From 564ea3448c72d409d31204c210e6b9431e3399b1 Mon Sep 17 00:00:00 2001 From: Eric Ihli Date: Thu, 22 Jul 2021 19:42:01 -0500 Subject: [PATCH] Update README --- web/README_WGU.org | 64 ++- web/resources/public/README_WGU.htm | 648 +++++++++++++++++++--------- 2 files changed, 488 insertions(+), 224 deletions(-) diff --git a/web/README_WGU.org b/web/README_WGU.org index bf2fdd0..5e576be 100644 --- a/web/README_WGU.org +++ b/web/README_WGU.org @@ -809,7 +809,7 @@ With this precaution in place, attackers will not be able to snoop the content t By having the application server behind an HAProxy load balancer, we can take advantage of the built-in HAProxy stats page for monitoring amount of traffic and health of the application servers. -[[file:resources/public/images/stats.png]] +[[file:assets/images/stats.png]] http://darklimericks.com:8404/stats @@ -827,15 +827,15 @@ The first input field is for a word or phrase for which you wish to find a rhyme The first visualization is a scatter plot of rhyming words with the "quality" of the rhyme on the Y axis and the number of times that rhyming word/phrase occurrs in the training corpus on the X axis. -[[file:resources/public/images/wgu-vis.png]] +[[file:assets/images/wgu-vis.png]] The second visualization is a word cloud where the size of each word is based on the frequency with which the word appears in the training corpus. -[[file:resources/public/images/wgu-vis-cloud.png]] +[[file:assets/images/wgu-vis-cloud.png]] The third visualization is a table that lists all of the rhymes, their pronunciations, the rhyme quality, and the frequency. The table is sorted first by the rhyme quality then by the frequency. -[[file:resources/public/images/wgu-vis-table.png]] +[[file:assets/images/wgu-vis-table.png]] * D. Documentation :PROPERTIES: @@ -875,16 +875,61 @@ I wrote code to perform certain types of data analysis, but I didn't find it use For example, there is natural language processing code at [[https://github.com/eihli/prhyme/blob/master/src/com/owoga/prhyme/nlp/core.clj]] that parses a line into a grammar tree. I wrote several functions to manipulate and aggregate information about the grammar trees that compose the corpus. But I didn't use any of that information in creation of the n-gram Hidden Markov Model nor in the user display. For tasks related to brainstorming rhyming lyrics, that extra information lacked significant value. -** Assessment +** Assessment Of Hypothesis +I'll use an example output to subjectively assess the results of the project. + +Below are some of the lyrics suggested to rhyme with the word "technologies". + +| Rhyme | Quality | Lyric | Perplexity | +| technologies | 8 | you will tear the skin from the nuclear technologies | -0.04695091652785746 | +| pathologies | 7 | there's no hope for body's pathologies | -0.09800371561934312 | +| apologies | 7 | swimming in a grey world dying it's time for apologies | -0.14781111654643642 | +| chronologies | 7 | damn god damn the seed lurks in chronologies | -0.20912909334441387 | +| anomalies | 6 | yesterday was born i encounter the anomalies | -0.19578505194217627 | +| atrocities | 6 | there's no return and and the pimp your atrocities | -0.21516240668167685 | +| ideologies | 6 | entrenched ideologies | -0.27407234083849513 | +| monopolies | 6 | monopolies | -0.8472654185540912 | +| qualities | 5 | with such qualities | -0.0793752454750395 | +| policies | 5 | stop looking at insurance policies | -0.11580898408112054 | +| colonies | 5 | betwixt my heels, through the tears you collapse the colonies | -0.1610184959356118 | +| harmonies | 5 | broken harmonies | -0.18655087962492334 | +| prophecies | 5 | seek the truth prophecies | -0.24506696021938001 | +| festivities | 4 | you have touching the festivities | -0.09271388814221376 | +| delicacies | 4 | grey that consumes what it never was sun and the delicacies | -0.14553081854920977 | +| anybody's | 4 | your eyes, will remain violent the anybody's | -0.17560987263626957 | +| extremities | 4 | i am missing extremities | -0.30386279996641197 | +| casualties | 3 | feed the casualties | -0.23600199637494926 | + +Do these lyrics provide benefit to the brainstorming process? + +The lines "make sense" to varying degrees. + +The "pathologies" line, for example, contains a sensible 2-gram of "body's pathologies". The model has learned that the possessive form of "body" is a reasonable prefix to the word "pathologies". + +| pathologies | 7 | there's no hope for body's pathologies | -0.09800371561934312 | + +And the beginning of that line contains a phrase, "there's no hope", that fits perfectly with the genre/context of the training set (dark heavy metal). + +It's clear that the training worked. The output is relevant to the genre and grammatically reasonable. + +There's also a wide variety in the output, which is beneficial for +brainstorming. Suggestions range from clean and clear rhymes, like +"technologies" and "pathologies", to more abstract rhymes like "technologies" +and "anybody's", which some artists can creatively manipulate effectively. + +I assess this version of the product proves viable and there's exciting +possibilities for improvements by integrating with making suggestions that meet +certain stress patterns, preferring phrases that contain synonyms or antonyms, +and more. ** Visualizations -[[file:resources/public/images/rhyme-scatterplot.png]] +[[file:assets/images/rhyme-scatterplot.png]] -[[file:resources/public/images/wordcloud.png]] +[[file:assets/images/wordcloud.png]] -[[file:resources/public/images/rhyme-table.png]] +[[file:assets/images/rhyme-table.png]] ** Accuracy @@ -902,7 +947,7 @@ Using this technique on a (small) sample of 100 generated sentences reveals that This is just one of many possible assessment techniques we could use. It's simple but could be expanded to include valid phrases other than Treebank's clauses. For the purpose of having a measurement by which to compare changes to the algorithm, this suffices. -#+begin_src clojure :session main :eval no-export :results output +#+begin_src clojure :session main :eval no-export :results output :exports both (require '[com.darklimericks.linguistics.core :as linguistics] '[com.owoga.prhyme.nlp.core :as nlp]) @@ -923,6 +968,7 @@ This is just one of many possible assessment techniques we could use. It's simpl (/ (count valid-english) 100))) (println (average-valid-of-100-suggestions)) +;; 47/100 #+end_src #+RESULTS: diff --git a/web/resources/public/README_WGU.htm b/web/resources/public/README_WGU.htm index a14b6a7..322e519 100644 --- a/web/resources/public/README_WGU.htm +++ b/web/resources/public/README_WGU.htm @@ -3,7 +3,7 @@ "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"> - + RhymeStorm™ - WGU CSCI Capstone Project @@ -223,111 +223,111 @@

