Patent Publication Number: US-11663403-B2

Title: System and method for detecting undesirable and potentially harmful online behavior

Description:
RELATED APPLICATIONS 
     The present application is a continuation of, relates to, and claims the benefit of priority to U.S. patent application Ser. No. 17/478,328 filed on Sep. 17, 2021, which claims benefit of priority of U.S. patent application Ser. No. 17/191,429 filed on Mar. 3, 2021, which claims benefit of priority of U.S. patent application Ser. No. 16/289,881 filed on Mar. 1, 2019, which claims benefit of priority to U.S. Provisional Patent Application No. 62/638,073, filed Mar. 3, 2018, which are hereby incorporated by reference herein in their entirety for all purposes as if fully set forth herein. 
    
    
     FIELD OF THE INVENTION 
     Inventions disclosed and claimed herein are in the field of natural language processing (NLP). 
     BACKGROUND 
     Online violence can be broadly defined as any form of abusing, harassing, bullying or exploiting other people, using electronic means. Some communication phenomena such as hate speech, toxic speech or abusive language overlap with online violence to some extent, whereas the other phenomena like cyberbullying or sexual harassment are entirely included into online violence. Online violence puts emphasis on harming other people. For example, using the word “f*cking” as an intensifier to stress out the positive emotions towards other person, e.g. “you are f*cking awesome”, is not online violence. 
     A number of methods exist for detecting online violence, its subclasses or similar phenomena. Most of them utilize statistical learning techniques such as machine/deep learning, where a classification model is trained on a set of labeled examples.  FIG.  2    is a block diagram of prior art online violence detection system  200 . The input is a text, which is processed by Online Violence Detection System  202  consisting of Machine Learning Techniques  204  trained on Manually Annotated Corpus  206 . Online Violence Detection System  202  produces Online Violence Detection System Output  208  which is then used by Other Systems/Applications  150 . 
     Although the learning capabilities of statistical learning techniques are impressive, especially the ones utilizing deep artificial neural networks, this approach is lumbered with many important drawbacks, including (but not limited to): 
     1. It requires a large set of annotated examples for each category. Therefore, the systems built with this approach usually offer a very limited number of categories or even only a binary classification (e.g. toxic/non-toxic). Furthermore, the most dangerous phenomena are usually very rare so there is a problem in getting large enough labeled dataset to train an accurate classifier.
 
2. It tends to over-focus on certain keywords and expressions that often co-occur with online violence such as vulgarisms. For example, many statistical systems have problems recognizing even the simplest forms of negation such as “you are not an idiot” or recognizing non-abusive context in utterances like “rapists should spend rest of their life in prison.”
 
3. It is vulnerable to adversarial attacks. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake. In terms of online violence detection, it can be: word changes (e.g. inserting a typo), word-boundary changes (e.g. inserting or removing whitespaces), or word appending (e.g. inserting positive non-hateful words).
 
4. It is a “blackbox” as it is impossible to say why the system made certain decision. One cannot track the decision-making process and cannot change anything within the model other than by retraining it on a different dataset or changing the learning algorithm or the architecture. It has a number of negative consequences, but the most obvious are significant difficulties in developing and maintaining such system.
 
5. It strongly depends on the annotation quality and the data source used for training. A statistical model trained on one data source (e.g. Twitter) shows a significant drop in accuracy when used on other data source (e.g. Reddit). The same applies to the annotation quality. If a model was trained using given annotation guidelines, it will not work well for other guidelines. Furthermore, if the annotation contains mistakes, it will affect the model&#39;s performance.
 
     Statistical models achieve low precision that effectively excludes the systems using such models from being used as proactive prevention instead of post factum moderation within a human-in-the-loop model. According to other limitations, such systems are not effective in detecting rare online violence phenomena and can be easily cheated or spoofed with adversarial examples. The latter affects the recall of such systems when applied to real online communities, especially if the users know that there is an online violence detection system monitoring their communication. Furthermore, such systems cannot be customized or relatively quickly adjusted to different services, data sources and guidelines, which is an important market need. 
     On the other hand, symbolic systems demonstrate even more severe drawbacks, which exclude them from the mainstream in both academic and industry applications. Due to the lack of learning capabilities, symbolic systems mostly rely on hand-crafted rules and knowledge bases. The process of building rules and knowledge bases is tedious, expensive and usually requires a number of well-educated specialists representing a range of disciplines such as computer science, linguistics, psychology, and so on. Furthermore, as the complexity of such system increases, it becomes very hard, and sometimes nearly impossible, to develop and maintain these systems. 
     It would be desirable to have an online violence detection system that combines the advantages of symbolic and statistical approaches, greatly reducing or even eliminating the individual drawbacks of each of them. It would be desirable to have an online violence detection system that is able to detect and name a range of online violence phenomena, including the rare dangerous phenomena, with both high precision and recall. High precision would be an enabling factor for changing the way in which online services handle the problem of online violence—from post factum moderation within the human-in-the-loop model to automatic prevention allowing the blocking of dangerous content before the damage is done. It would be desirable to have an online violence detection system that deeply analyzes the context in order to precisely detect complex and non-obvious online violence cases, and to prevent raising too many false alarms. It would be desirable to have a method for building and developing such online violence detection system that do not require large sets of annotated data, or even to enable such online violence detection system to learn directly from unlabeled datasets. Such a method should allow the labeled and unlabeled datasets to be drawn upon in order to make the system better, but should not make the system dependent on those datasets. It would be desirable to have an effective method for incorporating the knowledge of experts such as psychologists, sociologists, pedagogues, psychiatrists into the system, and for customizing and adjusting the system to new data sources, community guidelines, and other client&#39;s needs. It would be desirable to have a method for developing and maintaining such system that allows to precisely find the root causes of errors and problems, to plan the development short- and long-term, and to build the development teams that could work on different aspects of the system independently. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an online violence detection system environment according to an embodiment. 
         FIG.  2    is a block diagram of a prior art online violence detection system. 
         FIG.  3    is a block diagram of an online violence detection system according to an embodiment. 
         FIG.  4    is a block diagram of text preparation according to an embodiment. 
         FIG.  5    is a block diagram of normalization (part of text preparation) according to an embodiment. 
         FIG.  6    is a block diagram of correction (part of text preparation) according to an embodiment. 
         FIG.  7    is a block diagram of transformation (part of text preparation) according to an embodiment. 
         FIGS.  8 - 9    are diagrams illustrating the process of text preparation according to an embodiment. 
         FIG.  10    is a block diagram of detection engine according to an embodiment. 
         FIG.  11    is a block diagram of violence towards second person detection (part of detection engine) according to an embodiment. 
         FIG.  12    is a diagram illustrating the division of online violence phenomena into sub-classes according to an embodiment. 
         FIG.  13    is a block diagram of transformations to second person (part of detection engine) according to an embodiment. 
         FIG.  14    is a block diagram of other violence detection (part of detection engine) according to an embodiment 
         FIG.  15    is a block diagram of general architecture of online violence detection modules according to an embodiment. 
         FIG.  16    is a block diagram of symbolic processing component according to an embodiment. 
         FIG.  17    is a diagram illustrating syntactic parser output according to an embodiment. 
         FIGS.  18 - 19    are diagrams illustrating the operation of online violence detection modules according to an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     The present invention relates to computer-implemented methods and systems for detecting undesirable and potentially harmful online behavior. The embodiments described and claimed could also be applied to detecting any other type of online behavior to be detected, but the descriptions focus on detecting online violence. More particularly, the embodiments disclosed relate to detecting online violence using symbolic methods of natural language processing (NLP) that utilize and govern the usage of: 1) syntactic parser for analyzing grammatical context of the input text data, 2) unsupervised learning methods for improving selected aspects of the system and adjusting the system to new data sources and guidelines, and 3) statistical classifiers for resolving specific well-defined sub-tasks, in which statistical approaches surpass the symbolic methods. The online violence detection system comprises a hierarchical modular architecture, utilizing contextual models to detect specific well-defined online violence phenomena. The contextual models are built using the above-mentioned methods and organized into modules and submodules of increasing complexity representing responding types and subtypes of online violence. 
     Embodiments of inventions disclosed herein include improvements on current NLP systems and methods for an automatic detection of online violence by processing input that consists of text from different types of context. The input text can be obtained from any source of electronic data that could serve as a source of text input to the NLP system, including (but not limited to): plain text, text files, word processing documents, CSV files, XML, files. 
       FIG.  1    is a block diagram of an online violence detection system environment  100  according to an embodiment. Online Violence Detection System  110  accepts text as input. Text can include electronic data from many sources, such as the Internet, physical media (e.g. hard disc), a network connected database, etc. Online Violence Detection System  110  includes multiple System Databases  110 A, Dictionary  540  databases and Rule Libraries  550  databases, and multiple System Processors  110 B, Syntactic Parser  520  processors and Statistical Classifiers  530  processors. Databases  110 A,  540 ,  550  and processors  110 B,  520 ,  530  can be located anywhere that is accessible to a connected network  120 , which is typically the Internet. Databases  110 A,  540 ,  550  and processors  110 B,  520 ,  530  can also be distributed geographically in the known manner. Data Sources  130  include any source of electronic data that could serve as a source of text input to Online Violence Detection System  110 . 
     Other Systems/Applications  150  are systems, including commercial and non-commercial systems, and associated software applications that have the capability to access and use the output of Online Violence Detection System  110  through one or more application programming interface (APIs) as further described below. For example, Other Systems/Applications  150  can include:
         an online application offering its users a chatting option that is monitored for online violence and proactively moderated by blocking messages containing online violence before they reach the receiver (online violence prevention),   a mobile application for parents that monitors their children&#39;s online communication and alerts when children is exposed to online violence.       

