3604 results about "Sentence" patented technology

A method, system and computer program product for generating a document representation are disclosed. The system includes a server and a client computer, and the method involves: receiving into memory a resource containing at least one sentence of text; producing a tree comprising tree elements indicating parts-of-speech and grammatical relations between the tree elements; producing semantic structures each having three tree elements to represent a simple clause (subject-predicate-object); and storing a semantic network of semantic structures and connections therebetween. The semantic network may be created from a user provided root concept. Output representations include concept maps, facts listings, text summaries, tag clouds, indices; and an annotated text. The system interactively modifies semantic networks in response to user feedback, and produces personal semantic networks and document use histories.

This disclosure describes a practical system / method for predicting spoken text (a spoken word or a spoken sentence / phrase) given that text's partial spelling (example, initial characters forming the spelling of a word / sentence). The partial spelling may be given using “Speech” or may be inputted using the keyboard / keypad or may be obtained using other input methods. The disclosed system is an alternative method for inputting text into devices; the method is faster (especially for long words or phrases) compared to existing predictive-text-input and / or word-completion methods.

There is disclosed a computer-implemented method of handling a text expressed in a natural language comprising creating a second text or patent claim sentence from a first text or patent claim sentence and timestamping said second text or patent claim sentence. Developments comprise the creation of a plurality of texts or patent claim sentences, the use of trusted and / or trustless timestamping, the use of grammatical texts, the use of a parser and / or of a tagger, modification operations such as addition, insertion and deletion, injection of definitions of words, the use of a thesaurus (synonym, hyponym, hyperonym, holonym, antonym of a word, etc), the use of a unique and optionally persistent web address, making the second text or patent claim available to the public (or not), the use of lexical directions such as a patent classification indication and the use of crowdsourcing techniques.

Calculates a word n-gram probability with high accuracy in a situation where a first corpus), which is a relatively small corpus containing manually segmented word information, and a second corpus, which is a relatively large corpus, are given as a training corpus that is storage containing vast quantities of sample sentences. Vocabulary including contextual information is expanded from words occurring in first corpus of relatively small size to words occurring in second corpus of relatively large size by using a word n-gram probability estimated from an unknown word model and the raw corpus. The first corpus (word-segmented) is used for calculating n-grams and the probability that the word boundary between two adjacent characters will be the boundary of two words (segmentation probability). The second corpus (word-unsegmented), in which probabilistic word boundaries are assigned based on information in the first corpus (word-segmented), is used for calculating a word n-grams.

A method and apparatus for mining text databases, employing sequential pattern phrase identification and shape queries, to discover trends. The method passes over a desired database using a dynamically generated shape query. Documents within the database are selected based on specific classifications and user defined partitions. Once a partition is specified, transaction IDs are assigned to the words in the text documents depending on their placement within each document. The transaction IDs encode both the position of each word within the document as well as representing sentence, paragraph, and section breaks, and are represented in one embodiment as long integers with the sentence boundaries. A maximum and minimum gap between words in the phrases and the minimum support all phrases must meet for the selected time period may be specified. A generalized sequential pattern method is used to generate those phrases in each partition that meet the minimum support threshold. The shape query engine takes the set of phrases for the partition of interest and selects those that match a given shape query. A query may take the form of requesting a trend such as "recent upwards trend", "recent spikes in usage", "downward trends", and "resurgence of usage". Once the phrases matching the shape query are found, they are presented to the user.

The present invention generally relates to systems and methods for the customization of structured data entry screen templates. The systems and methods include the hierarchical organization of data capture fields, the defining of macro-embedded sentences at each hierarchical level in addition to a set of rules for generating and aggregating sentences. Lastly, the raw captured data and macro-embedded sentences are stored and then merged together with style sheets to generate semantically rich, natural-language documents.

A system and method for speech recognition includes generating a set of likely hypotheses in recognizing speech, rescoring the likely hypotheses by using semantic content by employing semantic structured language models, and scoring parse trees to identify a best sentence according to the sentence's parse tree by employing the semantic structured language models to clarify the recognized speech.

