CN105183712A - Method and apparatus for scoring English composition - Google Patents
Method and apparatus for scoring English composition Download PDFInfo
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- CN105183712A CN105183712A CN201510536368.4A CN201510536368A CN105183712A CN 105183712 A CN105183712 A CN 105183712A CN 201510536368 A CN201510536368 A CN 201510536368A CN 105183712 A CN105183712 A CN 105183712A
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- Prior art keywords
- sentence
- mark
- language model
- english composition
- giving
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Abstract
The present invention relates to the field of auto-scoring, and discloses a method and an apparatus for scoring an English composition. The method comprises: acquiring a to-be-scored English composition and separating sentences in the to-be-scored English composition; matching each sentence in the to-be-scored English composition with a preset language model, and calculating a matching degree between the each sentence and the language model; and according to the matching degree between the each sentence and the language model, scoring the to-be-scored English composition. According to the present invention, by introducing the language model, the problem of scoring the composition without a topic or with a self-prepared topic in composition corpora is solved.
Description
Technical field
The present invention relates to auto-scoring field, particularly, relating to a kind of method for giving a mark to english composition and device.
Background technology
In english composition auto-scoring, prior art is substantially all be aware of compostion topic in advance, carries out auto-scoring to the composition defining compostion topic.Carry out auto-scoring to the composition defining compostion topic mainly to compare by the template of same title in the composition of this restriction compostion topic and the language material of collection is write a composition, consider from the word of local and layout two aspect of the overall situation.Such method, under the prerequisite limiting compostion topic, can reach and basically identical result of manually giving a mark, such as, correct net (www.pigai.com).
But, that art methods needs to gather a large amount of given exercise question and be in the composition language material of different levels, consume a large amount of manpowers and time.In addition, art methods can not process the auto-scoring problem of the english composition of drafting a topic by oneself.
Summary of the invention
The object of this invention is to provide a kind of method for giving a mark to english composition and device.Wherein, described method, by introducing language model, solves the marking problem of composition not occurring exercise question or draft a topic by oneself in composition language material.
To achieve these goals, the invention provides a kind of method for giving a mark to english composition.Described method comprises: obtain and wait english composition of giving a mark, and treats the sentence in marking english composition described in isolating; The described each sentence waiting to give a mark in english composition is mated with the language model preset, and calculates the matching degree of described each sentence and described language model; And according to the matching degree of described each sentence and described language model, described english composition of waiting to give a mark is given a mark.
Preferably, wait english composition of giving a mark obtaining, and before waiting the sentence of giving a mark in english composition described in isolating, described method also comprises: gather article language material; And according to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.
Preferably, the training patterns of machine learning is used to be trained Hidden Markov Model (HMM) by the training framework of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.
Preferably, the probability that described matching degree is occurred in described language model by sentence characterizes, the probability according to following formulae discovery sentence occurs in described language model:
P(w
1w
2w
3…w
n)＝P(w
1)P(w
1|w
2)P(w
2|w
3)…P(w
n-1|w
n)
Wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, w
nfor the n-th word of sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.
Preferably, determine the mark of described sentence according to the matching degree of described sentence and described language model, and calculate average mark according to the mark of described sentence, thus the mark of english composition of waiting described in obtaining to give a mark.
Correspondingly, the present invention also provides a kind of device for giving a mark to english composition.Described device comprises: obtain separative element, waits for obtaining english composition of giving a mark, and treats the sentence in marking english composition described in isolating; Model Matching unit, for being mated with the language model preset by the described each sentence waiting to give a mark in english composition, and calculates the matching degree of described each sentence and described language model; And composition marking unit, for giving a mark to described english composition of waiting to give a mark according to the matching degree of described each sentence and described language model.
Preferably, described device also comprises: model construction unit, english composition of giving a mark is waited for obtaining at described acquisition separative element, and before waiting the sentence of giving a mark in english composition described in isolating, gather article language material, and according to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.
Preferably, described model construction unit uses the training patterns of machine learning to be trained Hidden Markov Model (HMM) by the training framework of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.
Preferably, the probability that described matching degree is occurred in described language model by sentence characterizes, the probability that described Model Matching unit occurs in described language model according to following formulae discovery sentence:
P(w
1w
2w
3…w
n)＝P(w
1)P(w
1|w
2)P(w
2|w
3)…P(w
n-1|w
n)
Wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, w
nfor the n-th word of sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.
Preferably, described composition marking unit determines the mark of described sentence according to the matching degree of described sentence and described language model, and calculates average mark according to the mark of described sentence, thus treats the mark of marking english composition described in obtaining.