     End Users  140  includes individuals who might use Other Systems/Applications  150  through one or more of End User Devices  140 A. End User Devices  140 A include without limitation personal computers, smart phones, tablet computers, and so on. In some embodiments, End Users  140  access Online Violence Detection System  110  directly through one or more APIs presented by Online Violence Detection System  110 . 
     Online violence (or cyber violence) is any form of abusing, harassing, bullying or exploiting other people, using electronic means. In other words, the term “online violence” used in this document should be understood in the broadest sense—as any activity or behavior of online communication that can harm or lead to harming other people, with the strong emphasis on (but not limited to) protecting an interlocutor. Phenomena such as personal attacks, sexual harassments, cyberbullying, threatening, blackmailing naturally fall to this category. For reasons of clarity, also some non-obvious phenomena are included into this category, such as: sexting, sex trafficking, racism, sexism, violence against minorities, persuading self-harm and violence against others. According to the definition, each of these phenomena can be seen as an example of abusing, harassing, bullying or exploiting other people, using electronic means. For example, sexting can be seen as an example of exploitation or harassing if one knows that the receiver of the message is a child that should not be presented with such content. Another special emphasis is put on protecting children, and therefore online violence covers any violent, harming or exploiting phenomena targeted against children, including: child grooming, child exploitation, pedophile attacks, extortion or coaxing of impropriate items or actions (e.g. nude pictures), and so on. Additionally, declaration of self-harming is included to online violence as an exception since it does not fulfill the definition. Although it is not targeted against other people, it is a very dangerous phenomenon that should be detected in order to prevent putting words into action. 
     The system and method comprise a novel approach to online violence detection that combines statistical methods (including machine and deep learning models) under the strict government of the symbolic processing, strongly relying on grammar structure. The system and method can be characterized by two important advantages in comparison to the state of the art approaches: high precision and broad division into diverse online violence phenomena. 
     High precision is an enabling factor for the detection system to be used for active prevention rather than post factum moderation. Current systems achieve low precision which results in a large number of false alarms. Taking an automatic action against a user detected with low precision system is very risky because such action can be a false accusation that effectively leads to the user&#39;s dissatisfaction or even resignation from using the service. Therefore, current system can be used as an aid for human moderation, not as an autonomous prevention system. 
     Broad division allows the assignment of a specific action to a specific detected phenomenon. The system and method allows the building and execution of narrow specialized detection models with a minimal amount (or even none) of training data. For example, one can set up different reactions for the detection of personal attacks, blackmailing and child grooming. Furthermore, some narrow models such as detection of nude picture extortion can be used to educate the potential victim, e.g. by informing that it is okay to tell parents and not fulfill these requests. 
       FIG.  3    is a block diagram illustrating the high-level architecture of the inventions according to an embodiment. Input text is received by Text Preparation  112 . Text Preparation  112  is responsible for preparing the input text to be further processed by Detection Engine  114 . Detection Engine  114  is responsible for determining if the input text contains online violence and—if so, what is the category of detected online violence. The output of the Detection Engine  114  is referred to here as System Output API  116 , and can be operated on by a variety of downstream software applications or systems as described in this document, referred to here as Other Systems/Applications  150 . Although the output of the system is referred as Application Programming Interface (API) in System Output API  116 , any reasonable alternatives are possible. 
     Text Preparation 
     Detection Engine  114  requires a normalized and corrected text to operate. The goal of Text Preparation  112  is to perform a broad set of text manipulation operations in order to convert the input text into the text of the form required by Detection Engine  114 . Text Preparation  112  is the only of the two system&#39;s components that allows the manipulation of the text string by changing, adding or removing the sub-strings. The original text string is preserved as a separated entity “text_orig.” 
       FIG.  4    is a block diagram showing the process of Text Preparation  112  according to an embodiment. The input of Text Preparation  112  consists of plain text. The process of Text Preparation  112  comprises three consecutive modules: Normalization  302 , Correction  304 , and Transformation  306 . The standard process utilizes one time pass. However, it is possible to perform subsequent passes if needed. Every Text Preparation  112  module has an access to all of the system&#39;s assets, features and functions such as syntactic parser, statistical classifiers, dictionaries and libraries. Any of these resources can be used in any module and submodule. Normalization  302  comprises the process of adjusting an input text into a predefined standard format. 
       FIG.  5    is a block diagram providing detailed division of Normalization  302  into a set of submodules realizing specific tasks. 
     Character Encoding  302 A is responsible for converting all characters in input text to a default encoding, including characters and character codes in any known form (e.g. “\u2018”, “0x2018”). Redundant Repetitions  302 B deletes a variety of well-defined sub-string repetitions in input text, including (but not limited to):
         repetitions of letters (more than two to two as there are no English words with more than two occurrence of the same letter),   repetitions of words (with exception for meaningful repetitions such as proper nouns),   repetitions of whole utterances,   repetitions of punctuation marks.       

     Tags/Markers/Code Snippets  302 C deletes or changes to acceptable words and characters a variety of usage of markups and formal (not natural) languages, including (but not limited to): HTML tags, BBCode tags, CSS styling, source-related tags, any known programming language code snippets. 
     Emoticons  302 D deletes or changes detected emoticons (including various styles such as western style, kaomoji) into acceptable words and characters. Each emoticon is changed into descriptive expression from an input text language, e.g. “:-(” can be changed to “i am sad.” sub-string. 
     Contractions  302 E expands shortened versions of the written forms of a words into their full normalized version, e.g. “you&#39;re” into “you are.” It also covers unambiguous incorrect contractions such as “youre” into “you are,” but not “hell” as it can be “he will” as well as the word “hell.” 
     Whitespaces  302 F normalizes any occurrence of whitespaces, including (but not limited to): spaces, tabulations, next lines. The normalization can be different for different occurrences of the same whitespace. 
     Segmentation  302 G is responsible for controlling the sentence division. It detects and inserts omitted full stops, and handles scattered text pieces (e.g. using certain whitespaces or tags introducing tabulations or new lines). It also secures the usage of dots in abbreviations. 
     Entity Recognition  302 H covers the recognition of a variety of well-defined entities, including (but not limited to):
         numbers,   series of numbers,   mathematic equations,   date/time information,   publication/article details (e.g. publication date, references),   identification numbers (IDs),   telephone/fax numbers,   IP addresses,   email addresses,   Uniform Resource Locators (URLs),   geographic coordinates,   money amounts,   filenames,   domain- and source-specific entities (e.g. Reddit username and subreddit conventions).       

     The recognized entities are changed into a normalized form that is further handled by Detection Engine  114  and other Text Preparation  112  modules. 
     Other Normalization  3021  covers every other normalization that does not fall into the above-mentioned modules. 
     Correction  304  (as described below with reference to  FIG.  6   ) comprises the process of revising the normalized text in order to remove misspellings and any other errors and obstacles that may impede the work of Detection Engine  114 . The process of Correction  304  (its every submodule) utilizes two major approaches:
         dictionary-based—using various algorithms for calculating “distances” (or similarity metrics) between misspelled words and correct dictionary entries; it may result in a replacement or in generating a list of possible candidates;   context-based—using various algorithms for selecting a proper candidate by establishing what is required within a given context; it may find a replacement by itself or use a list of candidates for other module or method, including the usage of dictionary-based correction; examples of context-based correction: “its a miracle” into “it is a miracle”, “you are eal stupid” into “you are really stupid.”       

       FIG.  6    is a block diagram providing detailed division of Correction  304  into a set of submodules realizing specific tasks. 
     Common Typos (Statistical)  304 A replaces incorrectly written words with their correct version based on statistical data, e.g. “accomodation” into “accommodation.” The statistical data can come form any source, including (but not limited to):
         existing databases of common typos,   dedicated submodules (internal or external) that builds up the dictionaries and patterns based on processing text corpora (either labeled or unlabeled),   built-in mechanisms allowing system&#39;s users to correct the data manually and collecting their activities (those can be used directly or as a training dataset for machine learning correction modules).       

     Common Typos (Rule-based)  304 B utilizes trial and error method for replacing common patterns that are often misspelled, e.g. “-ht” into “-th”, “-in” into “-ing”, “ae” into “ea.” The modified words are validated against the available dictionaries. 
     General Typos  304 C is the main submodule combining various techniques for correcting typos. Its core comprises the measurement of word distances that can be further parametrized according to the source of data and other aspects (e.g. cost of letter changes based on keyboard arrangement, sensibility to bad language, homophones). The parameters can be adjusted manually, semi-automatically or automatically, based on processing of unlabeled datasets from a given data source. 
     Word Boundaries  304 D resolves situations where the boundaries of words are incorrectly changed. The submodule takes into consideration intentional changes, including:
         splitting (e.g. “you are s t u p i d.”),   joining (e.g. “youarestupid.”),   combination of splitting and joining (e.g. “y ouares t u p id.”).       