Table of Contents

-
-

1 WGU Evaluator Notes

+
+

1 WGU Evaluator Notes

Hello! I hope you enjoy your time with this evaluation! @@ -361,20 +361,20 @@ After I describe the steps to initialize a development environment, you’ll

-
-

2 Evaluation Technical Documentation

+
+

2 Evaluation Technical Documentation

It’s probably not necessary for you to replicate my development environment in order to evaluate this project. You can access the deployed application at https://darklimericks.com/wgu and the libraries and supporting code that I wrote for this project at https://github.com/eihli/clj-tightly-packed-trie, https://github.com/eihli/syllabify, and https://github.com/eihli/prhyme. The web server and web application is not hosted publicly but you will find it uploaded with my submission as a .tar archive.

-
-

2.1 How To Initialize Development Environment

+
+

2.1 How To Initialize Development Environment

-
-

2.1.1 Required Software

+
+

2.1.1 Required Software

  • Docker
    • @@ -384,8 +384,8 @@ It’s probably not necessary for you to replicate my development environmen
-
-

2.1.2 Steps

+
+

2.1.2 Steps

  1. Run ./db/run.sh && ./kv/run.sh to start the docker containers for the database and key-value store. @@ -399,12 +399,12 @@ It’s probably not necessary for you to replicate my development environmen
-
-

2.2 How To Run Software Locally

+
+

2.2 How To Run Software Locally

-
-

2.2.1 Requirements

+
+

2.2.1 Requirements

  • Java
    • @@ -413,8 +413,8 @@ It’s probably not necessary for you to replicate my development environmen
-
-