The invention discloses a multi-triplets extraction method based on the entity relationship joint extraction model, comprises: performing segmentation processing on the target text, and tagging position, type and whether is involved with any relation or not of each word in the sentence; the joint extraction model of the entity relationship is established; the joint extraction model of the entity relationship is trained; the triple extraction is performed according to the joint extraction model of the entity relationship; the tri-part tagging scheme designed by the present invention is in the process of joint extraction of the entity relationship an entity that is not related to the target relationship can be excluded; the multi-triplets extraction method based on the entity relationship joint extraction model can be used to extract multiple triplets, and based on the model of the triplet extraction method of the present invention other models have stronger multi-triplets extraction capabilities.

This invention provides hierarchical, gradual and iterative methods, systems, and software for improving and correcting natural language text. The methods comprise the steps of applying natural language processing (NLP) algorithms to a corpus of sentences so as to abstract each sentence; applying scoring and linguistic annotation to each abstract sentence; applying NLP algorithms to abstract input sentences; applying search algorithms to match an abstract input sentence to at least one abstract corpus sentence; and applying NLP algorithms to adapt said matched abstract corpus sentence to the input sentence.

The invention discloses a text orientation analysis method which comprises the following steps of: preprocessing a review text; identifying a dependency relation structure of the Chinese syntax; calculating content polarity values of sentiment words; completing two-tuples extraction of evaluated objects and evaluation words and determining a slave relation between the evaluated objects; weighting and summing orientation values of the sentiment words to obtain an orientation value of a sentence so as to implement discrimination on orientation of a sentence level; discriminating appraising orientation of sentiment in the review by positive and negative polarity values of the sentence level; and according to the size of a polarity absolute value, discriminating intensity of appraising sentiment in the review. A product review orientation discriminator comprises an acquisition module, a preprocessing module, a syntactic analysis module, a sentiment calculating engine, a two-tuples mining engine, a content controller and a sentiment discriminator. According to the invention, a combined sentiment dictionary is combined and a domain ontology is added into text orientation analysis; accuracy of polarity calculation of the sentiment words and (the evaluated objects and the evaluation words) two-tuples extraction is improved; and orientation analysis on product reviews in a forum is implemented.

The invention provides a method and a system for converting the query sentence of a database. After a database table and a field are set, the name and field of the required database are selected in the foreground and the self-defined query sentence is inputted by a user according to the service requirement, and the query sentence inputted by the user is subjected to grammar analysis and validation, thus finishing the analysis by scanning the query sentence for only one time. By converting the service Chinese query sentence into a standard English SQL (structured query language) query sentence required by a service system, the invention fulfills the function of converting a service database query sentence into a standard executable SQL sentence, wherein the sentence acquired by the service system is directly available to the system, so that a common user can flexibly and freely use the database without mastering the query tool, SQL syntax and other professional technique of the database.

The invention provides a method of constructing a text classification model. The method comprises the steps of: constructing a training sample set according to structure features of characters, wordsand sentences of text information, wherein each piece of sample data in the training sample set corresponds to a feature matrix A of a piece of text information about the words and a feature matrix Babout the characters and a category vector O corresponding to the piece of text information, and the dimension number of O is the same as a category number; and using the feature matrix A about the words and the feature matrix B about the characters in the training matrix set as input and the corresponding category vector O as output to train a deep learning model to obtain the text classificationmodel. Classification is carried out according to the classification model constructed by the method, an accuracy rate of text classification can be improved, and the method is particularly suitablefor use in short-text classification.

The invention discloses an aspect-level emotion classification model and method based on dual-memory attention, belonging to the technical field of text emotion classification. The model of the invention mainly comprises three modules: an encoder composed of a standard GRU loop neural network, a GRU loop neural network decoder introducing a feedforward neural network attention layer and a Softmaxclassifier. The model treats input statements as a sequence, based on the attention paid to the position of the aspect-level words in the sentence, Two memory modules are constructed from the originaltext sequence and the hidden layer state of the encoder respectively. The randomly initialized attention distribution is fine-tuned through the attention layer of the feedforward neural network to capture the important emotional features in the sentences, and the encoder-decoder classification model is established based on the learning ability of the GRU loop neural network to the sequence to achieve aspect-level affective classification capabilities. The invention can remarkably improve the robustness of the text emotion classification and improve the classification accuracy.

Systems, devices, and methods of the present invention are related to determining an intent of an utterance. For example, an intent classification application accesses a sentence with fragments. The intent classification application creates a parse tree for the sentence. The intent classification application generates a discourse tree that represents rhetorical relationships between the fragments. The intent classification application matches each fragment that has a verb to a verb signature, thereby creating a communicative discourse tree. The intent classification application creates a parse thicket by combining the communicative discourse tree and the parse tree. The intent classification application determines an intent of the sentence from a predefined list of intent classes by applying a classification model to the parse thicket.