     Additionally, the submodule resolves ambiguous usages of dash and hyphen, detecting its desired function and deciding if it should be removed, preserved, changed from one to another (e.g. dash to hyphen) or changed into other more suitable character (e.g. colon). 
     Enumeration/Listing  304 E resolves any (complete and partial) usage of enumeration and listing in input text. The enumeration/listing markups are removed or changed in order to not interfere segmentation or any other operations that take into consideration sentence boundaries. The submodule tries to make the best decision based on the type and supposed purpose of the enumeration/listing. For example, enumerations/listings of words and phrases are often separated with commas, whereas sentences are separated with full stops. Furthermore, the submodule normalizes the usage of commas and conjunctions. 
     “Stars” (Letter Replacement)  304 F resolves situations where one or more letters in a word is/are replaced with other non-letter characters (e.g. “*”, “#”, “.”). This kind of replacement within abusive words is often used to mislead the detection system. The submodule allows to correct even the words with altered beginnings as well as with the combination of different characters. It validates 1-to-1 replacement as well as any other options such as 1-to-many, many-to-1, many-to-many, 1-or-many-to-zero. 
     Leet Speak  304 G resolves letter replacements related to so-called leet speak, a system of modified spellings. Leet speak often replaces letters with similarly looking characters or modifies words based on a system of suffixes and alternate meanings. The submodule replaces the replacements with the correct letters, e.g. “1 h4t3 y0v” is changed into “I hate you”, “1 h0p3 u d13” into “I hope you die.” 
     Part-of-speech Agreements  304 H resolves syntactic errors based on desirable part-of-speech patterns. The submodule makes use of the fact that certain combinations of parts-of-speech in certain order are highly unlikely or even impossible. Examples: “you are real stupid” into “you are really stupid”, “their a bunch of idiots” into “there are a bunch of idiots.” 
     Other Correction  304 I covers every other correction that does not fall into the above-mentioned modules. 
     As default, Correction  304  performs two passes in order to spot any combination of the above-mentioned phenomena. Furthermore, the submodules can overlap and therefore it is possible to correct any reasonable combination such as Leet Speak  304 G and Word Boundaries  304 D—“@ $ $ h 0 1 3” is changed into “asshole.” It is also possible to set triggers executing additional passes based on various parameters, e.g. percentage of dictionary-verified words. 
     Transformation  306  comprises the process of revising the corrected text in order to make it more suitable for Detection Engine  114 . The main goal of Transformation  306  is to remove ambiguity of input text by performing well-defined string manipulations.  FIG.  7    is a block diagram providing detailed division of Transformation  306  into a set of submodules realizing specific tasks. 
     Technical/Structural  306 A provides changes within general structure of texts and sentences, such as punctuation and segmentation-related indicators. There are two major aspects of this submodule:
         sentence merging/splitting—if consecutive sentences make more sense as a single sentence, or if a single sentence makes more sense as separated sentences, they are merged together or split up (additional connector can be added if required), e.g. “This dress. Is beautiful. You are not.” is changed into “This dress is beautiful, but you are not.”   comma insertion/deletion—if a comma is incorrectly omitted (or a sentence is more comprehensible with a comma), or a comma is redundant, the comma is added to/removed from the sentence, e.g. “Read my page idiot.” is changed into “Read my page, idiot.”       

     To Abusive  306 B replaces expressions (words, phrases, etc.) that are abusive in a given context but were softened or deprived of coarse language in order to hide the bad intention (euphemisms, idioms, etc.) with a single semantically equivalent expression that means the same and is unambiguous (or strictly abusive), e.g. “up your bottom” is changed into “up your ass”, “polishing the spear” into “masturbating.” All of the semantic transformations are convertible and preserve all the available information such as appropriate grammar forms such as verb forms, e.g. “i saw you shook your meat yesterday” is changed into “i saw you masturbated yesterday.” 
     To Non-abusive  306 C is a reciprocal of To Abusive  306 B. It replaces expressions that are not abusive but contain bad language with clearly non-abusive expressions. It lowers the probability of incorrect interpretation of such expressions. For example, “raping your inbox” is changed into “spamming your inbox”, “being the shit” into “being the best.” All of the semantic transformations are convertible and preserve all the available information. 
     To Be Deleted  306 D removes expressions (words, phrases, etc.) that are irrelevant to the meaning in a given context, such as part of interjections (e.g. “wow”, “oh”), part of internet slang abbreviations (e.g. “lol”, “rofl”), part of onomatopoeias (e.g. “blah blah”, “hahaha”) and word “that” when it can be omitted (e.g. “i hope that you die” is changed into “i hope you die”). Furthermore, the module can detect abusive words used as intensifiers and remove them as well, e.g. “you are fucking awesome” is changed into “you are awesome.” Alternatively, the intensifiers can be handled by the To Non-abusive  306 C module. 
     Coreference Resolution  306 E replaces referring expressions such as pronouns with the right entities that they refer to. It simplifies the detection process as usually the simpler relations need to be detected. The submodule operates on any referring expressions, not only pronouns. It also allows to distinguish nominal and adjectival aspects of the entities. For example, “jews are greedy. i hate their religion.” is changed into “jews are greedy. i hate jewish religion.”, “i saw your mom yesterday. she is fat like a pig.” into “i saw your mom yesterday. your mom is fat like a pig.” 
     Omission Fulfillment  306 F fulfills (usually using “copy and paste” method) expressions (words, phrases, etc.) that can be omitted in natural human communication, but their absence can be problematic for the system to understand the context, including (but not limited to):
         cloning subject+part of predicate if needed, e.g. “you need to crawl under a rock and die” is changed into “you need to crawl under a rock and you need to die”,   cloning subject+predicate, e.g. “why do not you send me $1000. if not, i will kill your dog.” is changed into “why do not you send me $1000. if you do not, i will kill your dog.”,   cloning infinitive particle “to”, e.g. “i am going to hunt you down and murder you violently” is changed into “i am going to hunt you down and to murder you violently.”       

     Additionally, Omission Fulfillment  306 F fulfills words that are incorrectly omitted, based on a validation of grammar correctness. The algorithm fulfill the lack with the most probable word based on a given context. This aspect is similar to Part-of-speech Agreements  304 H from Correction  304  module. For example, “you an idiot” is changed into “you are an idiot”, “you should nice to your mom” into “you should be nice to your mom.” 
     Entity Conversion  306 G detects named entities and other strings that can be considered to be abusive in a given context, including (but not limited to):
         person/brand/music band names,   song/movie/book/TV show title,   famous quotes,   song lyrics.       

     Detected entities and strings are replaced with equivalents that are neutral to Detection Engine  114 , e.g. “john tucker must die” is replaced with “movie title.” 
     Grammar Construction  306 H covers the transformation of a well-defined part of the grammar constructions that either exclude the otherwise violent utterance from being detected or changes the non-violent utterances in order to become detectable by Detection Engine  114 . There are three major constructions handled by this submodule:
         quoting—using explicit utterances (direct speech) of other people, e.g. “he said: ‘you are an idiot,’ and left the room.”   reported speech—expressing other people&#39;s utterances without quoting them, e.g. “he said that you are an idiot.”   calling x y—this is a collective name for a group of constructions that indirectly assign an abuse to an object, e.g. “i am sorry for calling you an idiot.”       

     The other part of constructions excluding the otherwise violent utterance from being detected is covered by Contradiction Checking which is broadly described in Detection Engine section. 
     Every grammar construction can be placed in any of these two submodules. A decision where a given construction should be placed (Grammar Construction  306 H or Contradiction Checking) depends on soft guidelines:
         any form of expressing other people&#39;s utterances should be placed in Grammar Construction  306 H,   expressions that require to check both sides of the potentially detected statement (what is before and after) should be placed in Grammar Construction  306 H,   counter-factual expressions should be placed in Contradiction Checking,   negation-related expressions should be placed in Contradiction Checking,   specific constructions (applicable to only a part of detection models) should be placed in Contradiction Checking.       

