I Tried to Walk Away from Lila but Good Ideas are Persistent

Remember Lila? Did you think I had abandoned her? If you did not follow my earlier blog you might be a little confused. Lila is not a live person. Lila was a conceptual design for a “cognitive writing technology,” natural language processing software to aid with reading and writing. It was a complex and consuming project. I tried to walk away from Lila but good ideas are persistent. Below you see a screenshot of a more basic project, a tool for analyzing individual After Reading essays and comparing them to the whole work.

The user interface is comparable to Voyant Tools by Stéfan Sinclair & Geoffrey Rockwell. Lila 0.1 has unique functions:

  1. On a Home screen a user gets to enter an essay. Lila 0.1 is intended to accept the text of individual essays created by me for After Reading. An Analyze button begins the natural language processing that results in the screen above. The text is displayed, highlighting one paragraph at a time as the user scrolls down.
  2. The button set provides four functions. The Home button is for navigation back to the Home screen. The Save button allows the user to save an essay with analytics to a database to build an essay set or corpus. The Documents button navigates to a screen for managing the database. The Settings button navigates to a screen that can adjust configurations for the analytics.
  3. The graph shows the output of natural language processing and analytics for a “Feeling” metric, an aggregate measure based on sentiment, emotion and perhaps other measures. The light blue shows the variance in Feeling across paragraphs. The dark blue straight line shows the aggregate value for the document. The user can see how Feeling varies across paragraphs and in comparison to the whole essay. Another view will allow for comparison of single essays to the corpus.
  4. The user can choose one of several available metrics to be displayed on the graph.
    • Count. The straight count of words.
    • Frequency. The frequency of words.
    • Concreteness. The imagery and memorability of words. A personal favourite.
    • Complexity. Ambiguity or polysemy, i.e., words with multiple meanings. Synonymy or antonmy. A measure of the readability of the text. Complexity can also be measured for sentences, e.g., number of conjunctions, and for paragraphs, e.g, number of sentences.
    • Hyponymy. A measure of the abstraction of words.
    • Metaphor. I am evaluating algorithms that identify metaphors.
    • Form. Various measures are available to measure text quality, e.g., repetition.
    • Readability by grade level.
    • Thematic presence can be measured by dictionary tagging of selected words related to the work’s theme.
  5. All metrics are associated with individuals words. Numeric values will be listed for a subset of the words.
  6. Topic Cloud. A representation of topics in an essay will be shown.

The intention is to help a writer evaluate the literary quality of an essay and compare it to the corpus. A little bit like spell-check and grammar-check, but packed with literary smarts. Where it is helpful to be conscious of conformity and variance, e.g., author voice, Lila can help. It is a modest step in the direction of an artificial intelligence project that will emerge in time. Perhaps one day Lila will live.

Email a comment: john.miedema@gmail.com
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