.
The invention has the beneficial effects that:
the method for automatically marking the correctness of the semantic analysis result based on the user feedback effectively utilizes the mass data generated by the semantic analysis, automatically marks the correctness of the semantic analysis result based on different feedback behaviors made by the user in response to the system, and then saves a large amount of manual marking cost and improves the marking efficiency through the big data analysis and the weight processing.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the embodiments or the drawings needed to be practical in the prior art description, and obviously, the drawings in the following description are only some embodiments of the embodiments, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
In any embodiment, as shown in fig. 1, the common smart TV is used as a terminal to explain the present embodiment, fifty fields and one hundred intentions are involved in the actual use process of the floor semantic analysis system, and the present solution only selects some common fields, VIDEO, MUSIC, CONTROL, TV, CHAT, and intent QUERY, pay, record, email, SET, CHAT. When the data can not be classified into video, music and television fields, the data is uniformly divided into chatting.
Log data for a day is intercepted as follows, with some fields omitted or simplified:
{ id: "A", mac: 18-99-f5-f4-ba-33 ", time: "2019-09-0820: 40: 33 ", text: "special soldier is frontal," domain: "CHAT", intent: "CHAT", semantic: {}}
{ id: "B", mac: 18-99-f5-f4-ba-33 ", time: "2019-09-0820: 40: 40 ", text: "want to see that i is a special soldier", domain: "VIDEO", intent: "QUERY", semantic: { name: "I am a special soldier" }
{ id: "C", mac: 18-99-f5-f4-ba-33 ", time: "2019-09-0820: 40: 48 ", text: "first collection troublesome to play", domain: "CONTROL", intent: "EPISODE", semantic: { action: "index", index: "1"}}
{ id: "D", mac: 18-99-f5-f4-ba-33 ", time: "2019-09-0820: 40: 54 ", text: "smart o", domain: "CHAT", intent: "CHAT", semantic: {}}
{ id: "E", mac: 18-99-f5-f4-ba-33 ", time: "2019-09-0823: 30: 12 ", text: "turn off tv", domain: "TV", intent: "SET", semantic: { operatands: "OBJ _ SETTING _ CLOSE" }
{ id: "F", mac: "00-6 c-ff-ee-75-ad", time: "2019-09-0820: 50: 05 ", text: "you are my answers," domain: "MUSCI", intent: "PLAY", semantic: { song: "you are my peace" }
{ id: "G", mac: "00-6 c-ff-ee-75-ad", time: "2019-09-0820: 50: 25 ", text: "i want to see you are my answers," domain: "VIDEO", intent: "QUERY", semantic: { name: "you are my eyes" }
{ id: "H", mac: "00-6 c-ff-ee-75-ad", time: "2019-09-0820: 50: 47 ", text: "Play your answer TV show", domain: "VIDEO", intent: "QUERY", semantic: { name: "you are My eyes", category: "TV play" }
{ id: "I", mac: "00-6 c-ff-ee-75-ad", time: "2019-09-0820: 50: 57 ", text: "you are my answer tv drama", domain: "VIDEO", intent: "QUERY", semantic: { name: "you are My eyes", category: "TV play" }
{ id: "J", mac: "00-6 c-ff-ee-75-ad", time: "2019-09-0820: 51: 11 ", text: "turn off tv", domain: "TV", intent: "SET", semantic: { operatands: "OBJ _ SETTING _ CLOSE" }
1. Determining the tolerance times of the user to the error response according to the terminal:
here defined as 3.
2. The maximum time interval for each domain intent to be determined for the same round of conversation is as follows (unit: seconds):
VIDEO and MUSIC relate to the field that network access is required to request resources, and users also need to spend time on analyzing and feeding back the resources, so the time setting is longer than CONTROL, CHAT, TV and the like for timely response. Wherein PLAY is also intended to require buffering of data PLAY, longer time should be set.
Both the VIDEO and MUSIC fields are related to entity data, so allowing both to occur in the same wheel-to-wheel conversation. The CHAT domain data is data that is not recognized by the domain classification and may therefore occur in any one round of the session. Based on the above analysis, the domains identifiable as the same round of conversation are grouped as [ VIDEO, MUSIC, CHAT ], [ CONTROL, CHAT ], [ TV, CHAT ], [ CHAT ].
