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Semantic engine

How Tropes operates

To process a text, Tropes operates in 6 stages:

sentences and propositions hashing,

ambiguity solving (regarding the words of the text),

identification of Equivalent classes,

statistics, detection of Bundles and Episodes,

detection of the Most characteristic parts of text,

layout and display of the result.

Results of first three stages are used by Zoom Semantic Search Engine.

Words are grouped together in several main Word categories. Among these, six are of interest to us:

verbs,

connectors (conjunctions, conjunctive phrases),

modalities (adverbs or adverbial phrases),

qualifying adjectives,

personal pronouns,

substantives and proper nouns.

To achieve an analysis, the software carries out a complex processing aiming at: assigning all the significant words to the above categories; analyzing their distribution into subcategories (Word categories, Equivalent classes, see below); examining their occurrence order, both within the propositions (Relations, Actants and Acted) and throughout the text (Distribution graph, Bundles, Episodes, Most characteristic parts of text, see below).

Propositional hashing

To simplify the analysis, Tropes divides the text into propositions (simple sentences). This first stage is based on a scrutiny of the punctuation, and on complex syntax analysis functions, which will not be detailed here. Thus, you obtain co-occurrence statistics (Relations) of high reliability, since it is not possible for two words to fit into the same grammatical proposition if they are not closely connected. Propositional hashing is bound to involve errors (propositions that are either too short or too long), but this does not alter the results.

Ambiguity solving

The automatic interpretation of words in any living language, either written or spoken, requires the solving of numerous ambiguities:

grammatical and syntactic (the word "pound", for example, corresponds to a noun in "a pound of fish", and to a verb in "I pound on the door"),

semantic (the word "pound", for example, can refer to a weight or to a currency). On average, one word out of four is deemed ambiguous.

One of the main functions of this software is to solve these ambiguities by means of several problem-solving algorithms. Though a perfect result is impossible to achieve, the error rate is low enough to guarantee an accurate analysis of your text.

Word categories

The verbs are either:

factive, i.e. expressing actions ("to do", "to work", "to walk", etc.)

stative, i.e. expressing states or concepts of possession ("to be", "to stay", etc.)

reflexive, i.e. expressing a statement about circumstances, beings, objects, etc. ("to think", "to believe", etc.)

or performative, i.e. expressing an act through the language and contained within the language ("I promise", "I demand", etc.)

Connectors (coordinating and subordinating conjunctions, conjunctive phrases) link together various parts of the discourse through concepts of:

condition ("if", "in as much as", "in case", etc.),

cause ("because", "in consequence of", "therefore", etc.),

goal ("so that", "in order that", etc.),

addition ("and", "moreover", "along with", etc.),

disjunction ("or", "either... or", "whether... or", etc.),

opposition ("but", "nevertheless", "however", etc.),

comparison ("as", "like", "as well as", etc.),

time ("when", "while", "since", etc.),

place ("whereby", "whereupon", etc.)

Personal pronouns are displayed in gender ("I", "You", "He", etc.) and in number ("They", "We", etc.) The middle/old English "Thou" form is also detected here.

Modalities (adverbs or adverbial phrases) enable the speaker to get involved in what he says, or to locate what he says in time and space, through concepts of:

time ("now", "yesterday", "tomorrow", etc.),

place ("there", "here" , "below", etc.),

manner ("directly", "together", etc.),

assertion ("absolutely", "certainly", etc.),

doubt ("maybe", "probably", etc.),

negation ("not", "never", "nothing", etc.),

intensity ("very", "much", "strongly", etc.)

Adjectives are either:

objective, i.e. enabling to characterize beings or objects, regardless of the speaker's standpoint (color adjectives, for example),

subjective, i.e. indicating judgment on something or on somebody, thus enabling to express the speaker's standpoint ("beautiful", "small", "nice", etc.),

or numeral, i.e. grouping together numbers (in letters or in figures), along with ordinal and cardinal adjectives.

Other word categories include pronouns, articles, prepositions and non-qualifying adjectives. Do not take these categories into account, because they are used only for ambiguity solving.