     The information about detected grammar construction is passed through the whole detection process so it is possible for the system to tell that the utterance contains certain category of online violence (e.g. direct personal attack), but the violence occurred in a quote. Grammar Construction  306 H can replace detected content with anything neutral to Detection Engine  114 . 
     Other Transformation  3061  covers every other transformation that does not fall into the above-mentioned modules. 
       FIGS.  8 - 9    are diagrams illustrating a process of Text Preparation  112  module according to an embodiment. Referring to  FIG.  8   , the input to Text Preparation  112  module is the text “Ima cum 2 ur h0u$3 . . . &amp;amp; \u2018mak sure\u2019 dat u w0n+have kids !!!1!” According to Normalization  302 , five submodules are used: Character Encoding  302 A, Redundant Repetitions  302 B, Tags/Markers/Code Snippets  302 C, Contractions  302 E, and Whitespaces  302 F. After Normalization  302 , the input text for Correction  304  is: “i am going to cum 2 ur h0u$3 . . . and “mak sure” dat u w0n+have kids!” According to Correction  304 , three submodules are used: Common Typos (Statistical)  304 A, General Typos  304 C, and Leet Speak  304 G. After Correction  304 , the input text for Transformation  306  is: “i am going to come to your house . . . and “make sure” that you will not have kids!” According to Transformation  306 , three submodules are used: Technical/Structural  306 A, To Be Deleted  306 D, and Omission Fulfillment  306 F. The output of Text Preparation  112  module is: “i am going to come to your house and i am going to make sure you will not have kids!” Referring to  FIG.  9   , the input to Text Preparation  112  module is: “I HaT GAYZ,,.,..&lt;br&gt;tHe SHuOLD BuRyED iN Da GRoUnD . . . ” According to Normalization  302 , two submodules are used: Redundant Repetitions  302 B, and Tags/Markers/Code Snippets  302 C. According to Correction  304 , four submodules are used: Common Typos (Statistical)  304 A, Common Typos (Rule-based)  304 B, General Typos  304 C, and Leet Speak  304 G. According to Transformation  306 , two submodules are used: Coreference Resolution  306 E, and Omission Fulfillment  306 F. The output of Text Preparation  112  module is: “i hate gays. gays should be buried in the ground.” 
     Detection Engine 
     Referring briefly to  FIG.  3   , the output of Text Preparation  112  is received by Detection Engine  114  module.  FIG.  10    is a block diagram of Detection Engine  114  module architecture according to an embodiment. In an embodiment, Detection Engine  114  comprises three submodules: Violence Towards Second Person Detection (VTSPD)  402 , Transformations To Second Person (TTSP)  404 , and Other Violence Detection (OVD)  406 . According to an embodiment, only Violence Towards Second Person Detection (VTSPD)  402  is obligatory, the other two submodules are optional, but allow to expand the detection process to online violence phenomena targeted against any other objects than interlocutor. Other embodiments allow to include more submodules, following the same architecture described in this document. 
     Violence Towards Second Person Detection (VTSPD)  402  comprises the core of Detection Engine  114  as potentially the most damaging online violence phenomena are targeted against an interlocutor. VTSPD utilizes a set of contextual models for detecting certain types of online violence. A contextual model can be seen as a complete functioning detection model for detecting a single well-defined category of online violence. Although the contextual models are independent, they can overlap each other (e.g. a given violent sentence can be detected by more than one contextual model). Furthermore, contextual models can utilize the same system&#39;s assets such as dictionaries or rule libraries. According to an embodiment, contextual models related to certain type of online violence are grouped together as a submodule of Detection Engine  114 , defining the target of online violence—an interlocutor in case of Violence Towards Second Person Detection (VTSPD)  402 . Other embodiments may comprise different functional structure. 
       FIG.  11    is a block diagram providing detailed division of Violence Towards Second Person Detection (VTSPD)  402  into a set of contextual models detecting certain types of online violence. As contextual models are independent from each other, their order is completely arbitrary. All of the following contextual models are presented as components of VTSPD and therefore the terms “interlocutor” and “second person” are used to point out the target of online violence. Aside from obvious second person indicators (such as pronoun “you”), VTSPD also covers online violence targeted against interlocutor&#39;s relatives and other persons/things important for the interlocutor. Therefore, whenever the terms “interlocutor” and “second person” are used, the broader definition should be concerned. According to an embodiment, violence towards other persons is detected using VTSPD after performing Transformations To Second Person (TTSP)  404 . Other embodiments may comprise different functional structure. 
     Contextual models can be further divided into contextual sub-models which detect particular online violence phenomena within a given type of online violence. The following description of contextual models contains exemplary divisions into online violence phenomena. Such division can be a resemblance of the division into contextual sub-models. For the demonstration purpose, in case of two sub-models—Blackmails  402 C and Sexual/Child Exploitation  402 E, the division into contextual sub-models will be presented explicitly. All examples in the following section are presented as they would be after Text Preparation  112 . It means that the exemplary texts are lowercase and do not have contractions, abbreviations, misspellings, and other elements corrected and normalized in Text Preparation  112 . 
     Personal Attacks  402 A detects any forms of abusive remarks on or relating to an interlocutor, including (but not limited to):
         direct abuses, e.g. “you are an idiot”,   indirect abuses, e.g. “stop acting like an idiot”,   abusive comparisons, e.g. “you smell like a skunk”,   abuses using vocative case, e.g. “hold on, idiot”,   part of cyberbullying related with personal remarks, e.g. “your parents were paid to adopt you.”       

     Threats  402 B detects any forms of threats, bad intentions and bad wishes towards an interlocutor, including (but not limited to):
         direct threats, e.g. “i am going to kill you”,   indirect threats, e.g. “sooner or later we will get you”,   implied threats, e.g. “i know where you live”,   intentions and wishes for harming an interlocutor, e.g. “i wish you have aid”,   regrets for non-harming an interlocutor, e.g. “too bad your family is not dead.”       

     Blackmails  402 C detects blackmails, coercions and part of extortions.  FIG.  12    illustrates the division of Blackmails  402 C into contextual sub-models. Four contextual sub-models are disclosed (there are more):
         revealing information, e.g. “give me money or your parents will find out you are gay”,   damaging physical integrity, e.g. “give me money or I will kill you”,   depriving of or damaging property, e.g. “hire me or i will set fire to your car”,   humiliation, e.g. “give me your pocket money or i will clean the bathroom floor with your clothes.”       

     Blackmails  402 C contextual sub-models are therefore classified by the type of threat in case of not fulfilling the blackmailer&#39;s request. Referring to  FIG.  12   , Blackmails  402 C contextual sub-models appear in two versions which is mapped into the output categorization. Each sub-model can be detected as punishable or non-punishable—punishable blackmail and extortion acts typically use threats of criminal prosecution in accordance with the American law. This classification can be adjusted to any other law system if needed. It is worth noting that due to the threat-related classification, Blackmails  402 C shares the system&#39;s assets (dictionaries, libraries) with Threats  402 B. 
     Sexual Harassment  402 D detects any form of sexual harassment towards an interlocutor, including (but not limited to):
         proposing sexual-oriented activities, e.g. “i want you to give me a blowjob”,   sharing sexual information/experiences, e.g. “i masturbate thinking of you”,   asking inappropriate sexual-oriented questions, e.g. “what is the size of your boobs?”,   sexually-oriented threats, e.g. “i will rape you until you bleed”,   making inappropriate comments, e.g. “your bulge is too big for my mouth.”       

     Sexual/Child Exploitation  402 E detects any behaviors/activities aiming at exploiting an interlocutor sexually, especially targeted against children.  FIG.  12    illustrates the division of Sexual/Child Exploitation  402 E into contextual sub-models. Five contextual sub-models are disclosed (there are more):
         child grooming, e.g. “there is nothing wrong with hugging an older guy like me”,   nudity extortion, e.g. “can you show me your picture of what is under your skirt?”,   persuading sexual activity, e.g. “let us meet, i will teach you how to satisfy men”,   intimate question, e.g. “have you had your period yet?”,   isolation, e.g. “why would you need your immature schoolmates when you have an adult friend like me?”       

     Sexual/Child Exploitation  402 E contextual sub-models are therefore classified by the type of behavior/activity. It allows to design a relevant reaction for each classification, e.g. informing a child that it is completely normal to talk to the parents about everything if an attempt to isolation is detected. It is worth noting that due to the sex-related phenomena, Sexual/Child Exploitation  402 E shares the system&#39;s assets (dictionaries, libraries) with Sexual Harassment  402 D and Sex Trafficking  402 H. 
     Persuading Suicide/Self-harm  402 F detects coaxing, urging, persuading and encouraging of an interlocutor to commit suicide or to harm oneself, including (but not limited to):
         direct coaxing, e.g. “you should kill yourself”,   conditional coaxing, e.g. “if i were you, i would jump off the bridge”,   lowering self-esteem, e.g. “your life is worthless”,   rationalizing, e.g. “binging and purging are fine, everyone does that”,   presenting self-harm as something good, e.g. “you should try slitting your wrists, it is such a relief”       

     Persuading Violence  402 G is analogous to Persuading Suicide/Self-harm  402 F and detects coaxing, urging, persuading and encouraging an interlocutor to use violence (either verbal or physical) towards other people or animals, including (but not limited to):
         direct coaxing, e.g. “we should smash his face”,   conditional coaxing, e.g. “if i were you, i would kick his ass”,   lowering self-esteem, e.g. “i can not believe you allow him to talk this shit about you”,   rationalizing, e.g. “that is fine, he even does not deserve to live”,   presenting violence as something good, e.g. “nothing will make you feel better than beating this piece of shit.”       

     Sex Trafficking  402 H detects any forms of baiting both minors and adults to be forcefully involved in commercial sex acts through a detailed analysis of requests, threats and offers, including (but not limited to):
         suspicious job offers, e.g. “i am looking for a young, female model for an international photoshoot in egypt”,   suspicious matrimonial offers, e.g. “a 24-year-old man looking for an under-20-girl, willing to take you on a long vacation”,   cross-country dating, e.g. “i feel as if i have known you my entire life, come visit me here in dubai, i promise we will have a great time together”,   luring for a better life, e.g. “life here is so much better, we have everything that you could dream of. just come, i will help you with everything”,   international sponsorship, e.g. “let us just spend some time together, no strings attached, and i will get you something pretty in return.”       

     Other Second Person Violence  402 I stands for any new contextual sub-model that can be defined and build in order to detect another well-defined online violence phenomenon. The system does not set up any limitation in regard to this matter. 
     Transformations To Second Person (TTSP)  404  comprises the method for using Violence Towards Second Person Detection (VTSPD)  402  in order to online violence targeted against any other individual or group (referenced as “new violence object” in this description). In general, the method consists of: 
     1. Removing/hiding the second person references (e.g. pronouns—“you”, “your”, “yourself”) in order to prevent repeat detections or not mislead VRSPD in other way. An example approach is to replace the second person references with responding form of the word “someone”, e.g. “you” with “someone”, “your” with “someone&#39;s.”
 