2.2.2 Steps

+
+

2.2.2 Steps

  1. Run ./db/run.sh && ./kv/run.sh to start the docker containers for the database and key-value store. @@ -435,8 +435,8 @@ It’s probably not necessary for you to replicate my development environmen
    -
    -

    3.1 Problem Summary

    +
    +

    3.1 Problem Summary

    Songwriters, artists, and record labels can save time and discover better lyrics with the help of a machine learning tool that supports their creative endeavours. @@ -448,8 +448,8 @@ Songwriters have several old-fashioned tools at their disposal including diction

    -
    -

    3.2 Benefits

    +
    +

    3.2 Benefits

    How many sensible phrases can you think of that rhyme with “war on poverty”? What if I say that there’s a restriction to only come up with phrases that are exactly 14 syllables? That’s a common restriction when a songwriter is trying to match the meter of a previous line. What if I add another restriction that there must be primary stress at certain spots in that 14 syllable phrase? @@ -465,8 +465,8 @@ And this is a process that is perfect for machine learning. Machine learning can

    -
    -

    3.3 Product - RhymeStorm™

    +
    +

    3.3 Product - RhymeStorm™

    RhymeStorm™ is a tool to help songwriters brainstorm. It provides lyrics automatically generated based on training data from existing songs while adhering to restrictions based on rhyme scheme, meter, genre, and more. @@ -494,8 +494,8 @@ This auto-complete functionality will be similar to the auto-complete that is co

    -
    -

    3.4 Data

    +
    +

    3.4 Data

    The initial model will be trained on the lyrics from http://darklyrics.com. This is a publicly available data set with minimal meta-data. Record labels will have more valuable datasets that will include meta-data along with lyrics, such as the date the song was popular, the number of radio plays of the song, the profit of the song/artist, etc… @@ -507,8 +507,8 @@ The software can be augmented with additional algorithms to account for the type

    -
    -

    3.5 Objectives

    +
    +

    3.5 Objectives

    This software will accomplish its primary objective if it makes its way into the daily toolkit of a handful of singers/songwriters. @@ -528,8 +528,8 @@ Another example is the package that turns phrases into phones (symbols of pronun

    -
    -

    3.6 Development Methodology - Agile

    +
    +

    3.6 Development Methodology - Agile

    This project will be developed with an iterative Agile methodology. Since a large part of data science and machine learning is exploration, this project will benefit from ongoing exploration in tandem with development. @@ -545,8 +545,8 @@ The prices quoted below are for an initial minimum-viable-product that will serv

    -
    -

    3.7 Costs

    +
    +

    3.7 Costs

    Funding requirements are minimal. The initial dataset is public and freely available. On a typical consumer laptop, Hidden Markov Models can be trained on fairly large datasets in short time and the training doesn’t require the use of expensive hardware like the GPUs used to train Deep Neural Networks. @@ -630,17 +630,17 @@ These are my estimates for the time and cost of different aspects of initial dev

    -
    -

    3.8 Stakeholder Impact

    +
    +

    3.8 Stakeholder Impact

    -The only stakeholders in the project will be the record labels or songwriters. I describe the only impact to them in the 3.2 section above. +The only stakeholders in the project will be the record labels or songwriters. I describe the only impact to them in the 3.2 section above.

    -
    -

    3.9 Ethical And Legal Considerations

    +
    +

    3.9 Ethical And Legal Considerations

    Web scraping, the method used to obtain the initial dataset from http://darklyrics.com, is protected given the ruling in https://en.wikipedia.org/wiki/HiQ_Labs_v._LinkedIn. @@ -652,8 +652,8 @@ The use of publicly available data in generative works is less clear. But Micros

    -
    -

    3.10 Expertise

    +
    +

    3.10 Expertise

    I have 10 years experience as a programmer and have worked extensively on both frontend technologies like HTML/JavaScript, backend technologies like Django, and building libraries/packages/frameworks. @@ -674,8 +674,8 @@ Write an executive summary directed to IT professionals that addresses each of t