Pass through technique scheme, the training patterns of machine learning is used to train to obtain language model to gathered article language material, and carry out mating and giving a mark to english composition according to the matching degree of each sentence and language model with language model by from the sentence be separated in english composition of waiting to give a mark, solve in language material of writing a composition the marking problem of composition not occurring exercise question or draft a topic by oneself.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the method for giving a mark to english composition provided by the invention; And
Fig. 2 is the structural representation of the device for giving a mark to english composition provided by the invention.
Description of reference numerals
10 obtain separative element 20 Model Matching unit
30 composition marking unit 40 model construction unit
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described in detail.Should be understood that, embodiment described herein, only for instruction and explanation of the present invention, is not limited to the present invention.
In english composition auto-scoring, prior art is substantially all be aware of compostion topic in advance, carries out auto-scoring to the composition defining compostion topic.Carry out auto-scoring to the composition defining compostion topic mainly to compare by the template of same title in the composition of this restriction compostion topic and the language material of collection is write a composition, consider from the word of local and layout two aspect of the overall situation.But art methods can not process the auto-scoring problem of the english composition of drafting a topic by oneself.Therefore, the present invention spy provides a kind of method for giving a mark to english composition.
In order to solve problem existing in prior art, invention introduces the concept and methodology of language model.Below language model is introduced in detail:
English composition is by sentence is molecular one by one, and sentence is made up of word one by one.The array mode of word can affect the fluency of sentence and graceful degree at (comprising order and collocation).With symbol and equation expression as follows, use w
1w
2w
3w
nrepresent a sentence be made up of n word, this n word is w respectively
1, w
2, w
3..., w
n.Probability P (the w that this sentence occurs
1w
2w
3w
n) represent, we suppose that the appearance relation between word has Hidden Markov Model (HMM) (HiddenMarkovModel) characteristic, are also a word w in sentence
iappearance only and its previous word w of being close to
i-1have relation, and and word (w in the past before it
i-2w
i-3) appearance have nothing to do.So probability P (w of this sentence appearance
1w
2w
3w
n)=P (w
1) P (w
1| w
2) P (w
2| w
3) ... P (w
n-1| w
n), describe probability that sentence occurs relevant with the probability that wherein " word to " occurs.
For there is not compostion topic or from intending the english composition of compostion topic, there is no model essay in advance before this, therefore, unworkable at this by the marking mode compared with model essay, and want directly give a mark to english composition itself.Fig. 1 is the process flow diagram of the method for giving a mark to english composition provided by the invention.As shown in Figure 1, the method for giving a mark to english composition provided by the invention comprises: in step S101, gathers article language material.Particularly, outstanding article language material is gathered.Described outstanding article language material comes from China Daily and Wall Street Journal.In general, carry out training by outstanding article language material and obtain language model, the size of required language material text is at more than 2.5G.
In step s 102, according to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.In general, the training patterns of machine learning is used to be trained Hidden Markov Model (HMM) by the training framework (http://rnnlm.org/) of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.Train the form that the language model obtained adopts word right.Utilize symbol and equation expression, training can obtain all words to the probability P (w occurred in described language model
i| w
j).Wherein, w
iand w
jfor traveling through the English word that whole language material text comprises, the word do not occurred in language material is to employing smoothing processing.
In step s 103, obtain and wait english composition of giving a mark, and described in isolating, treat the sentence in marking english composition.
In step S104, the described each sentence waiting to give a mark in english composition is mated with the language model preset, and calculates the matching degree of described each sentence and described language model.The probability that described matching degree is occurred in described language model by sentence characterizes.Probability according to following formulae discovery sentence occurs in described language model: P (w
1w
2w
3w
n)=P (w
1) P (w
1| w
2) P (w
2| w
3) ... P (w
n-1| w
n), wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.The probability that described sentence occurs in described language model is larger, then the matching degree of described sentence and described language model is larger.On the contrary, the probability that described sentence occurs in described language model is less, then the matching degree of described sentence and described language model is less.
In step S105, according to the matching degree of described each sentence and described language model, described english composition of waiting to give a mark is given a mark.The matching degree of described sentence and described language model is larger, that is to say that the possibility that described sentence occurs in corpus is larger, namely the expression way of sentence is more as the sentence in corpus, and corpus is all the outstanding article gathered, therefore, larger with language model matching degree sentence fluency and graceful degree better.On the contrary, less with language model matching degree sentence fluency and graceful degree poorer.Determine the mark of described sentence according to the fluency of described sentence and graceful degree, and calculate average mark according to the mark of described sentence.The average mark obtained just can reflect the integral level of english composition, this completes the auto-scoring of composition not occurring exercise question or draft a topic by oneself.