2. Replacing references to the new violence object with responding form of second person references (e.g. pronouns—“you”, “your”, “yourself”) in order to use exactly the same contextual models to detect online violence towards the new violence objects. This step requires to preserve and pass through the original text and the information which new violence object was transformed.
 
       FIG.  13    is a block diagram providing detailed division of Transformations To Second Person (TTSP)  404  into a set of well-defined transformations of certain semantic/syntactic references to the new violence objects. The output of Transformations To Second Person (TTSP)  404  is the input to the next pass of Violence Towards Second Person Detection (VTSPD)  402 . There is no limit of subsequent passes. Every transformation should be followed by a separate pass of Violence Towards Second Person Detection (VTSPD)  402  in order to not confuse the detection process and not lose the ability to precisely say which individual or group is the target of online violence detection. 
     Personal Pronouns  404 A is the basic transformation replacing third person personal pronouns (“he”, “she”, “they”, “his”, “himself”, and so on) with responding second person personal pronouns (“you”, “your”, and so on). It is very important to note that this transformation is applied after Coreference Resolution  306 E from Transformation  306  in Text Preparation  112  module described in details in the previous section. The usage of Coreference Resolution  306 E greatly reduces the chance of replacing unwanted pronouns as—at this point—part of the pronouns is already replaced with responding entities. The term “unwanted pronouns” refers to the situation that should not be detected as violence, e.g. expressing opinions about unspecified group of people such as “some people even do not realize they are idiots.” Part of such situations are resolved by Coreference Resolution  306 E, e.g. the above-mentioned sentence would be changed into “some people even do not realize some people are idiots.” Additionally, the transformation has a built-in method for verifying if a given third person personal pronoun should be ignored as it refers to unspecified individual or group. 
     Named Entities  404 B replaces named entities that can be objects of online violence with responding second person references. This transformation can be further divided into various functional groups. The first and the most obvious group consists of persons identified by names (full, first names, last names, etc.). The transformation allows to treat differently private persons and public figures such as politicians, celebrities, and so on. The user of the system (e.g. client using the API) is provided with the information against whom online violence was used and can decide what to do with this information on one&#39;s own. The other groups consist of members of named groups or named groups themselves. This covers the combination named person/groups and membership indicator defined by common nouns, e.g. “Trump supporters” or “fans of SF Giants.” This submodule has an access to the system&#39;s assets, including dictionaries and statistical classifiers. According to an embodiment, there are built-in methods for performing Named Entity Recognition (NER) combining both statistical and symbolic processing. Other embodiments allow to use external NER tools, e.g. accessed through an API. 
     Minority Terms  404 C replaces terms related to various minorities with responding second person references. This transformation can be further divided into various functional groups, including (but not limited to): people of given sexual orientation, ethnicity, nationality, religion, supporters of given political party, people representing specific views, people practicing specific profession. In an embodiment, the minority terms are detected and replaced using dictionary-based methods. Other embodiments allow to use dedicated statistical classifiers in order to perform this task. 
     Other Group/Individual Terms  404 D is very similar to Minority Terms  404 C, but covers the replacements of different terms, mainly related to groups that are not considered as minorities in the Western world, including (but not limited to): people representing certain nations or religions (e.g. Americans, Christians). Other Group/Individual Terms  404 D and Minority Terms  404 C use the same methods of detection and replacement, according to an embodiment. Although, Other Group/Individual Terms  404 D is further constrained by additional context-based rules in order to avoid over-reacting and not violate the freedom of speech in any way. 
     Other Transformation  404 E stands for any new transformation that can be defined in order to include another well-defined individual or group into the standard process of online violence detection. The system does not set up any limitation in regard to this matter. 
     Other Violence Detection (OVD)  406  resembles the same methods for detecting online violence as Violence Towards Second Person Detection (VTSPD)  402 , but functionally covers every other aspect of online violence other than violence targeted against an interlocutor, or any other individual and group, but only if the violence resembles violence targeted against second person. Therefore, it is very important to note that an overall violence detection regarding certain group of people (e.g. Jews) is usually split between two submodules: 
     1. Violence Towards Second Person Detection (VTSPD)  402  through the usage of Transformations To Second Person (TTSP)  404 , which covers phenomena such as personal attacks or threats against certain group (e.g. Jews). 
     2. Other Violence Detection (OVD)  406 , which covers other phenomena, such as the Holocaust denial and praising Hitler/Nazism in case of detecting violence towards Jews. 
       FIG.  14    is a block diagram providing detailed division of Other Violence Detection (OVD)  406  into a set of contextual models related to (and detecting) certain types of online violence. Similarly to Violence Towards Second Person Detection (VTSPD)  402 , Other Violence Detection (OVD)  406  utilizes a set of independent contextual models. According to an embodiment, the OVD&#39;s contextual models are built using the same methodology and having an access to the system&#39;s assets such as dictionaries and libraries, exactly as the contextual models of VTSPD. 
     Sexting/Sex-related Content  406 A detects if a given text contains any sex-related expressions. In comparison to sex-related contextual models from Violence Towards Second Person Detection (VTSPD)  402  (Sexual Harassment  402 D, Sexual/Child Exploitation  402 E, Sex Trafficking  402 H), Sexting/Sex-related Content  406 A can be seen as the broadest sex-related model as the detection does not require the violence object, only the sex-related content. Although, VTSPD modules can still detect phenomena that are not (and should not be) detected by Sexting/Sex-related Content  406 A. In this manner, all those models are complementary. The main application of Sexting/Sex-related Content  406 A is to protect children from the content that they should not be presented with. According to an embodiment, this contextual model almost exclusively utilizes statistical classification. In this case, the governance of the symbolic part is boiled down to launch the statistical classifiers and to verify the symbolic exceptions that may exclude a text from detection due to some very specific conditions (e.g. detecting school materials about sex education if they are allowed). 
     Antisemitism  406 B detects hostility to, prejudice, or discrimination against Jews. As mentioned, it focuses solely on phenomena not covered by the VTSPD&#39;s contextual models after specific transformation. Antisemitism  406 B can be divided into contextual sub-models representing well-defined phenomena, including (but not limited to):
         stereotypes/opinions that are not abusive if related to ordinary second person, but become abusive if related to Jews, e.g. “jews support only other jews”,   praising or justifying Hitler/Nazism, e.g. “hitler would be a perfect leader for our nation”,   antisemitic slogans and taglines, e.g. “warm up the gas chambers”,   abusive antisemitic terms, e.g. “kike”,   Holocaust denials/lies, e.g. “there is no proof that holocaust has ever happened.”       

     Self-harm Declaration  406 C detects declarations/intentions of the person who wrote the message to harm him- or herself, including committing suicide. This contextual model does not have its supplement VTSPD counterpart as it does not detect online violence targeted towards any other individual or group. According to an embodiment, it is built using the same methodology as other contextual models. The model consists of contextual sub-models related to various form of declarations/intentions, including (but not limited to):
         explicit declarations, e.g. “i am going to kill myself”,   implicit intentions, e.g. “my life does not make any sense to me anymore”,   self-convincing/justifying, e.g. “if i die, no one would even care”,   dangerous considerations, e.g. “maybe the better choice would be jumping off the bridge?”       

     X-ism/Y-phobia  406 D detects phenomena such as sexism, racism, ageism, ableism, homophobia, and any other hostility to, prejudice, or discrimination against minorities. Similarly to Antisemitism  406 B, X-ism/Y-phobia  406 D covers all of such phenomena not covered by the VTSPD&#39;s contextual models after specific transformation, including (but not limited to):
         stereotypes/opinions that are not abusive if related to ordinary second person, but become abusive if related to certain minority, e.g. “all muslims are terrorists”,   spreading harmful conspiracy theories, e.g. “gays control the mainstream media in my country”,   X-ist/Y-phobic slogans and taglines, e.g. “gay as aids”,   X-ist/Y-phobic terms, e.g. “faggot”,   X-ist/Y-phobic jokes, e.g. “q: why did the polack put ice in his condom? a: to keep the swelling down.”       

     Other Violence  406 E stands for any new contextual sub-model that can be defined and build in order to detect another well-defined online violence phenomenon. The system does not set up any limitation in regard to this matter. 
     Contextual Models 
     Contextual models are the foundation of the system and method. A contextual model detects a single well-defined type of online violence or a single well-defined online violence phenomenon. However, this division is completely conventional. It is assumed that type of online violence is a superordinate category detected by contextual models (e.g. Sexual/Child Exploitation  402 E), whereas online violence phenomenon is a subordinate category detected by contextual sub-models (e.g. nudity extortion). Under this assumption, contextual sub-models detecting online violence phenomena are grouped together into superordinate contextual models detecting types of online violence. For reasons of convenience, in this section both contextual models and contextual sub-models are referred as contextual models because the above-mentioned division is only the result of introducing the auxiliary hierarchy. 
     The contextual model is an implementation of a dedicated algorithm designed to: 
     a) process an input text preprocessed with Text Preparation  112  module in order to 
     b) detect a selected well-defined online violence phenomenon. 
     According to an embodiment, contextual models are built using the architecture and methodology disclosed in this document. 
       FIG.  15    is a block diagram of general architecture of online violence detection modules according to an embodiment. Every contextual model utilizes the presented architecture. Each contextual model takes in a Text Preparation Output  308  as an input, and returns Detection Output  570  as an output of the processing. According to an embodiment, Text Preparation Output  308  and Detection Output  570  are the same array/object with the fields/variables (related to the result) fulfilled after the detection process. In an embodiment, the output array/object is created in the initial module and then it passed through each module, and each module adds new information (fulfills certain fields/variables) related to the process of the module. In an embodiment, the output array/object utilizes the following form (fields/variables with responding type in parenthesis):
         “text_orig” (string),   “text” (string),   “segmentate” (array of strings),   “parse” (array of arrays),   “action” (array or arrays),   “result” (array of arrays).       