    -
    -

    4.1 Decision Support Opportunity

    +
    +

    4.1 Decision Support Opportunity

    Songwriters expend a lot of time and effort finding the perfect rhyming word or phrase. RhymeStorm™ is going to amplify user’s creative abilities by searching its machine learning model for sensible and proven-successful words and phrases that meet the rhyme scheme and meter requirements requested by the user. @@ -687,8 +687,8 @@ When a songwriter needs to find likely phrases that rhyme with “war on pov

    -
    -

    4.2 Customer Needs And Product Description

    +
    +

    4.2 Customer Needs And Product Description

    Songwriters spend money on dictionaries, compilations of slang, thesauruses, and phrase dictionaries. They spend their time daydreaming, brainstorming, contemplating, and mixing and matching the knowledge they acquire through these traditional means. @@ -708,8 +708,8 @@ Computers can process and sort this information and sort the results by quality

    -
    -

    4.3 Existing Products

    +
    +

    4.3 Existing Products

    We’re all familiar with dictionaries, thesauruses, and their shortcomings. @@ -725,8 +725,8 @@ RhymeZone is limited in its capability. It doesn’t do well finding rhymes

    -
    -

    4.4 Available Data And Future Data Lifecycle

    +
    +

    4.4 Available Data And Future Data Lifecycle

    The initial dataset will be gathered by downloading lyrics from http://darklyrics.com and future models can be generated by downloading lyrics from other websites. Alternatively, data can be provided by record labels and combined with meta-data that the record label may have, such as how many radio plays each song gets and how much profit they make from each song. @@ -750,8 +750,8 @@ Each new model can be uploaded to the web server and users can select which mode

    -
    -

    4.5 Methodology - Agile

    +
    +

    4.5 Methodology - Agile

    RhymeStorm™ development will proceed with an iterative Agile methodology. It will be composed of several independent modules that can be worked on independently, in parallel, and iteratively. @@ -775,8 +775,8 @@ Much of data science is exploratory and taking an iterative Agile approach can t

    -
    -

    4.6 Deliverables

    +
    +

    4.6 Deliverables

    • Supporting libraries source code
      • @@ -810,8 +810,8 @@ The trained data model and web interface has been deployed at the following addr
    -
    -

    4.7 Implementation Plan And Anticipations

    +
    +

    4.7 Implementation Plan And Anticipations

    I’ll start by writing and releasing the supporting libraries and packages: Tries, Syllabification/Phonetics, Rhyming. @@ -831,8 +831,8 @@ In anticipation of user growth, I’ll be deploying the final product on Dig

    -
    -

    4.8 Requirements Validation And Verification

    +
    +

    4.8 Requirements Validation And Verification

    the methods for validating and verifying that the developed data product meets the requirements and subsequently the needs of the customers @@ -852,8 +852,8 @@ The final website will integrate multiple technologies and the integrations won&

    -
    -

    4.9 Programming Environments And Costs

    +
    +

    4.9 Programming Environments And Costs

    the programming environments and any related costs, as well as the human resources that are necessary to execute each phase in the development of the data product @@ -877,8 +877,8 @@ All code was written and all models were trained on a Lenovo T15G with an Intel

    -
    -

    4.10 Timeline And Milestones

    +
    +

    4.10 Timeline And Milestones

    @@ -956,16 +956,16 @@ RhymeStorm™ is an application to help singers and songwriters brainstorm new l

    -
    -

    5.1 Descriptive And Predictive Methods

    +
    +

    5.1 Descriptive And Predictive Methods

    -
    -

    5.1.1 Descriptive Method

    +
    +

    5.1.1 Descriptive Method

      -
    1. Most Common Grammatical Structures In A Set Of Lyrics
      +
    2. Most Common Grammatical Structures In A Set Of Lyrics

      By filtering songs by metrics such as popularity, number of awards, etc… we can use this software package to determine the most common grammatical phrase structure for different filtered categories. @@ -1042,12 +1042,12 @@ In the example below, you’ll see that a simple noun-phrase is the most pop

    -
    -

    5.1.2 Prescriptive Method

    +
    +

    5.1.2 Prescriptive Method

      -
    1. Most Likely Word To Follow A Given Phrase
      +
    2. Most Likely Word To Follow A Given Phrase

      To help songwriters think of new lyrics, we provide an API to receive a list of words that commonly follow/precede a given phrase. @@ -1143,8 +1143,8 @@ In the example below, we provide a seed suffix of “bother me” and as

    -
    -

    5.2 Datasets

    +
    +

    5.2 Datasets

    The dataset currently in use was generated from the publicly available lyrics at http://darklyrics.com. @@ -1156,12 +1156,12 @@ Further datasets will need to be provided by the end-user.