3. The map-reduce is used for converging data according to the mac fields (determined as uniform users according to the mac), and the data are arranged in an ascending order according to the time fields, so that the following results are obtained:
{mac：”18-99-f5-f4-ba-33”，logsId：[“A”，”B”，”C”，”D”]}
{mac：”00-6c-ff-ee-75-ad”，logsId：[“E”，”F”，”G”，”H”，”I”]}
4. analyzing all converged ordered data:
the data for mac addresses 18-99-f5-f4-ba-33 were first analyzed:
and (4) acquiring a first piece of data A, and directly storing the first piece of data A into a cache according to the step (1).
And (3) acquiring the next piece of data B, classifying the current data field as VIDEO according to the step 4 (2), classifying the latest one in the cache as CHAT, wherein the VIDEO and the CHAT are in the field group of the same round of conversation, and the time interval of the two pieces of data is 7 seconds and less than 10 seconds, so that the two pieces of data belong to the same round of conversation, and entering the step 4 (3), wherein the current data is not evaluated and the current data is cached.
Acquiring the next piece of data C, classifying the current data field as CONTROL according to the step 4, (2), and classifying the current data field as VIDEO in the cache, so that the current data field does not belong to the same round of conversation, wherein A, B pieces of data exist in the cache region, and according to the step 4, (4), the semantic analysis result of A is sematic: { }, the semantic analysis result of B is semantic: { name: and (4) performing step (5) when the two are inconsistent, marking A as an error and B as a correct, clearing the buffer area and caching the data C.
And acquiring the next piece of data D, wherein the data D and the data in the cache belong to the same round of conversation according to the step 4, (2), the data D is positively evaluated according to the step 4, (3), the step 4), (5) is carried out, and the data D is marked as correct, and the data D is buffered in a buffer area.
And (3) acquiring the next piece of data E, wherein according to the step 4, (2), if the E and the data in the cache do not belong to the same round of conversation, according to the step 4, (4), marking D as correct, clearing the cache region and caching the data E.
Finally, the time interval between data E and the next day zero also exceeds the time of the same session, so according to step 4 (4), E is marked as correct.
Continue to analyze the data with mac address 00-6 c-ff-ee-75-ad:
and (4) acquiring a first piece of data F, and directly storing the first piece of data F into a cache according to the step (4).
And (3) acquiring the next piece of data G, wherein the current data and the cache data belong to the same round of conversation according to the step 4 (2), and entering the step 4 (3), wherein the current data is not evaluated, and the data G is cached.
And acquiring the next piece of data H, and performing the same analysis process as the data G.
And acquiring the next piece of data I, and performing the same analysis process as the data G.
And (3) acquiring the next piece of data J, wherein the current data and the cache data do not belong to the same round of conversation according to the step 4.(2), and entering the step 4.(4), wherein E, F, H, G are marked as errors because the number of the data in the cache is 4 and is greater than the tolerance 3.
Finally, the interval between data I and the next day zero exceeds the time of the same session, so I is labeled as correct according to step 4 (4).