     The fields “text_orig” and “text” are fulfilled in Text Preparation  112  and they refer to the original input text and the text modified within the module, respectively. The fields “segmentate” and “parse” are related to syntactic parsing, and are fulfilled in various stages of Text Preparation  112  (“parse” can be fulfilled in Detection Engine  114 ). The field “action” is an additional field that stores the information regarding particular text manipulations performed in Text Preparation  112  module. Exemplary entry in “action” field: “[”133t speak“]=&gt;array(1) {[0]=&gt;string(14) “idi0t=&gt;idiot” }” refers to the correction of word “idi0t” to “idiot” using Leet Speak  304 G from Correction  304  module. The field “result” remains empty in Text Preparation  112  and is fulfilled in Detection Engine  114  by the process of contextual models. Exemplary entry in “result” filed: “array(1) {[0]=&gt;array(3) {[0]=&gt;string(4) “high” [1]=&gt;string(10) “#peratt001” [2]=&gt;string(9) #id000001″}}” refers to the processing of the sentence “You are an idi0t.” According to an embodiment the above-mentioned example of field “result” contains:
         “[0]=&gt;string(4) “high” refers to the level of violence (“high”, “medium”, “low”),   “[1]=&gt;string(10) “#peratt001″” refers to the internal violence category, here “#peratt001” is a token referring to direct abuses within Personal Attacks  402 A,   “[2]=&gt;string(9) #id000001″” refers to the specific rule responsible for the detection, here “#id000001” is a token referring to the rule finding abuse assignment to second person using linking verbs.       

     It is important to note that according to an embodiment, each detection can be easily tracked down to the single rule responsible for the detection, using the tokens referring to particular contextual models and to particular rule within this model. Furthermore, every action performed by Text Preparation  112  module is stored and available for the analysis or development purpose. Therefore, contrary to prior art blackbox approaches based on machine/deep learning, the system and method propose a largely explainable and trackable decision-making process. 
     Referring to  FIG.  15   , the main block is Symbolic Processing  510 , responsible for:
         executing symbolic algorithms, methods and components, including detection rules, and   governing the whole detection process, including requesting other system&#39;s components and gathering data from them,   fulfilling the result-related fields/variables in Detection Output  570 .       

     Symbolic Processing  510  is described in details later in this document. 
     Syntactic Parser  520  is responsible for providing information regarding syntactic analysis to the system. The decent set of information delivered by Syntactic Parser  520  consists of the division into words, phrases and clauses and the syntactic relations between them, including dependencies (e.g. phrase A is subject to phrase B) and inclusions (e.g. phrase A contains words X, Y and Z). According to an embodiment, Language Decoder (as described in U.S. Pat. No. 9,152,623 issued Oct. 6, 2015 and owned by Applicants) is used as a syntactic parser providing optimal amount of information as well as high accuracy for processing user-generated content. Other embodiments allow to use different syntactic parsers, including various implementations of dependency or phrase structure grammar parsing. Although, the complement of lacking information may be required in any form (e.g. a syntactic parser&#39;s wrapper or a set of component rules). The output of Syntactic Parser  520  can be utilized directly with the use of a general purpose programming language or using a dedicated query language. The latter is recommended as it greatly eases the process of building rules using parser&#39;s output. According to an embodiment, both the component rules and the detection rules are built using a dedicated query language designed to work with Language Decoder. Other embodiments allow to use other methods for building rules. Using other syntactic parser may require to use a different query language or to adjust the one designed to work with Language Decoder to work with another parser. If a syntactic parser provides additional information such as grammatical tense (e.g. present simple), mood (e.g. imperative) or word&#39;s base forms (e.g. “hate” as a base form of “hating”), it can be a great aid for the process of building rules. According to an embodiment, Language Decoder provides such information. Other embodiments may require to build dedicated rules for establishing grammatical tense, mood or word&#39;s base form. 
       FIG.  17    is a diagram illustrating syntactic parser (Language Decoder) output according to an embodiment.  502 A shows a visualization of simplified output of Language Decoder for the input text “Send me more nudes or I&#39;ll publish the ones I already have.” One can see the division into words, phrases and clauses as well as the relations between phrases. The relation types are presented with labels for clauses and block colors for phrase. Such visualization can be useful for getting the general impressions in regard to the syntactic decomposition.  502 B shows a part of a dump of the actual Language Decoder output according to an embodiment. The disclosed part provides the complete information about the first word (token) for the same input text as in  502 A. One can see that aside from the information regarding divisions and relations, there is also an information regarding:
         original writing (“word_orig_lex”),   base form of words (“phrase_core_base”),   grammatical tense (“clause_tense”),   grammatical mood (“clause_constructions),   Named Entity Recognition (“ner_id”, “ner_type”, “ner_content”),   additional semantic information about phrases (“phrase_aspect” and “phrase_category”).       

     Statistical Classifiers  530  consist of a set of dedicates statistical classifiers utilized mainly in the detection process, but also within Text Preparation  112  (e.g. Named Entity Recognition). The statistical classifiers mostly comprise machine/deep learning models trained on labeled data in order to preform the assumed classification. However, any other approach is accepted as long as it realizes the assumed functionality. There can be more than one classifier realizing exactly the same goal. An engineer can select which one should be used in certain contextual models, or even can use all of them and set conditions for selecting the best one under given circumstances. These instructions are coded in the symbolic rules. Statistical Classifiers  530  are organized in a form of library available to Symbolic Processing  510 . The symbolic rule can assign a part of the input text, send it to the selected classifier as a request, and gets back the classifier&#39;s output as a response (e.g. a category and confidence score). Therefore, Statistical Classifiers  530  do not need to be physically on the same device as the rest of the system. According to an embodiment Statistical Classifiers  530  are installed on the same device in order to minimize the communication overhead. According to other embodiment, Symbolic Processing  510  communicates with Statistical Classifiers  530  through the Internet via Application Programming Interface (API). The same rule applies to Syntactic Parser  520 , and potentially all the other components disclosed on  FIG.  15   . It is also possible to scatter the Statistical Classifiers  530 , having part of them internally on the same device, and another part on an external server. Furthermore, it is possible to use completely external services providing the statistical classifiers. 
     According to an embodiment, each of Statistical Classifiers  530  takes in an input text (can be only a part of the whole text from Text Preparation Output  308 ) and returns a set of detected categories (can be a single category for a binary classification) along with responding confidence score (from 0 to 1). Threshold for the confidence scores is set within Symbolic Processing  510 . Statistical Classifiers  530  are organized in a form of library and can be described with the training- and performance-related data, e.g. what was the training dataset (size, source), what architecture/learning algorithm was used, what is the performance (average processing time, precision, recall, F1-score). This data allows an engineer to select the most suitable classifier for the given purpose. In order to give the sense of the functionality range of Statistical Classifiers  530 , one can consider the following examples: 
     1. A text-level binary classification for detecting sexting/sex-related content: As mentioned in the description of Sexting/Sex-related Content  406 A, the statistical classification plays the dominant role in this contextual model. The classifier takes in the whole input text and returns a confidence score (range from 0 to 1), describing the chances for the input text to contain any sex-related content (1 stands for very high confidence).
 
2. A clause-level binary classification for detecting negative consequences of a blackmail: In this case, the detection rules from Symbolic Processing  510  assign a clause or a combination of clauses (obtained from Syntactic Parser  520  output) that potentially describes the consequences of not fulfilling a request. This potential consequence is then verified whether it is negative or not.
 
3. A phrase-level multi-category classification for detecting abusive phrases/expressions: The detection rules from Symbolic Processing  510  assign a phrase or a combination of phrases and/or clauses (obtained from Syntactic Parser  520  output) that is assigned to an interlocutor (e.g. using linking verbs). The classifier&#39;s goal is to classify whether the phrase(s) falls into one or more of the predefined categories related to interlocutor&#39;s attribute (appearance, intelligence, knowledge, experience, and so on).
 
     Rule Libraries  540  contain the rules utilized and executed in Symbolic Processing  510 . The rules can utilize Syntactic Parser  520 , but do not need to. According to an embodiment, there are two major types of the rules:
         detection rules are the rules detecting the smallest subpart of the given phenomenon, such as detecting one of many ways how one person can threat another person, e.g. using a second person reference as a subject, and expressing the future using any future tense or “be going to” types of constructions; therefore, executing a detection rule can tell if the input text contains this very specific subpart of online violence or not,   component rules consist of all the other rules realizing specific sub-tasks (even non-obvious) related to the detection of the given phenomenon, but not the phenomenon itself; component rules are subparts of detection rules and can be seen as building blocks; in other words, they can be used to compose the detection rule from smaller parts realizing certain well-defined tasks; some examples of component rules: detecting requests, greetings, recommendations, wishes, regrets, second person indications, intents, future expressions, and so on; other component rules can be seen as wrappers responsible for communication with Statistical Classifiers  530 , including assigning specific parts of the input texts, sending them to selected classifier, and getting back the results.       