    -
    -

    5.3 Decision Support Functionality

    +
    +

    5.3 Decision Support Functionality

    -
    -

    5.3.1 Choosing Words For A Lyric Based On Markov Likelihood

    +
    +

    5.3.1 Choosing Words For A Lyric Based On Markov Likelihood

    Entire phrases can be generated using the previously mentioned functionality of generating lists of likely prefix/suffix words. @@ -1177,8 +1177,8 @@ The user can supply criteria such as restrictions on the number of syllables, nu

    -
    -

    5.3.2 Choosing Words To Complete A Lyric Based On Rhyme Quality

    +
    +

    5.3.2 Choosing Words To Complete A Lyric Based On Rhyme Quality

    Another part of the decision support functionality is filtering and ordering predicted words based on their rhyme quality. @@ -1404,8 +1404,8 @@ In the example below, you’ll see that the first 20 or so rhymes are perfec

    -
    -

    5.4 Featurizing, Parsing, Cleaning, And Wrangling Data

    +
    +

    5.4 Featurizing, Parsing, Cleaning, And Wrangling Data

    The data processing code is in https://github.com/eihli/prhyme @@ -1441,8 +1441,8 @@ words can be compared: “Foo” is the same as “foo”.

    -
    -

    5.5 Data Exploration And Preparation

    +
    +

    5.5 Data Exploration And Preparation

    The primary data structure and algorithms supporting exploration of the data are a Markov Trie @@ -1490,8 +1490,8 @@ All Trie code is hosted in the git repo located at -

    5.6 Data Visualization Functionalities For Data Exploration And Inspection

    +
    +

    5.6 Data Visualization Functionalities For Data Exploration And Inspection

    The functionality to explore and visualize data is baked into the Trie data structure. @@ -1501,7 +1501,7 @@ The functionality to explore and visualize data is baked into the Trie data stru By simply viewing the Trie in a Clojure REPL, you can inspect the Trie’s structure.

    -
    +
       (let [initialized-trie (->> (trie/make-trie "dog" "dog" "dot" "dot" "do" "do"))]
     initialized-trie)
     ;; => {(\d \o \g) "dog", (\d \o \t) "dot", (\d \o) "do", (\d) nil}
@@ -1543,12 +1543,12 @@ The Hidden Markov Model data structure doesn’t lend itself to any useful g
    -
    -

    5.7 Implementation Of Interactive Queries

    +
    +

    5.7 Implementation Of Interactive Queries

    -
    -

    5.7.1 Generate Rhyming Lyrics

    +
    +

    5.7.1 Generate Rhyming Lyrics

    This interactive query will return a list of rhyming phrases to any word or phrase you enter. @@ -1691,8 +1691,8 @@ The interactive query for the above can be found at -

    5.7.2 Complete Lyric Containing Suffix

    +
    +

    5.7.2 Complete Lyric Containing Suffix

    This interactive query will return a list of lyrics completing the given suffix with randomly generated prefixes. @@ -1794,8 +1794,8 @@ The interactive query for the above can be found at -

    5.8 Implementation Of Machine Learning Methods

    +
    +

    5.8 Implementation Of Machine Learning Methods

    The machine learning method chosen for this software is a Hidden Markov Model. @@ -1865,7 +1865,7 @@ The algorithm for generating predictions from the HMM is as follows.