A system and method are disclosed for extracting information from text which can be performed without prior knowledge as to whether the text includes a list. The method applies parser rules to a sentence spanning lines of text to identify a set of candidate list items in the sentence. Each candidate list item is assigned a set of features including one or more non-linguistic feature and a linguistic feature. The linguistic feature defines a syntactic function of an element of the candidate list item that is able to be in a dependency relation with an element of an identified candidate list introducer in the same sentence. When two or more candidate list items are found with compatible sets of features, a list is generated which links these as list items of a common list introducer. Dependency relations are extracted between the list introducer and list items and information based on the extracted dependency relations is output.

Input texts, e.g., electronic text files containing a written work, are received by the invention. Selected visual characteristics of text, such as such as font case, font type, font size, font color, word spacing, and line spacing, are automatically applied to the text and are varied within the text to produce enhanced text. The variation may occur as frequently as character to character, or word to word, sentence to sentence, line to line, paragraph to paragraph, or some other frequency. The resulting enhanced text is visually different from the received text (provided the applied variations are not already present in the received text). The enhanced text is then visually displayed to a reader, as by providing it on a computer monitor or in printed form. The enhanced text, having more visible variety than the received text, is believed to result in greater reading speed, comprehension, and / or interest in people having certain types of reading deficiencies.

A system and method for generating word alignments from pairs of aligned text strings are provided. A corpus of text strings provides pairs of text strings, primarily sentences, in source and target languages. A first alignment between a text string pair creates links therebetween. Each link links a single token of the first text string to a single token of the second text string. A second alignment also creates links between the text string pair. In some cases, these links may correspond to bi-phrases. A modified first alignment is generated by selectively modifying links in the first alignment which include a word which is infrequent in the corpus, based on links generated in the second alignment. This results in removing at least some of the links for the infrequent words, allowing more compact and better quality bi-phrases, with higher vocabulary coverage, to be extracted for use in a machine translation system.

The invention provides a Q&A method based on a hierarchal memory network. The method comprises the following steps: firstly performing sentence granularity memory encoding, and finishing the information inference of the sentence granularity memory unit through an attention mechanism in multi-rounds of iterations under the stimulation of the problem semantic encoding, screening the sentence through k maximum sampling, and further performing the word granularity memory encoding on the basis of the sentence granularity encoding, namely, performing the memory encoding on two levels to form the hierarchal memory encoding; the output word probability distribution is predicted through the combination of the sentence granularity memory unit and the word granularity memory unit, the accuracy of the automatic Q&A is improved, and the answer selection problem of low-frequency word and unlisted word is effectively solved.

Techniques for determining sentence based interactive topic-based summarization are provided. A text to be summarized is segmented. Discrete keyword, key-phrase, n-gram, sentence and other sentence constituent based summaries are generated based on statistical measures for each text segment. Interactive topic-based summaries are displayed with human sensible omitted text indicators such as alternate colors, fonts, sounds, tactile elements or other human sensible display characteristics useful in indicating omitted text. Individual and / or combinations of discrete keyword, key-phrase, n-gram, sentence, noun phrase and sentence constituent based summaries are dynamically displayed to provide an overview of topic and subtopic development within a text. A hierarchical and interactive display of texts based on the use of discrete sentence constituent based summaries which associates expansible and contractible displayed text provides contextualized access to an interactive topic-based text summary and to an original text.

The invention discloses a text classification method combining dynamic word embedding with part-of-speech tagging, and provides the text classification method based on a deep neural network through combining dynamic word embedding with part-of-speech tagging. The method can fully utilize the advantages that a large-scale corpus can provide more accurate grammar and semantic information, and can also adjust word embedding by combining with the features of the corpus during a model training process, and thus the features of the corpus can be better learned. Meanwhile, classification accuracy can be further improved by combining with part of speech information of words in sentences. The invention also comprehensively utilizes the advantages of LSTM in the aspect of learning context information of words and part of speech in the sentences, and the advantages of CNN in the aspect of learning text local features. The classification model provided by the invention has the advantages of high accuracy and strong universality, and achieves good effect in some famous public corpuses including IMDB corpus, Movie Review and TREC.