Correspondingly, the present invention also provides a kind of device for giving a mark to english composition.Fig. 2 is the structural representation of the device for giving a mark to english composition provided by the invention.As shown in Figure 2, the device for giving a mark to english composition provided by the invention comprises: obtain separative element 10, waits for obtaining english composition of giving a mark, and treats the sentence in marking english composition described in isolating; Model Matching unit 20, for being mated with the language model preset by the described each sentence waiting to give a mark in english composition, and calculates the matching degree of described each sentence and described language model; And composition marking unit 30, for giving a mark to described english composition of waiting to give a mark according to the matching degree of described each sentence and described language model.
In a particular embodiment, described device also comprises: model construction unit 40, english composition of giving a mark is waited for obtaining at described acquisition separative element, and before waiting the sentence of giving a mark in english composition described in isolating, gather article language material, and according to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.Particularly, described model construction unit 40 uses the training patterns of machine learning to be trained Hidden Markov Model (HMM) by the training framework of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.
Selectively, the probability that described matching degree is occurred in described language model by sentence characterizes, the probability that described Model Matching unit 20 occurs in described language model according to following formulae discovery sentence:
P(w
1w
2w
3…w
n)＝P(w
1)P(w
1|w
2)P(w
2|w
3)…P(w
n-1|w
n)
Wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, w
nfor the n-th word of sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.
In concrete application, described composition marking unit 30 determines the mark of described sentence according to the matching degree of described sentence and described language model, and calculates average mark according to the mark of described sentence, thus treats the mark of marking english composition described in obtaining.
It should be noted that, for the details that the device for giving a mark to english composition provided by the invention also may relate to, in the method for giving a mark to english composition provided by the invention, having done detailed description, having repeated no more herein.
The present invention uses the training patterns of machine learning to train to obtain language model to gathered article language material, and carry out mating and giving a mark to english composition according to the matching degree of each sentence and language model with language model by from the sentence be separated in english composition of waiting to give a mark, not only directly to not occurring that exercise question or the composition of drafting a topic by oneself are given a mark, but also can give a mark to the fluency of the sentence expression in english composition and graceful degree.
Below the preferred embodiment of the present invention is described in detail by reference to the accompanying drawings; but; the present invention is not limited to the detail in above-mentioned embodiment; within the scope of technical conceive of the present invention; can carry out multiple simple variant to technical scheme of the present invention, these simple variant all belong to protection scope of the present invention.
It should be noted that in addition, each concrete technical characteristic described in above-mentioned embodiment, in reconcilable situation, can be combined by any suitable mode, in order to avoid unnecessary repetition, the present invention illustrates no longer separately to various possible array mode.
In addition, also can carry out combination in any between various different embodiment of the present invention, as long as it is without prejudice to thought of the present invention, it should be considered as content disclosed in this invention equally.
Claims (10)
1. the method for giving a mark to english composition, is characterized in that, described method comprises:
Obtain and wait english composition of giving a mark, and described in isolating, treat the sentence in marking english composition;
The described each sentence waiting to give a mark in english composition is mated with the language model preset, and calculates the matching degree of described each sentence and described language model; And
According to the matching degree of described each sentence and described language model, described english composition of waiting to give a mark is given a mark.
2. the method for giving a mark to english composition according to claim 1, is characterized in that, wait english composition of giving a mark obtaining, and before waiting the sentence of giving a mark in english composition described in isolating, described method also comprises:
Gather article language material; And
According to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.
3. the method for giving a mark to english composition according to claim 2, it is characterized in that, the training patterns of machine learning is used to be trained Hidden Markov Model (HMM) by the training framework of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.
4. the method for giving a mark to english composition according to claim 3, it is characterized in that, the probability that described matching degree is occurred in described language model by sentence characterizes, the probability according to following formulae discovery sentence occurs in described language model:
P(w
1w
2w
3…w
n)＝P(w
1)P(w
1|w
2)P(w
2|w
3)…P(w
n-1|w
n)
Wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, w
nfor the n-th word of sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.
5. the method for giving a mark to english composition according to claim 4, it is characterized in that, the mark of described sentence is determined according to the matching degree of described sentence and described language model, and calculate average mark according to the mark of described sentence, thus described in obtaining, treat the mark of marking english composition.
6. the device for giving a mark to english composition, is characterized in that, described device comprises:
Obtaining separative element, waiting for obtaining english composition of giving a mark, and described in isolating, treat the sentence in marking english composition;
Model Matching unit, for being mated with the language model preset by the described each sentence waiting to give a mark in english composition, and calculates the matching degree of described each sentence and described language model; And
Composition marking unit, for giving a mark to described english composition of waiting to give a mark according to the matching degree of described each sentence and described language model.