     Dictionaries  550  are lexical databases storing words, phrases, and other expressions used in the detection process. Functionally, they are used to supplement the rules form Rule Libraries  540 , as the rules focus on grammar constructions and relations between entities, whereas Dictionaries  550  provide the vocabulary. Therefore, the detection rules are usually built from the component rules supplemented by the expressions from Dictionaries  550  and the statistical classifications from Statistical Classifiers  530 . Dictionaries  550  are not limited to single words. It is possible to store the whole expressions. Although, for reasons of convenience of further development, in some cases it is better to split the expression into predicates and objects, and let the rules combine them together. Some examples of dictionaries showing the range of potential applications:
         verbs (including phrasal verbs) related to revealing an information,   verbs and verb phrases related to harming a person physically/verbally with extended categorization, including the level of harmfulness (to hurt, to damage, to kill),   nouns and noun phrases related to male/female genitals with extended categorization, including the abusiveness of the expression,   sexual activities: from single words to complex expressions, stored in a minimal form and allowed to be completed and/or fulfilled with other words/expressions by the component rules, e.g. an entry “verb: suck+noun: male_genitals” can be completed with nouns/noun phrases related to male genitals and fulfilled with descriptors allowed to describe the given nouns/noun phrases. According to an embodiment, Dictionaries  550  are stored in internal databases. Although, they can be built using external sources. Other embodiments can utilize external databases such as WordNet, OpenCyc or DBpedia. According to an embodiment, dictionary entries are stored only in a base form. If required, every kind of inflection, such as conjugation or declination, is applied in Symbolic Processing  510  according to the rule requirements. Other embodiments can utilize other methods of storing entries in Dictionaries  550 .       

     Unsupervised Learning Algorithms (ULA)  560  is a collective name for additional models used for extending dictionaries and rules without any need for labeling data. Referring to  FIG.  15   , there are two instances of Unsupervised Learning Algorithms (ULA)  560 . The left-hand instance affects Dictionaries  550  and will be referred as Dictionary ULA. The right-hand instance affects Symbolic Processing  510  and will be referred as Rule ULA. Both Dictionary ULA and Rule ULA utilize Unlabeled Data, which refers to any normal data set or data stream without human labeling. Although, in some cases pre-trained models can be used (e.g. a pre-trained vector representation of words) and there is no need for using any additional data. In these cases, the process of learning from unlabeled data was just performed before, possibly by someone else. Each of Unsupervised Learning Algorithms (ULA)  560  can be applied in semi-supervised way in order to decrease the number of rules or dictionary entries causing the drop in precision or recall. For example, ULA generated 18 new entries to a given dictionary: 11 correct, 6 neutral (not affecting precision/recall) and 1 incorrect (causing a small drop in precision). These entries can be just added to the dictionary (unsupervised process) or can be verified by a trained person in order to exclude the incorrect entry (semi-supervised process). 
     Dictionary ULA extends the existing dictionaries with new entries. The already existing dictionary entries are used as primers/seeds in this process. It means that these entries comprise the starting set of words/phrases and the algorithms try to find different words/phrases that can play a similar role within similar context. The main method consists of using word embedding (vector representations of words). If needed, the approach can be supplemented with the usage of lexical databases and thesauruses (e.g. WordNet). In that case, there is a two-step process of: 
     a) extending the list of primers/seeds with synonyms from selected lexical database, 
     b) extending the list of primers/seeds/their synonyms with the algorithms utilizing vector representations. 
     Alternative methods can be applied as long as they increase precision/recall and the consequential drop in the second parameter is tolerable. The tolerance threshold can be set in a form of exchange par, e.g. 2 new true positives for 1 new false positive can be set as a minimal tolerance threshold, and verified on testing data set. An example of alternative method of Dictionary ULA is to use predefined grammar patterns (e.g. predefined list of subjects+linking verb+arbitrary complement) utilizing the output of Syntactic Parser  520  in order to create a list of extracted complements based on the processing of unlabeled data. The list of extracted complements can be therefore used to extend the pool of abusive terms or to improve Correction  304  by finding similarly written but still misspelled entries. 
     Rule ULA extends mainly the detection rules, and the component rules to some extent. It resembles the same idea—to use the existing rules as primers/seeds and to generate new rules that realize the same functionality. It is important to note that the new rules have to represent the same method of expression as primers/seeds. It means that they need to be executable using the same method/engine. Instead of word embedding, a vector representation of syntactic trees delivered by Syntactic Parser  520  is used. A set of corresponding syntactic trees is generated for each of primer/seed rule. Then, the other trees are compared to the ones delivered from existing trees, using vector representation built on large corpora of unlabeled data. Once the similar trees are selected, the algorithms try to compose a new rule based on available elements, including component rules and dictionaries. Similarly to Dictionary ULA, alternative methods for generating new rules can be applied as long as they increase precision/recall and the consequential drop in the second parameter is tolerable. 
       FIG.  16    is a block diagram providing detailed division of Symbolic Processing  510  into a set of subsequent processing steps according to an embodiment. Each of the following steps has the full access to all of the components (Syntactic Parser  520 , Statistical Classifiers  530 , and so on). However, certain steps are strongly related to certain components which will be emphasized in the following descriptions. 
     Preliminary Filtering  501  is the process of finding the input texts that have no chance of being detected by given contextual model. These texts are excluded from the detection process of given contextual model, which drastically reduce the average processing time, especially in case of rare phenomena detection. The main assumption is that there is a set of features that allows to decide with a very high confidence if a given text has any chance for being detected or not. As the input text is already corrected, in most cases Preliminary Filtering  501  relies on a verification of a list of essential keywords, or a set of keywords in proper combination and order, e.g. one of filters may require 3 words in any form in a given combination where it does not matter what is between these words. Therefore, Preliminary Filtering  501  utilizes any fast and reliable method for performing such search, e.g. regular expressions. According to an embodiment, every contextual model has a built-in function that builds up the preliminary regular expression filters automatically, based on Rule Libraries  540  and Dictionaries  550  used in the detection process. The automatically generated filters can be then modified by human engineers if needed. Some of online violence phenomena are very rare. If there is a 1% of a phenomenon in normal datasets, cutting off 95% of texts that do not have any chance for being detected is a huge profit. 
     Syntactic Parsing  502  is the process of using Syntactic Parser  520  in order to provide the detection rules with syntactic analysis. Only the input texts that passed through Preliminary Filtering  501  can be parsed. Furthermore, not every rule requires Syntactic Parsing  502 . The input text is segmented at this point so each sentence can be parsed separately. Additionally, Preliminary Filtering  501  can filter only sentences, not the whole texts. It can be applied for the contextual models (or certain rules) that require only the sentence-based information to operate. It is also possible to filter the sentence and the whole text in order to parse the sentence and use the whole text differently, without parsing. According to an embodiment, the output of Syntactic Parsing  502  is stored in the system&#39;s output array/object. Therefore, Syntactic Parsing  502  is performed at most once for each sentence of the input text. Once the sentence is parsed in one contextual model, the information is stored in the array/object and passed through to the rest of models. Every contextual model verifies if it has all of the required information and run Syntactic Parsing  502  only if it is necessary. 
     Detection Rules Composition  503  is the process of combining together the component rules and the entries from the dictionaries in order to form the executable detection rules. The detection rules are build and stored in the most convenient way for their further development and maintenance. Therefore, at this point their clarity and the ability to quickly adjust or modify them is more important than the processing speed. The detection rules are coded using variables representing building blocks, e.g. request+sexual activity. Detection Rules Composition  503  is the process of collecting the required data from all of the system&#39;s components and to substitute the conceptual elements of the detection rules with their executable counterparts. It covers the process of generating the required forms of words, phrases and expressions stored in Dictionaries  550  in their base form. This process utilizes the built-in dedicated algorithms. For purpose of optimization, the executable detection rules are stored in Rule Libraries  540 . If any element of the detection rule is changed, the new executable rule is generated. In contrast to development version of the system, the production version of the system requires only the executable detection rules. 
     Detection Rules Execution  504  is the process of executing the executable detection rules, delivered by Detection Rules Composition  503  or stored in Rule Libraries  540  if they were already processed. 
     This step largely overlaps with the two following steps: Dedicated Classifiers  505  and Contradiction Checking  506 . Functionally, Detection Rules Execution  504  verifies if the input text matches the executable detection rule. In that case, Detection Rules Execution  504  performs two additional steps: 
     1. Dedicated Classifiers  505 : If the detection rule requires the usage of Statistical Classifiers  530 , the selected part of the input texts is assigned as the classifier&#39;s input by Detection Rules Execution  504  using the guidelines from the detection rule. Detection Rules Execution  504  sends the request, gets the response and takes it into consideration executing the detection rules. At this point, Detection Rules Execution  504  has the complete set of information required to detect online violence.
 
2. Contradiction Checking  506 : If the input text is detected as containing online violence, the final step is performed. Detection Rules Execution  504  assigns the selected part of the input texts as the input of Contradiction Checking  506 . Usually, the input of Contradiction Checking  506  consists of the detected sentence and a couple of surrounding sentences. Although, sometimes the whole input text is used. Contradiction Checking  506  utilizes a special set of rules that verifies the broader context in order to find the indicators that the already detected text should not be treated as online violence. These indicators can be any form of:
         negation-related expression, “i don&#39;t think you are an idiot”,   counter-factual expressions, e.g. “at first, i thought you are an idiot, but i was totally wrong”,   quotation or indirect speech, e.g. “i can not believe he said you are an idiot”,   other expressions that do not fall into the above-mentioned categories.       