This invention discloses a method and a device for optimizing query SQL request, in which, the method includes: standardizing the field names and bynames in the SQL sentences to eliminate the redundant associated relations in them, converting said SQL sentences into a grammar tree to be optimized for logic relation to be converted to standard SQL sentences.

A system for coding and decoding languages that includes a first set of computerized databases and each of database storing a predetermined number of indexed information elements (such as words) and a second set of computerized databases wherein each database includes a predetermined number of structural arrangements for using the information elements. The indexed information elements are classified by the component class of a sentence or phrase. They may be classified as verbs, adjectives, etc. and this holds true for the same indexed entry for the different databases for a particular information element. The system decodes the information elements in a sentence, or phrase and matches a corresponding structural arrangement in a given language which holds for the other language.

The invention discloses a commodity target word oriented emotional tendency analysis method, which belongs to the field of the analysis processing of online shopping commodity reviews. The method comprises the following four steps that: 1: corpus preprocessing: carrying out word segmentation on a dataset, and converting a category label into a vector form according to a category number; 2: word vector training: training review data subjected to the word segmentation through a CBOW (Continuous Bag-of-Words Model) to obtain a word vector; 3: adopting a neural network structure, and using an LSTM(Long Short Term Memory) network model structure to enable the network to pay attention to whole-sentence contents; and 4: review sentence emotion classification: taking the output of the neural network as the input of a Softmax function to obtain a final result. By use of the method, semantic description in a semantic space is more accurate, the data is trained through the neural network so as to optimize the weight and the offset parameter in the neural network, parameters trained after continuous iteration make a loss value minimum, at the time, the trained parameters are used for traininga test set, and therefore, higher accuracy can be obtained.

The invention is a kind of Chinese and foreign languages double-way automatic translation method which is based on the sentential form conversion and adopts the Chinese phonetic codes with the word as the unit, and it belongs to the automatic translation technology field. By the method we can translate Chinese and another language such as English to each other expediently, on one hand, it overcomes the present faults that all the Chinese-foreign languages translation can only be done by the Chinese characters and Chinese spelling because of that Chinese uses the connecting written Chinese phonetic codes by word which only adopts twenty-six Latin letters as the code element and includes Chinese sound, rhythm and tone to expresses information when translating, that Chinese characters and Chinese phonetic signs can not be compatible with ASCII codes completely and the malpractice that before every translation, the Chinese information that Chinese characters expresses has to be divided to single word, on the other hand, because of adopting the sentential form conversion method which bases on the lexical and the syntax system coincident with the goal language, to make the automatic translation method more exact than the traditional translation method, and the effectiveness better. However the inputting Chinese form is Chinese characters, Chinese spelling or Chinese phonetic codes, it can not only translates by the traditional method but also translate after changing the Chinese characters to Chinese phonetic codes, likely, if needed, the Chinese phonetic codes gotten after translation can not only express the Chinese information directly, but also be conversed to Chinese characters, Chinese spelling, Chinese sound, Chinese special person, Chinese localism or minority language sound.

A method for enhancing a text presentation comprising: performing a linguistic analysis of a plurality of sentences, each comprising a plurality of linguistic elements, in a text content, using analyzed data created by said linguistic analysis to match between a group of said plurality of sentences and a plurality of interactive text related object templates, creating a plurality of interactive text related objects each for a member of said group, each one of said plurality of interactive text related objects is created by placing a respective said analyzed data into a respective said interactive text related object template, and causing a client device to present simultaneously at least one of said plurality of interactive text related objects and a text section of said text content that includes a marking of at least one linguistic element of a respective member of said group.

The invention relates to an emotion classification method and system, a storage medium and equipment. The method comprises: encoding a context by using a position word vector and multi-head self-attention; encoding the target word by using a bidirectional gating circulation unit and multi-head self-attention; fully extracting semantic information and position information of long and short sentences; meanwhile, interactively splicing the context semantic information and the target word semantic information for low-order fusion; performing position coding on the basis of low-order fusion by using a capsule network; and performing high-order fusion on the information after low-order fusion by using a multi-head interaction attention mechanism, averagely pooling the high-order fusion information, and splicing the averagely pooled high-order fusion information with the averagely pooled target word semantic information and the averagely pooled context semantic information to obtain target feature representation. Compared with the prior art, the context semantic information, the target word semantic information and the position information are fully fused, and the emotion classification accuracy and efficiency are improved.