5. Converging the marked data according to the contents of text, domain, intent and semantic fields, summarizing the marked data into correct and wrong frequencies, and analyzing the data as follows (true: correct frequency, false: wrong frequency):
{ id: "a", text: "special soldier is frontal," domain: "CHAT", intent: "CHAT", semantic: { }, true: 0, false: 1}
{ id: "b", text: "want to see that i is a special soldier", domain: "VIDEO", intent: "QUERY", semantic: { name: "I am a special soldier" }, true: 1, false: 0}
{ id: "c", text: "first collection troublesome to play", domain: "CONTROL", intent: "EPISODE", semantic: { action: "index", index: "1" }, true: 1, false: 0}
{ id: "d", text: "smart o", domain: "CHAT", intent: "CHAT", semantic: { }, true: 1, false: 0}
{ id: "e", text: "turn off tv", domain: "TV", intent: "SET", semantic: { operatands: "OBJ _ SETTING _ CLOSE" }, true: 2, false: 0}
{ id: "f', text: "you are my answers," domain: "MUSCI", intent: "PLAY", semantic: { song: "you are my peace" }, true: 0, false: 1}
{ id: "g", text: "i want to see you are my answers," domain: "VIDEO", intent: "QUERY", semantic: { name: "you are my eyes" }, true: 0, false: 1}
{ id: "h", text: "Play your answer TV show", domain: "VIDEO", intent: "QUERY", semantic: { name: "you are My eyes", category: "tv drama" }, true: 0, false: 1}
{ id: "i", text: "you are my answer tv drama", domain: "VIDEO", intent: "QUERY", semantic: { name: "you are My eyes", category: "tv drama" }, true: 0, false: 1}
6. The experience accuracy of the semantic analysis system carried by the smart television is about 0.65, and the final label of each piece of analytic data can be calculated by substituting a formula as follows:
data a, with 0 x 0.7 < 1 x (1-0.7), marked as error;
data b, c, d, with 1 x 0.7 > 0 x (1-0.7), are labeled as correct;
data e, with 2 x 0.7 > 0 x (1-0.7), labeled correct;
data f, g, h, i, with 0 x 0.7 < 1 x (1-0.7), marked as error;
the above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims. It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition. In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (3)
1. The method for automatically marking the correctness of the semantic analysis result based on the user feedback is characterized by comprising the following steps of:
step 1, determining the tolerable error response times of a user in the same round of conversation;
step 2, collecting data generated by a semantic analysis system, summarizing all field intentions related to the data, and determining that two adjacent semantic requests are the longest time interval of the same round of conversation and field groups which can be regarded as the same round of conversation according to different field intentions;
step 3, aggregating the data according to different users, and arranging the data of the same user in an ascending order according to the request time;
step 4, traversing the data which are sequenced by each user, sequentially taking out each piece of data, and comparing and analyzing the data with the latest piece of data in the cache region;
step 5, defining a mark identified as the same semantic analysis data, summarizing all marked single data according to the mark, and counting the marked correct frequency t and the error frequency f of the same semantic analysis data;
step 6, if the accuracy of the semantic analysis system exists, the semantic analysis data can be finally labeled by adopting a strategy;
if not, the correct and wrong frequencies can be directly compared, and the larger frequency is selected as the label.
2. The method for automatically labeling correctness of semantic analysis results based on user feedback according to claim 1, wherein in the step 4, the step of comparing and analyzing the data to be fetched and the latest data in the cache area comprises:
(1) judging whether the data in the current cache area is empty, if so, caching the current data and acquiring the next piece of data;
(2) taking out the data closest to the current data in the cache region, judging the field difference and the time difference of the two data, determining whether the two data are in the same round of conversation, if so, jumping to (3), otherwise, jumping to (4);
(3) whether the current request is clear evaluation on the semantic analysis result is analyzed, if the current request is positive evaluation, skipping (5) is carried out, if the current request is negative evaluation, skipping (6) is carried out, and if the current request is not clear evaluation, the current data is cached;
(4) and at the moment, the current data and the data in the cache are dialogues in different turns, firstly, whether the semantic analysis results of the data in the cache are consistent or not is judged, and if so, skipping (6). Otherwise, judging the quantity of the data in the cache, if the quantity is larger than the tolerable error response times of the user in the same conversation, marking all the data in the cache as errors, emptying the cache, then putting the current data, and otherwise, skipping (5);
(5) at this moment, the last piece of data is correct, namely one piece of data adjacent to the current data in the cache is marked as correct, and the rest pieces of data are marked as errors, emptying the cache and storing the current data;
(6) and at this moment, all the previous data are wrong, namely all the data in the cache are marked as wrong, and the cache is emptied and the current data is stored.
3. The method for automatically labeling the correctness of the semantic analysis result based on the user feedback as claimed in claim 1, wherein in the step 6, the strategy for performing the final labeling on the semantic analysis data is as follows:
tp is more than or equal to f (1-p) is marked as correct;
tp < f (1-p) is marked as error;
wherein p is the accuracy of the semantic analysis system.
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