118 results about "Adnoun" patented technology

A method for automatically analyzing the text of a document to generate verification questions to be administered to a user as a quiz for the purpose of verifying whether the user has read the document. Syntactic analysis is applied to statements (e.g. sentences) in the text to automatically generate various types of verification questions, including fill-in-the-blank, true/false, and multiple-choice questions. Nouns and proper nouns in a statement may be used to generate fill-in-the-blank questions; numerical values may be used to generate fill-in-the-blank, true/false and multiple-choice questions; and verbs, adjectives and adverbs may be used to generate true/false questions. The questions may be generated dynamically for each user, or generated once, stored and used for multiple users.

The invention discloses a synonym expansion method and a synonym expansion device both used for text duplication detection, which include a text preprocessing unit used for deleting stop words in a suspected text and tagging the part-of-speech, wherein verbs, nouns, and adjectives are taken as the to-be-processed objects; through retrieving synonyms of single words, computing the Cartesian product and obtaining the initial expansion set of all word collocations in the suspected text; through comparing the initial expansion set and an actual corpus, filtering word collocations impossible in an actual language environment, simplifying the set, and obtaining the final expansion set; and during the duplication detection, according to different collocation results, giving the words different weights which are taken as the computation base for the duplication detection results. Through applying the method or the device disclosed by the embodiment of the invention, the problem of synonym replacement in text duplication can be efficiently overcome, the efficiency is higher, and the accuracy of the duplication detection is greatly improved.

The present invention discloses a density-based text clustering algorithm research method. The method comprises the following steps: using the ICTCLAS word segmentation system to carry out word segmentation on a text in a text set, and extracting corresponding keywords from the word segmentation according to the three parts of speech of the noun, the verb, and the adjective, and the word frequency; using an improved HowNet word similarity algorithm to calculate keyword similarity of the obtained keywords; according to the keyword similarity in the text, calculating text similarity; and according to the obtained text similarity, using the density-based clustering algorithm to carry out clustering on the text, so that the performance of the existing text-related information retrieval technology can be significantly improved.

A method of classifying and accessing writing examples for writing composition. A language domain is first selected and representatives texts from that domain are analyzed to build a classification system for the domain. The text is first analyzed to determine root nouns and root verbs. The texts are further analyzed to determine relationships between nouns and the root verbs used for each noun-to-noun relationship. At this point, writing examples are then extracted from the texts and stored in a database. These writing examples are then classified by the earlier defined noun-to-noun relationships and root verbs that go along with those noun-to-noun relationships. Access to the writing examples is accomplished via a three-level interface. The first level (noun interface) maps nouns and pre-determined relationships between those nouns. By selecting one of these relationships, a navigation link takes the user is a second level (verb interface) showing root verbs that relate to the particular noun-to-noun relationship selected. Here the user selects a particular root verb which causes a query of the writing examples database. The results of the query are sent to a third level interface (results interface) where the writing examples are displayed. The user may then select one or more writing examples to insert in a word processing program or document where the user may modify them for the writing job at hand.

The invention discloses a method of retrieving English text based on the matching degree. The method includes the following steps that firstly, in a server, retrieval information is stored in advance, each English document is related to a retrieval unit, wherein each retrieval unit includes ID, English document entry time and at least one search strip, each retrieval strip comprises at least one noun and notional verb in the abstract of the English document associated with the retrieval unit, and weights are preset for all the search strips; secondly, the retrieval of English is divided into noun and notional verb after inputting, the noun and the notional verb are expanded into retrieval statements; thirdly, retrieval the weight is obtained from the similarity evaluation of the retrieval statement, the retrieval weight is matched with the preset weight, results are sorted according to the matching degree and a list of retrieval results is obtained.

A phrase translation and language instruction system is provided comprising a plurality of printed sheets having disposed thereupon information relating to the translation of certain generic phrases. Additionally, there is provided therein a set of alternative verbs, nouns, adjectives and the like to enable the substitution of words relating to one's specific situation and the communication of such in a foreign language.