    -
    +
     [(("<s>" "call" "me")
   ("<s>" "call")
   ("<s>" "right" "</s>")
@@ -1954,8 +1954,8 @@ It also performs compaction and serialization. Song lyrics are typically provide
    -
    -

    5.9 Functionalities To Evaluate The Accuracy Of The Data Product

    +
    +

    5.9 Functionalities To Evaluate The Accuracy Of The Data Product

    Since creative brainstorming is the goal, “accuracy” is subjective. @@ -2022,8 +2022,8 @@ This standardized measure of accuracy can be used to compare different language

    -
    -

    5.10 Security Features

    +
    +

    5.10 Security Features

    Artists/Songwriters place a lot of value in the secrecy of their content. Therefore, all communication with the web-based interface occurs over a secure connection using HTTPS. @@ -2039,16 +2039,16 @@ With this precaution in place, attackers will not be able to snoop the content t

    -
    -

    5.11 Tools To Monitor And Maintain The Product

    +
    +

    5.11 Tools To Monitor And Maintain The Product

    By having the application server behind an HAProxy load balancer, we can take advantage of the built-in HAProxy stats page for monitoring amount of traffic and health of the application servers.

    -
    -

    stats.png +

    +

    stats.png

    @@ -2066,8 +2066,8 @@ The server also includes the certbot script for updating and mainta
    -
    -

    5.12 A User-Friendly, Functional Dashboard That Includes At Least Three Visualization Types

    +
    +

    5.12 A User-Friendly, Functional Dashboard That Includes At Least Three Visualization Types

    You can access an example of the user interface at https://darklimericks.com/wgu. @@ -2086,8 +2086,8 @@ The first visualization is a scatter plot of rhyming words with the “quali

    -
    -
    -

    6.5 Visualizations

    +
    ++ ++ ++ ++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    RhymeQualityLyricPerplexity
    technologies8you will tear the skin from the nuclear technologies-0.04695091652785746
    pathologies7there’s no hope for body’s pathologies-0.09800371561934312
    apologies7swimming in a grey world dying it’s time for apologies-0.14781111654643642
    chronologies7damn god damn the seed lurks in chronologies-0.20912909334441387
    anomalies6yesterday was born i encounter the anomalies-0.19578505194217627
    atrocities6there’s no return and and the pimp your atrocities-0.21516240668167685
    ideologies6entrenched ideologies-0.27407234083849513
    monopolies6monopolies-0.8472654185540912
    qualities5with such qualities-0.0793752454750395
    policies5stop looking at insurance policies-0.11580898408112054
    colonies5betwixt my heels, through the tears you collapse the colonies-0.1610184959356118
    harmonies5broken harmonies-0.18655087962492334
    prophecies5seek the truth prophecies-0.24506696021938001
    festivities4you have touching the festivities-0.09271388814221376
    delicacies4grey that consumes what it never was sun and the delicacies-0.14553081854920977
    anybody’s4your eyes, will remain violent the anybody’s-0.17560987263626957
    extremities4i am missing extremities-0.30386279996641197
    casualties3feed the casualties-0.23600199637494926
    + +

    +Do these lyrics provide benefit to the brainstorming process? +

    + +

    +The lines “make sense” to varying degrees. +

    + +

    +The “pathologies” line, for example, contains a sensible 2-gram of “body’s pathologies”. The model has learned that the possessive form of “body” is a reasonable prefix to the word “pathologies”. +

    + + + + +++ ++ ++ ++ + + + + + + + + +
    pathologies7there’s no hope for body’s pathologies-0.09800371561934312
    + +

    +And the beginning of that line contains a phrase, “there’s no hope”, that fits perfectly with the genre/context of the training set (dark heavy metal). +

    + +

    +It’s clear that the training worked. The output is relevant to the genre and grammatically reasonable. +

    + +

    +There’s also a wide variety in the output, which is beneficial for +brainstorming. Suggestions range from clean and clear rhymes, like +“technologies” and “pathologies”, to more abstract rhymes like “technologies” +and “anybody’s”, which some artists can creatively manipulate effectively. +

    + +

    +I assess this version of the product proves viable and there’s exciting +possibilities for improvements by integrating with making suggestions that meet +certain stress patterns, preferring phrases that contain synonyms or antonyms, +and more. +

