Processing of Large Document Collections 1

Helena Ahonen-MykaUniversity of Helsinki

Organization of the courseClasses: 17.9., 22.10., 23.10., 26.11.

lectures (Helena Ahonen-Myka): 10-12,13-15 exercise sessions (Lili Aunimo): 15-17 required presence: 75%

Exercises are given (and returned) each week required: 75%

Exam: 4.12. at 16-20, AuditorioPoints: Exam 30 pts, exercises 30 pts

Schedule

17.9. Character sets, preprocessing of text, text categorization

22.10. Text summarization23.10. Text compression26.11. … to be announced…

self-study: basic transformations for text data, using linguistic tools, etc.

In this part...

Character setspreprocessing of texttext categorization

1. Character sets

Abstract character vs. its graphical representation

abstract characters are grouped into alphabets each alphabet forms the basis of the

written form of a certain language or a set of languages

Character sets

For instance for English:

uppercase letters A-Zlowercase letters a-zpunctuation marksdigits 0-9common symbols: +, =

ideographic symbols of Chinese and Japanese

phonetic letters of Western languages

Character sets

To represent text digitally, we need a mapping between (abstract) characters and values stored digitally (integers)

this mapping is a character setthe domain of the character set is

called a character repertoire (= the alphabet for which the mapping is defined)

Character sets

For each character in the character repertoire, the character set defines a code value in the set of code points

in English: 26 letters in both lower- and uppercase ten digits + some punctuation marks

in Russian: cyrillic lettersboth could use the same set of code points (if

not a bilingual document) in Japanese: could be over 6000 characters

Character sets

The mere existence of a character set supports operations like editing and searching of text

usually character sets have some structure e.g. integers within a small range all lower-case (resp. upper-case) letters

have code values that are consecutive integers (simplifies sorting etc.)

Character sets: standars

Character sets can be arbitrary, but in practice standardization is needed for interoperability (between computers, programs,...)

early standards were designed for English only, or for a small group of languages at a time

Character sets: standards

ASCIIISO-8859 (e.g. ISO Latin1)UnicodeUTF-8, UTF-16

ASCII

American Standard Code for Information Interchange

A seven bit code -> 128 code pointsactually 95 printable characters only

code points 0-31 and 128 are assigned to control characters (mostly outdated)

ISO 646 (1972) version of ASCII incorporated several national variants (accented letters and currency symbols)

ASCII

With 7 bits, the set of code points is too small for anything else than American English

solution: 8 bits brings more code points (256) ASCII character repertoire is mapped to the

values 0-127 additional symbols are mapped to other

values

Extended ASCII

Problem: different manufacturers each developed

their own 8-bit extensions to ASCIIdifferent character repertoires ->

translation between them is not always possible

also 256 code values is not enough to represent all the alphabets -> different variants for different languages

ISO 8859

Standardization of 8-bit character sets In the 80´s: multipart standard ISO 8859 was

produceddefines a collection of 8-bit character sets,

each designed for a group of languages the first part: ISO 8859-1 (ISO Latin1)

covers most Western European languages 0-127: identical to ASCII, 128-159 (mostly)

unused, 96 code values for accented letters and symbols

Unicode

256 is not enough code points for ideographically represented languages

(Chinese, Japanese…) for simultaneous use of several languages

solution: more than one byte for each code value

a 16-bit character set has 65,536 code points

Unicode

16-bit character set, e.g. 65,536 code points

not sufficient for all the characters required for Chinese, Japanese, and Korean scripts in distinct positions CJK-consolidation: characters of these

scripts are given the same value if they look the same

Unicode

Code values for all the characters used to write contemporary ’major’ languages also the classical forms of some languages Latin, Greek, Cyrillic, Armenian, Hebrew,

Arabic, Devanagari, Bengali, Gurmukhi, Gujarati, Oriya, Tamil, Telugu, Kannada, Malayalam, Thai, Lao, Georgian, Tibetan

Chinese, Japanese, and Korean ideograms, and the Japanese and Korean phonetic and syllabic scripts

Unicode

punctuation marks technical and mathematical symbols arrows dingbats (pointing hands, stars, …) both accented letters and separate diacritical

marks (accents, tildes…) are included, with a mechanism for building composite characterscan also create problems: two characters that look

the same may have different code values->normalization may be necessary

Unicode

Code values for nearly 39,000 symbols are provided

some part is reserved for an expansion method (see later)

6,400 code points are reserved for private use they will never be assigned to any

character by the standard, so they will not conflict with the standard

Unicode: encodings

Encoding is a mapping that transforms a code value into a sequence of bytes for storage and transmission

identity mapping for a 8-bit code? it may be necessary to encode 8-bit characters as

sequences of 7-bit (ASCII) characters e.g. Quoted-Printable (QP)

code values 128-255 as a sequence of 3 bytes1: ASCII code for ’=’, 2 & 3: hexadecimal digits of the

value233 -> E9 -> =E9

Unicode: encodings

UTF-8 ASCII code values are likely to be more

common in most text than any other valuesin UTF-9 encoding ASCII characters are sent

themselves (high-order bit 0)other characters (two bytes) are encoded

using up to six bytes (high-order bit is set to 1)

