outline - i could have written that

intro

problematic situation

short description here, plus focus on text mining that is mainly regarded as a reading technique. what happens if text mining would be seen as a writing technique?

in-between-language / inter-language

OCR A + B example. Two fonts that did consessions in their precence, and form objects that are optimized for both machine and human reading processes.

NLP

In the whole project 'i-could-have-written-that', i would like to place NLP software central, not only as technology but also as a cultural object, to reveal in which way NLP software is constructed to understand human language, and what side-effects these techniques have.

audience

This thesis will aim for an audience that is interested in an alternative perspective on buzzwords like 'big data' and 'data-mining'. Also, this thesis will (hopefully!) offer a view from a computer-vision side: how software is written to understand the non-computer world of written text.

hypothesis

The results of data-mining software are not mined, results are constructed.

1 - text mining; what if text mining is seen as a writing machine?

If text mining is regarded to be a writing system, what and where does it write?

1.1 - text mining culture

What are the levels of construction in text mining software culture?
- By considering text mining technology as a reading machine?
- terminology
  - How does the metaphor of 'mining' effect the process?
  - 'data mining' → Knowledge Discovery in Data (KDD)
- How much can be based on a cultural side-product (like the text that is commonly used, as it is extracted from ie. social media)?

1.2 – Pattern's gray spots

What are the levels of construction in text mining software itself?
- What gray spots appear when text is processed?
  - What is meant by 'grayness'? How can it be used as an approach to software critique?
  - Text processing: how does written text transform into data?
  - Bag-of-words, or 'document'
  - 'count' → 'weight'
  - trial-and-error, modeling the line
- Testing process
  - how is an algorithm actually only 'right' according to that particular test-set, with its own particularities and exceptions?
  - loops to improve your outcomes

1.3 – text mining (context)

what are text mining applications? listed here

Showing that text mining has been applied across very different field, and thereby seeming to be a sort of 'holy grail', solving a lot of problems. (though i'm not sure if this is needed)

1 - 'key value pair' focus

Data is seen as a material that easily can be extracted from the web, and regarded to be that little 'truth-snapshot' that is taken at a certain time and moment. In text mining, the material that is used as input are written pieces of texts transformed into data. The data is formatted as a word + number, a feature + weight, a key + value pair, or how a linguist would call it: a subject + predicate combination. This is the material building block where text mining outcomes are constructed with. Immaterial building blocks are: point-of-departures, source-selection, noise-reduction, and test-techniques. All these elements effect the vector / the model / the algorithm.

what is the position of the key-value pair in the text mining process?
- description of the 5 KDD steps
what side effects does it have to describe a profile in key-value pairs?
- using written words is already a representation: the word represents the meaning/thought/intention
- in text mining, written text is aimed to be interpreted from a reader's perspective (as opposed to the writer's intention)
- key-value pair is a representational format: the value represents the key

2 - context and history

where could the key-value format be traced back from?
- in a tradition of systematizing language?
  - Are these aims not relying to much on a rational tradition? Austin's 'Speech Act theory' & Heidegger's 'dasein' and opinion that we “should not split up the object from the predicate”
- in a tradition of data-processing?
- in a tradition of AI/NLP projects like ELIZA & SHRDLU (?)?

Text mining seems to be a rather brutal way to deal with the aim to process natural language into useful information. To reflect on this brutality, tracing back a longer tradition of natural language processing could be usefull. Hopefully this will be a way to create some distance to the hurricanes of data that are mainly known as 'big', 'raw' or 'mined' these days.

By looking at projects from the past in the field of NLP (being part of AI/computer science departments), this chapter will attempt to highlight their attitudes towards written text.

(The systemization of language through key-value pairs follows a Western tradition that is based on the convention that there is an individual that perceives, and there is an outsie world that contains its own natural truth. The linguist Austin rather shows that language is merely a speech act, happening as a social act. In these speech acts there is no such external objective meaning of the words we use in language, that meaning only exists in social relations. Heidegger even goes further, and says that we should not split up the object from the predicate. For example: while 'hammering' the person that hammers is not regarding the hammer in a reflective sense. The person is in the moment of using the hammer to achieve something. The only moment when the person would be confronted with a representational sense of the hammer, is when the hammer breaks down. It is at that moment that the person will need to fix the hammer, and learn a bit more about what a hammer 'is'.)

3 - circularity (reasoning)

A text mining process is not aiming to 'find' if assumptions can be confirmed, it is rather engineered to be so.

text mining & an iterating workflow
- Pattern's workflow more here
- IBM's 'iterating' graph
heliocentric/geocentric diagrams to understand the universe → complexity confirms the input (here: the perception of stars and their positions) Could this be the case in mining practices?

material

bibliography (five key texts)

Matthew Fuller - short presentation of the poem: Blue Notebook #10 / The Red-haired Man, during Ideographies of Knowledge, Mundaneum, Mons (Oct. 2015); annotations
Joseph Weizenbaum - Computer Power and Human Reason: From Judgement to Calculation (1976);
Winograd + Flores - Understanding Computers & Cognition (1987);
Vilem Flusser - Towards a Philosophy of Photography (1983); → annotations
Antoinette Rouvroy - All Watched Over By Algorithms - Transmediale (Jan. 2015); → annotations
The Journal of Typographic Research - OCR-B: A Standardized Character for Optical Recognition this article (V1N2) (1967); → abstract

annotations

Alan Turing - Computing Machinery and Intelligence (1936)
The Journal of Typographic Research - OCR-B: A Standardized Character for Optical Recognition this article (V1N2) (1967); → abstract
Ted Nelson - Computer Lib & Dream Machines (1974);
Joseph Weizenbaum - Computer Power and Human Reason (1976); → annotations
Water J. Ong - Orality and Literacy (1982);
Vilem Flusser - Towards a Philosophy of Photography (1983); → annotations
Christiane Fellbaum - WordNet, an Electronic Lexical Database (1998);
Charles Petzold - Code, the hidden languages and inner structures of computer hardware and software (2000); → annotations
John Hopcroft, Rajeev Motwani, Jeffrey Ullman - Introduction to Automata Theory, Languages, and Computation (2001);
James Gleick - The Information, a History, a Theory, a Flood (2008); → annotations
Matthew Fuller - Software Studies. A lexicon (2008);
- Language, Florian Cramer; → annotations
- Algorithm, Andrew Goffey;
Marissa Meyer - the physics of data, lecture (2009); → annotations
Matthew Fuller & Andrew Goffey - Evil Media (2012); → annotations
Antoinette Rouvroy - All Watched Over By Algorithms - Transmediale (Jan. 2015); → annotations
Benjamin Bratton - Outing A.I., Beyond the Turing test (Feb. 2015) → annotations
Ramon Amaro - Colossal Data and Black Futures, lecture (Okt. 2015); → annotations
Benjamin Bratton - On A.I. and Cities : Platform Design, Algorithmic Perception, and Urban Geopolitics (Nov. 2015);

currently working on

* terminology: data 'mining'
* Knowledge Discovery in Data (KDD) in the wild, problem formulations
* KDD, applications
* KDD, workflow
* text-processing: simplification
* list of data mining parties

other

outline-thesis (2) → NLP

User:Manetta/thesis/thesis-outline

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