    +
    +
    + +
    +

    6.5 Visualizations

    -
    -

    rhyme-scatterplot.png +

    +

    rhyme-scatterplot.png

    -
    -

    wordcloud.png +

    +

    wordcloud.png

    -
    -

    rhyme-table.png +

    +

    rhyme-table.png

    -
    -

    6.6 Accuracy

    +
    +

    6.6 Accuracy

    It’s difficult to objectively test the models accuracy since the goal of “brainstorm new lyric” is such a subjective goal. A valid test of that goal will require many human subjects to subjectively evaluate their performance while using the tool compared to their performance without the tool. @@ -2225,8 +2437,8 @@ If we allow ourselves the assumption that the close a generated phrase is to a v

    -
    -

    6.6.1 Percentage Of Generated Lines That Are Valid English Sentences

    +
    +

    6.6.1 Percentage Of Generated Lines That Are Valid English Sentences

    We can use Apache OpenNLP to parse sentences into a grammar structure conforming to the parts of speech specified by the University of Pennsylvania’s Treebank Project. @@ -2265,9 +2477,15 @@ This is just one of many possible assessment techniques we could use. It’s (/ (count valid-english) 100))) (println (average-valid-of-100-suggestions)) +;; 47/100

    +
    +47/100
+
    + +

    Where nlp/valid-sentence? is defined as follows.

    @@ -2298,8 +2516,8 @@ Where nlp/valid-sentence? is defined as follows.
    -
    -

    6.7 Testing

    +
    +

    6.7 Testing

    My language of choice for this project encourages a programming technique or paradigm known as REPL-driven development. REPL stands for Read-Eval-Print-Loop. This is a way to write and test code in real-time without a compilation step. Individual code chunks can be evaluated inside an editor, resulting in rapid feedback. @@ -2343,12 +2561,12 @@ Here is an example of the test suite for the code related to syllabification:

    -
    -

    6.8 Source Code

    +
    +

    6.8 Source Code

    -
    -

    6.8.1 Tightly Packed Trie

    +
    +

    6.8.1 Tightly Packed Trie

    This is the data structure that backs the Hidden Markov Model. @@ -2360,8 +2578,8 @@ This is the data structure that backs the Hidden Markov Model.

    -
    -

    6.8.2 Phonetics

    +
    +

    6.8.2 Phonetics

    This is the helper library that syllabifies and manipulates words, phones, and syllables. @@ -2373,8 +2591,8 @@ This is the helper library that syllabifies and manipulates words, phones, and s

    -
    -

    6.8.3 Rhyming

    +
    +

    6.8.3 Rhyming

    This library contains code for analyzing rhymes, sentence structure, and manipulating corpuses. @@ -2386,8 +2604,8 @@ This library contains code for analyzing rhymes, sentence structure, and manipul

    -
    -

    6.8.4 Web Server And User Interface

    +
    +

    6.8.4 Web Server And User Interface

    This application is not publicly available. I’ll upload it with submission of the project. @@ -2396,16 +2614,16 @@ This application is not publicly available. I’ll upload it with submission

    -
    -

    6.9 Quick Start

    +
    +

    6.9 Quick Start

    -
    -

    6.9.1 How To Initialize Development Environment

    +
    +

    6.9.1 How To Initialize Development Environment

      -
    1. Required Software
      +
    2. Required Software
      • Docker
        • @@ -2415,7 +2633,7 @@ This application is not publicly available. I’ll upload it with submission
      3. -
    3. Steps
      +
    4. Steps
      1. Run ./db/run.sh && ./kv/run.sh to start the docker containers for the database and key-value store. @@ -2430,12 +2648,12 @@ This application is not publicly available. I’ll upload it with submission
      -
      -

      6.9.2 How To Run Software Locally

      +
      +

      6.9.2 How To Run Software Locally

        -
      1. Requirements
        +
      2. Requirements
        • Java
          • @@ -2444,7 +2662,7 @@ This application is not publicly available. I’ll upload it with submission
        3. -
      3. Steps
        +
      4. Steps
        1. Run ./db/run.sh && ./kv/run.sh to start the docker containers for the database and key-value store. @@ -2464,7 +2682,7 @@ This application is not publicly available. I’ll upload it with submission

        Author: Eric Ihli

        -

        Created: 2021-07-22 Thu 19:09

        +

        Created: 2021-07-22 Thu 19:41