The invention relates to an associated word searching method and device based on semantics, and belongs to the technical field of computers. The semantic-based associated word searching method comprises the following steps: acquiring a text document from an internet database; identifying the text document by using a deep learning entity identification model to obtain an entity noun and an entity noun position; calculating a word vector of the entity noun according to the text of the sentence context of the entity noun determined by the entity noun position; performing word formation analysis on the entity nouns to determine entity types of the entity nouns; and according to the word vectors of the entity nouns and the entity types of the entity nouns, performing similar retrieval in a word vector library to search similar entity nouns. According to the semantic-based associated word searching method, the problem that one word is polysemy is solved, the method does not depend on an existing word bank, and unknown entity nouns can be processed.

The invention provides a text matching model training method and device, equipment and a storage medium, and the method comprises the steps: obtaining a plurality of first sample pairs, each first sample pair comprises two texts and a matching result of the two texts, carrying out the mask processing of entity nouns and/or verbs contained in the texts in each first sample pair, obtaining corresponding second sample pair, performing iteration pre-training on the initial text matching model based on the second sample pair to obtain a pre-trained text matching model, obtaining a plurality of third sample pairs corresponding to the preset business scene, wherein each third sample pair comprises two texts and a matching result of the two texts, based on the third sample pairs, performing iterative fine tuning training on the pre-trained text matching model to obtain a text matching model corresponding to the preset business scene. According to the invention, text matching can be carried out more accurately.

The invention relates to an artificial intelligence technology, and discloses a language model training method, which comprises the following steps of: respectively carrying out word-level mask, phrase-level mask, entity-level mask and part-of-speech-level mask processing on texts in a training data set to obtain a to-be-used pre-training data set; performing sentence vector representation processing on texts in the to-be-used pre-training data set to obtain a pre-training data set represented by sentence vectors; and inputting the pre-training data set represented by the sentence vector into the language model, carrying out model reasoning iteration training on the language model, and when a preset model training completion condition is satisfied, completing the training of the language model. The invention further relates to a block chain technology, and the training data set is stored in the block chain. The problems that in the prior art, a model obtained through an existing model training mode cannot learn information of a Chinese semantic level and information of a Chinese entity relation, and the sensitivity and accuracy of the model to nouns are low can be solved.

The invention belongs to the technical field of Internet. The invention relates to a method for discovering important noun labels through machine learning and context part-of-speech. The method comprises the following steps: S1, determined new words with certain data are listed through corpora, wherein the corpora need the most recent article, the articles of the last year are chosen as the corpus, the corpora are used for learning adjectives, connected words, verbs and the like in previous and later texts with the most possible nouns, and dozens of determined important nouns (such as Huawei,Xiaomi, ZTE, etc.) are sorted; and S2, through the operation of the previous step, a batch of collated sentence patterns such as 'XXX publishes... in the current year', 'no matter how XXX achieves...', 'for XXX' and the like can be obtained, and meanwhile, different probability situations can be obtained by calculating different sentence patterns. According to the method, the new words can be found through machine learning with extremely low manual intervention and arrangement cost.

The embodiment of the invention discloses a vulnerability information matching processing method and device. The method comprises the following steps: obtaining vulnerability related information in anetwork, carrying out the part-of-voice tagging and block extraction of vulnerability related information, and obtaining preprocessed vulnerability information; combining a plurality of blocks conforming to a preset grammatical structure in the preprocessed vulnerability information into a new noun block to obtain block vulnerability information; and matching verbs in the block vulnerability information according to preset sensitive verbs, and determining a target noun connected with the matched target verbs as vulnerability information. Part-of-voice tagging and block extraction are carried out on vulnerability related believes in a network; according to the vulnerability information matching method and device, the blocks conforming to the combined grammatical structure of adjectives andnouns are combined, so that each block can be quickly matched when vulnerability information matching is performed, the vulnerability information matching efficiency is greatly improved, the labor cost is reduced, Meanwhile, vulnerability processing in time and loss reduction are facilitated.

The invention discloses an English grammar training method and an English grammar training system. The English grammar training method comprises the following steps: acquiring text information and audio and video files corresponding to the text information from a server; identifying the part-of-voice of each word in the text information, the part-of-voice being a word of a noun and being marked asa first type of words; wherein the part-of-voice is a word in a word segmentation form of verbs and is marked as a second type of words; wherein the part-of-voice is a word of a verb, does not include an emotional verb and a second type of words, and is marked as a third type of words; displaying one or more statements, wherein the first type of words, the second type of words and the third typeof words are all hidden; playing an audio and video file corresponding to the statement; responding to external input of a user; judging whether the external input of the user is matched with the first type of words, the second type of words or the third type of words or not, if yes, displaying the words, and if not, waiting for the user to input again and judging again.