Use of Word categories

Broadly speaking, we can say that:

time and place connectors and modalities provide the means to locate the action,

intensity and negation modalities provide the means to dramatize the discourse,

cause and condition connectors provide the means to construct a chain of reasoning,

addition connectors provide the means to enumerate facts or characteristics,

opposition connectors more specifically provide the means to argue, to put things into perspective and to set out conflicting standpoints.

Statistical, probabilistic and cognitive analyses

Tropes carries out different sorts of text analyses:

statistics on the total occurrence frequency of the main Word categories and of their subcategories,

statistics on the co-occurrence and the connection rate of Equivalent classes and Word categories,

a probabilistic analysis of the words occurring in Bundles, and a geometric analysis of the Bundles delimiting the Episodes,

a Cognitive-Discursive Analysis (CDA), making it possible to detect the Most characteristic parts of text.

Among other things, statistics are used to build the graphs and to lay out the results.

The Frequent word categories and the Text Style are obtained by comparing the distribution of the occurrence frequency of the categories observed in the text with linguistic production norms. These norms have been elaborated after studying a great number of different texts. They are stored into specific in-built tables.

Equivalent classes and Relations between equivalents

The Equivalent classes group together closely related References (common nouns, proper nouns, trademarks) appearing frequently throughout the text. For example, "father" and "mother" are grouped together into the "family" class.

The Reference fields group together the words comprising the Equivalent classes in order to enable the software to elaborate a representation of the context. To achieve this, the Semantic equivalents dictionary of Tropes is composed of three different classification levels. At the lower level are the References, which are next merged more broadly into Reference fields 2, which, in turn, are merged into Reference fields 1.

In the example below, the word "Lord Chancellor" belongs to the "minister" Reference, included in the "government" field 2, which is part of the "politics" field 1. The "politics" field 1 includes broader concepts, such as "political system", "foreign policy", etc.

Fields 1	Fields 2	References	Words
Politics	Political system	Communism	Communism
Politics	Political system	Communism	Marxism
Politics	Political system	Democracy	Democracy
Politics	Political system	Democracy	Republic
Politics	Government	Federal government	Federal government
Politics	Government	Head of Government	Head of Government
Politics	Government	Head of Government	Prime Minister
Politics	Government	Minister	Lord Chancellor
Politics	Government	Minister	Minister
Politics	Government	Minister	Secretary of State
Politics	Government	Government	Government

Bundles and Episodes

Tropes carries out two different tools to study the chronology of a discourse. It is based on two notions, Bundles and Episodes:

a Bundle groups together word occurrences (belonging to an Equivalent class or to a Word category) that tend to appear in a remarkable density within a limited portion of the text (somewhere near the beginning, the middle or the end, but not on a regular pattern throughout the whole text).

an Episode corresponds to a part of the text in which a number of Bundles have been formed and completed. These are large blocks of arguments, quite representative of the structure of the studied discourse.

Most characteristic parts of text

The contraction of the text reveals the Most characteristic parts of text. These are "propositions introducing main themes or characters, expressing events that are essential to the progression of the story (causal attributions of consequences, results, aims)".

To extract these propositions, Tropes carries out a complex Cognitive-Discursive Analysis processing (CDA). To simplify matters, let us say that each proposition of the text is allotted a score, depending on its relative weight, its occurrence order and its argumentative role. The propositions are then sorted out according to their respective scores. To enable you to control the amount of displayed propositions, and to insure that the result obtained reflects the text analyzed, Tropes provides the means to adjust the contraction rate of the text.

Text Style

The software makes a diagnosis of the Text Style and of its Setting according to the statistical indicators retrieved during the analysis.

Here are the possible Styles:

Style	Explanation:
Argumentative	the speaker involves himself, argues, explains or analyzes in order to try to convince the interlocutor
Narrative	a narrator states a series of events, happening at a given time, and in a given place
Enunciative	the speaker and the interlocutor establish a mutual relation of influence, make their standpoints known
Descriptive	a narrator describes, identifies or classifies something or somebody