     Functionally, the rules of Contradiction Checking  506  can be built and stored exactly as the detection rules. They use the same system&#39;s assets: the component rules from Rule Libraries  540  and the entries from Dictionaries  550 . Therefore, they can be substituted by their executable counterparts in Detection Rules Composition  503  and stored in Rule Libraries  540 . According to an embodiment, they are separated from the detection rules due to the fact that the contradiction rules are independent from the detection rules and the same set of contradiction rules can be applied to different contextual models. Therefore, the detection rules and the contradiction rules can be developed independently by different teams of engineers. 
       FIGS.  18 - 19    are diagrams illustrating a process of Symbolic Processing  510  according to an embodiment. The two first steps—Preliminary Filtering  501  and Syntactic Parsing  502 —are skipped since they are not required to explain the process. One should assume that the input texts from both diagrams passed through Preliminary Filtering  501  and were successfully parsed using Syntactic Parsing  502 . Referring to  FIG.  18   , the input to Symbolic Processing  510  representing a contextual model of Personal Attacks  402 A is the text “i can not believe he said you are an idiot.” Detection Rules Composition  503  is presented as a process of combining three elements in order to detect a personal attack (detected elements are underlined):
         second_person_indication from Rule Libraries  540  is a component rule that allows to find second person references in text; functionally, it detects if a given phrase contains a second person reference; in the example “i can not believe he said you are an idiot”, a reference is detected as subject “you”,   assignment_relation from Rule Libraries  540  is another component rule that detects if there is an assignment relation between two other elements; assignment relation is a relation that assigns a feature or a set of features expressed within one element to the entity expressed in the second element; the most frequent case of assignment relation is using a linking verb that assigns the features from complement to the entity from subject; in the example “i can not believe he said you are an idiot”, an assignment relation is detected as predicate “are”,   abusive_expression from Dictionaries  550  is a set of expressions (words, phrases) that are considered to be offensive/abusive while used against other person; the expressions can be categorized with type of abuse or level of harmfulness; Detection Rules Composition  503  takes the full set from Dictionaries  550  and generates all required forms of the expressions using the dedicated built-in functions; the enriched set of expressions is then used to verify if a given phrase contains an abusive expression; in the example “i can not believe he said you are an idiot”, an abusive expression is detected as complement “an idiot.”       

     The executable rule composed in Detection Rules Composition  503  step is then executed in Detection Rules Execution  504  step. The personal attack is detected. Dedicated Classifiers  505  step is skipped since this particular contextual model does not require this step to operate. As a final step, Contradiction Checking  506  is performed. According to the example, a contradiction was detected (underlined) in a form of indirect speech and therefore the input text is eventually not classified as online violence, although it was detected with the executable rule. Symbolic Processing  510  returns “no violence” as a result of detection process. 
     Referring to  FIG.  19   , the input to Symbolic Processing  510  representing a contextual model of Blackmails  402 C is the text “send me more nudes or i will publish the ones i already have.” Detection Rules Composition  503  is presented as a process of combining three elements in order to detect a personal attack (detected elements are underlined):
         request from Rule Libraries  540  is a component rule that allows to find any grammar construction that can be used to express request; it can be simply an imperative or any of the following constructions: “you should/have to/had better . . . ”, “why do not you . . . ”, “if you do not . . . ”, and so on; the above list serves the illustrative purpose, it is neither exhaustive nor complete; in the example “send me more nudes or i will publish the ones i already have”, a request is detected as predicate (imperative) “send me more nudes”,   alternative from Rule Libraries  540  is another component rule that verifies if two other elements are connected with each other using a relation of alternative of not fulfilling a condition; therefore, one of the two connected elements comprises a condition (request), whereas the second comprises an implication (consequence); the alternative does not need to by explicitly present in text (e.g. “i will kill you if you do not give me money”) to be detected with this component rule; in the example “send me more nudes or i will publish the ones i already have”, an alternative is detected as connector “or”,   get_consequence from Rule Libraries  540  is an example of component rule that assigns part of an input text in order to perform statistical classification using Dedicated Classifiers  505 ; this particular rule assigns the implication (consequence) part of the alternative for the further processing (marked with angle brackets in  FIG.  19   ); in the example “send me more nudes or i will publish the ones i already have”, a consequence is assigned as “i will publish the ones i already have.”       

     During Detection Rules Execution  504  step, Dedicated Classifiers  505  sends the assigned part of text to Statistical Classifiers  530 , and gets back the response; here, the assigned text is classified as “reveal_information” with the confidence score of 0.99. Therefore, the blackmail is detected. Since Contradiction Checking  506  does not find any contradictions, Symbolic Processing  510  returns “blackmail, revealing information” as a result of detection process. 
     Customization 
     Due to the strict symbolic governance over the decision-making process and the clear modular architecture, the system and method demonstrates high customization capabilities. According to an embodiment, each detection rule, even if utilizes Statistical Classifiers  530 , can be identified by its unique ID number. Similarly, as mentioned in the Contextual Models section, every modification introduced by Text Preparation  112  module is also stored and passed through the whole online violence detection process. Therefore, an engineer developing the system can immediately determine why the system made certain decision. This feature greatly reduces time and effort required for development and maintenance of the system. 
     According to an embodiment, a system developer is provided with a development environment that allows to collect, visualize and analyze statistics of processed data in regard to data sources and detection rule IDs along with modifications introduced by Text Preparation  112  module. 
     Furthermore, the development environment is equipped with the tools to label data and the ability to use labeled datasets in order to control the system&#39;s accuracy, but also to see which rules are responsible for correct and incorrect detections. This ability comprises a great aid in planing the future development directions and adjusting the system to particular data sources. Additionally, the development environment is integrated with Syntactic Parser  520  which helps to track if an error is related to wrong parsing or not. In an embodiment, a system developer adjusts the system to given data source manually or semi-automatically using the data stored in the development environment. In other embodiments, the process of adjusting the system is fully automatized utilizing labeled datasets and supervised machine learning methods. 
     The process of adjusting the system to particular data sources or guidelines is called “onboarding process.” The information related to data sources and guidelines can be obtained automatically as mentioned above, or manually by referring to client&#39;s guidelines (if available) or interviewing/surveying client&#39;s employees. The goal of the interviews/surveys is to determine which online violence phenomena are allowed and which should be blocked within given service/community. The interviews/surveys can be performed by human (e.g. a system developer) or using predefined forms/applications (e.g. filling up a web-based online survey). The decisions regarding online violence phenomena are applied by switching on and off the detection rules related to these phenomena. This process can be performed manually, semi-automatically or automatically. The most common types of settings regarding the onboarding process can be predefined and delivered through specific API requests. According to an embodiment, a control over what an API returns in terms of the whole hierarchical output structure is provided within the API, using the API request features. Other embodiments require the deployment of a dedicated API after adjusting the system to certain data sources or guidelines. Additionally, Unsupervised Learning Algorithms (ULA)  560  described in the Contextual Models section can be used as a part of onboarding process in terms of adjusting the system to certain data sources. 
     The various functions or processes disclosed herein may be described as data and/or instructions embodied in various computer-readable media, in terms of their behavioral, register transfer, logic component, transistor, layout geometries, and/or other characteristics. Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and/or instructions through wireless, optical, or wired signaling media or any combination thereof. 
     Examples of transfers of such formatted data and/or instructions by carrier waves include, but are not limited to, transfers (uploads, downloads, e-mail, etc.) over the internet and/or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, etc.). When received within a computer system via one or more computer-readable media, such data and/or instruction-based expressions of components and/or processes under the system described may be processed by a processing entity (e.g., one or more processors) within the computer system in conjunction with execution of one or more other computer programs. 
     Aspects of the systems and methods described herein may be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (PLDs), such as field programmable gate arrays (FPGAs), programmable array logic (PAL) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits (ASICs). Some other possibilities for implementing aspects of the system include: microcontrollers with memory (such as electronically erasable programmable read only memory (EEPROM)), embedded microprocessors, firmware, software, etc. Furthermore, aspects of the system may be embodied in microprocessors having software-based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types. Of course the underlying device technologies may be provided in a variety of component types, e.g., metal-oxide semiconductor field-effect transistor (MOSFET) technologies like complementary metal-oxide semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, etc. 
     Unless the context clearly requires otherwise, throughout the description and the claims, the words “comprise,” “comprising,” and the like are to be construed in an inclusive sense as opposed to an exclusive or exhaustive sense; that is to say, in a sense of “including, but not limited to.” Words using the singular or plural number also include the plural or singular number respectively. Additionally, the words “herein,” “hereunder,” “above,” “below,” and words of similar import refer to this application as a whole and not to any particular portions of this application. When the word “or” is used in reference to a list of two or more items, that word covers all of the following interpretations of the word: any of the items in the list, all of the items in the list and any combination of the items in the list. 
     The above description of illustrated embodiments of the systems and methods is not intended to be exhaustive or to limit the systems and methods to the precise forms disclosed. While specific embodiments of, and examples for, the systems components and methods are described herein for illustrative purposes, various equivalent modifications are possible within the scope of the systems, components and methods, as those skilled in the relevant art will recognize. The teachings of the systems and methods provided herein can be applied to other processing systems and methods, not only for the systems and methods described above. 
     The elements and acts of the various embodiments described above can be combined to provide further embodiments. These and other changes can be made to the systems and methods in light of the above detailed description.