7. the device for giving a mark to english composition according to claim 6, is characterized in that, described device also comprises:
Model construction unit, wait for obtaining at described acquisition separative element english composition of giving a mark, and before waiting the sentence of giving a mark in english composition described in isolating, gather article language material, and according to gathered article language material, Hidden Markov Model (HMM) is trained, to obtain default language model.
8. the device for giving a mark to english composition according to claim 7, it is characterized in that, described model construction unit uses the training patterns of machine learning to be trained Hidden Markov Model (HMM) by the training framework of Recognition with Recurrent Neural Network, to obtain default language model according to gathered article language material.
9. the device for giving a mark to english composition according to claim 8, it is characterized in that, the probability that described matching degree is occurred in described language model by sentence characterizes, the probability that described Model Matching unit occurs in described language model according to following formulae discovery sentence:
P(w
1w
2w
3…w
n)＝P(w
1)P(w
1|w
2)P(w
2|w
3)…P(w
n-1|w
n)
Wherein, P (w
1w
2w
3w
n) probability that occurs in described language model for sentence, w
nfor the n-th word of sentence, P (w
1) probability that occurs in described language model for first word of sentence, P (w
n-1| w
n) word that forms for (n-1)th word and n-th word of sentence is to the probability occurred in described language model.
10. the device for giving a mark to english composition according to claim 9, it is characterized in that, described composition marking unit determines the mark of described sentence according to the matching degree of described sentence and described language model, and calculate average mark according to the mark of described sentence, thus described in obtaining, treat the mark of marking english composition.
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|---|---|---|---|
| CN201510536368.4A CN105183712A (en) | 2015-08-27 | 2015-08-27 | Method and apparatus for scoring English composition |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510536368.4A CN105183712A (en) | 2015-08-27 | 2015-08-27 | Method and apparatus for scoring English composition |
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| Publication Number | Publication Date |
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| CN105183712A true CN105183712A (en) | 2015-12-23 |
Family
ID=54905801
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| CN201510536368.4A Pending CN105183712A (en) | 2015-08-27 | 2015-08-27 | Method and apparatus for scoring English composition |
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| CN106294726A (en) * | 2016-08-09 | 2017-01-04 | 北京光年无限科技有限公司 | Based on the processing method and processing device that robot role is mutual |
| CN107291694A (en) * | 2017-06-27 | 2017-10-24 | 北京粉笔未来科技有限公司 | A kind of automatic method and apparatus, storage medium and terminal for reading and appraising composition |
| CN107729936A (en) * | 2017-10-12 | 2018-02-23 | 科大讯飞股份有限公司 | One kind corrects mistakes to inscribe reads and appraises method and system automatically |
| CN108319581A (en) * | 2017-01-17 | 2018-07-24 | 科大讯飞股份有限公司 | A kind of natural language sentence evaluation method and device |
| CN109285404A (en) * | 2018-10-25 | 2019-01-29 | 安徽创见未来教育科技有限公司 | A kind of English composition automatic scoring system |
| CN110147542A (en) * | 2019-05-23 | 2019-08-20 | 联想(北京)有限公司 | A kind of information processing method and electronic equipment |
| CN111797631A (en) * | 2019-04-04 | 2020-10-20 | 北京猎户星空科技有限公司 | Information processing method and device and electronic equipment |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106294726A (en) * | 2016-08-09 | 2017-01-04 | 北京光年无限科技有限公司 | Based on the processing method and processing device that robot role is mutual |
| CN108319581A (en) * | 2017-01-17 | 2018-07-24 | 科大讯飞股份有限公司 | A kind of natural language sentence evaluation method and device |
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| CN107291694A (en) * | 2017-06-27 | 2017-10-24 | 北京粉笔未来科技有限公司 | A kind of automatic method and apparatus, storage medium and terminal for reading and appraising composition |
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| CN107729936A (en) * | 2017-10-12 | 2018-02-23 | 科大讯飞股份有限公司 | One kind corrects mistakes to inscribe reads and appraises method and system automatically |
| CN107729936B (en) * | 2017-10-12 | 2020-12-08 | 科大讯飞股份有限公司 | Automatic error correction review method and system |
| CN109285404A (en) * | 2018-10-25 | 2019-01-29 | 安徽创见未来教育科技有限公司 | A kind of English composition automatic scoring system |
| CN111797631A (en) * | 2019-04-04 | 2020-10-20 | 北京猎户星空科技有限公司 | Information processing method and device and electronic equipment |
| CN110147542A (en) * | 2019-05-23 | 2019-08-20 | 联想(北京)有限公司 | A kind of information processing method and electronic equipment |
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