The invention relates to a key phrase generation method based on a training model. The method comprises the following steps: S1, obtaining to-be-processed text data; S2, performing word segmentation and part-of-speech tagging on the acquired text data; S3, establishing a disabled lexicon, and removing words existing in the disabled lexicon; filtering out words which are not verbs and nouns; S4, performing N-gram combination to obtain a candidate word combination; S5, performing text vector conversion on the text data and the candidate word combination based on a pre-training model of Bert; S6, performing cosine similarity calculation on the vector representation of the document level and the vector representation of the candidate word, and performing semantic similarity sorting; and S7, according to a set value, selecting the words or phrases with the semantic similarity ranks in the top in the step S6 to form keywords. According to the method, the open-source pre-training model Bert is used for carrying out text vectorization expression, information of the semantic level of the text is completely obtained, keyword extraction is facilitated, phrase-level keywords are obtained according to N-gram combination, and the meaning is more complete compared with single words.

The invention provides a barrage text clustering method based on feature extension and T-oBTM. The method comprises three steps of a network new word processing stage, a theme modeling stage and a text clustering stage. The invention provides an oBTM streaming short text clustering method (T-oBTM) for carrying out threshold constraint on word pairs according to bullet screen characteristics, the algorithm execution time is shortened, network new words are recognized and processed, the purpose of expanding text characteristics is achieved, and then the algorithm precision is improved. Accordingto the method, the network new words are recognized and processed, the word segmentation lexicon is enriched, and the word segmentation precision is improved; when the network new words are processed, the recognized entity nouns and sentiments, viewpoints and opinion words are processed differently, short text features are expanded, and clustering precision is improved.

A text topic mining method based on word semantic weight of Internet service comprises the following steps: 1, performing part-of-speech tagging on words in a Mashup service description document by using a natural language toolkit in Python; 2, counting word frequency information, and calculating TF-IDF information; 3, extracting Mashup service label information wherein the semantic weight of each word in the Mashup service description document is recalculated on the basis of the noun set Nset and the TF-IDF value; and 4, solving Mashup theme features through an NMF model. On the basis of TF-IDF, in combination with service label information and context word information, weights of words are recalculated, and weight values of key words are increased, so that Mashup service modeling and service document theme confirmation are effectively performed.

The invention discloses a hybrid information extraction method and system oriented to an open domain. According to the method, firstly, through context clause decomposition and NLP preprocessing, a composite sentence is simplified, and language attributes of the sentence are obtained; then identifying explicit phrases in the sentences, and identifying implicit phrases by using the two defined extension rules; and finally, based on the defined six language scene rules or the combination thereof, extracting the relationship between the identified entities, and generating a relationship triple. Aiming at the defects of non-atomic extraction, implicit phrases in a text are expanded by utilizing dependency analysis and heuristic rules, long noun phrases and relation phrases can be further decomposed into meaningful and more compact phrases, and the atomic requirement of information extraction can be better met; for cross-clause extraction, co-reference resolution and context clause decomposition are adopted for compositing sentences, the relation on the simplified clauses is extracted in combination with dependency analysis, and the extraction capacity on a composite sentence pattern is improved.

The invention provides an information extraction method and device, electronic equipment and a storage medium. The embodiment of the invention provides the information extraction method. The method comprises the steps of performing word segmentation processing on a to-be-processed text according to a preset word segmentation tool to obtain an action type word segmentation set and a noun type wordsegmentation set, then, determining a first probability value corresponding to point mutual information between a first action type segmented word in the action type word segmentation set and a firstnoun type segmented word in the noun type word segmentation set, and if the first probability value is greater than a preset probability threshold, generating first intention information according tothe first action type segmented word and the first noun type segmented word. According to the information extraction method provided by the embodiment of the invention, the probability value corresponding to the point mutual information between any combination of the action type segmented words and the noun type segmented words is calculated, and the combined words and sentences of the action typesegmented words and the noun type segmented words with higher correlation are selected as the intention information of the user, so that the new intention of the client is discovered.