Unicode: encodings

UTF-16: expansion method two 16-bit values are combined to a 32-

bit value -> a million characters available

2. Preprocessing of text

Text cannot be directly interpreted by the many document processing applications

an indexing procedure is needed mapping of a text into a compact

representation of its contentwhich are the meaningful units of text?how these units should be combined?

usually not ”important”

Vector model

A document is usually represented as a vector of term weights

the vector has as many dimensions as there are terms (or features) in the whole collection of documents

the weight represents how much the term contributes to the semantics of the document

Vector model

Different approaches: different ways to understand what a

term is different ways to compute term weights

Terms

Words typical choice set of words, bag of words

phrases syntactical phrases statistical phrases usefulness not yet known?

Terms

Part of the text is not considered as terms very common words (function words):

articles, prepositions, conjunctions

numeralsthese words are pruned

stopword listother preprocessing possible

stemming, base words

Weights of terms

Weights usually range between 0 and 1binary weights may be used

1 denotes presence, 0 absence of the term in the document

often the tfidf function is used higher weight, if the term occurs often in the

document lower weight, if the term occurs in many

documents

Structure

Either the full text of the document or selected parts of it are indexed

e.g. in a patent categorization application title, abstract, the first 20 lines of the

summary, and the section containing the claims of novelty of the described invention

some parts may be considered more important e.g. higher weight for the terms in the title

Dimensionality reductionMany algorithms cannot handle high

dimensionality of the term space (= large number of terms)

usually dimensionality reduction is applieddimensionality reduction also reduces

overfitting classifier that overfits the training data is good at

re-classifying the training data but worse at classifying previously unseen data

Dimensionality reduction

Local dimensionality reduction for each category, a reduced set of terms

is chosen for classification that category hence, different subsets are used when

working with different categoriesglobal dimensionality reduction

a reduced set of terms is chosen for the classification under all categories

Dimensionality reduction

Dimensionality reduction by term selection the terms of the reduced term set are a

subset of the original term setDimensionality reduction by term

extraction the terms are not the same type of the terms

in the original term set, but are obtained by combinations and transformations of the original ones

Dimensionality reduction by term selection

Goal: select terms that, when used for document indexing, yields the highest effectiveness in the given application

wrapper approach the reduced set of terms is found iteratively and

tested with the application

filtering approach keep the terms that receive the highest score

according to a function that measures the ”importance” of the term for the task

Dimensionality reduction by term selection

Many functions available document frequency: keep the high

frequency termsstopwords have been already removed50% of the words occur only once in the

document collection e.g. remove all terms occurring in at most 3

documents

Dimensionality reduction by term selection

Information-theoretic term selection functions, e.g. chi-square information gain mutual information odds ratio relevancy score

Dimensionality reduction by term extraction

Term extraction attempts to generate, from the original term set, a set of ”synthetic” terms that maximize effectiveness

due to polysemy, homonymy, and synonymy, the original terms may not be optimal dimensions for document content representation

Dimensionality reduction by term extraction

Term clustering tries to group words with a high degree of pairwise

semantic relatedness groups (or their centroids) may be used as

dimensions

latent semantic indexing compresses document vector into vectors of a

lower-dimensional space whose dimensions are obtained as combinations of the original dimensions by looking at their patterns of co-occurrence

3. Text categorization

Text classification, topic classification/spotting/detection

problem setting: assume: a predefined set of categories,

a set of documents label each document with one (or more)

categories

Text categorization

Two major approaches: knowledge engineering -> end of 80’s

manually defined set of rules encoding expert knowledge on how to classify documents under the given gategories

machine learning, 90’s ->an automatic text classifier is built by

learning, from a set of preclassified documents, the characteristics of the categories

Text categorization

Let D: a domain of documents C = {c1, …, c|C|} : a set of predefined categories T = true, F = false

The task is to approximate the unknown target function ’: D x C -> {T,F} by means of a function : D x C -> {T,F}, such that the functions ”coincide as much as possible”

function ’ : how documents should be classified function : classifier (hypothesis, model…)

We assume...

Categories are just symbolic labels no additional knowledge of their meaning is

availableNo knowledge outside of the documents is

available all decisions have to be made on the basis of

the knowledge extracted from the documents metadata, e.g., publication date, document

type, source etc. is not used

-> general methods

Methods do not depend on any application-dependent knowledge in operational applications all kind of

knowledge can be usedcontent-based decisions are necessarily

subjective it is often difficult to measure the

effectiveness of the classifiers even human classifiers do not always agree

Single-label vs. multi-label

Single-label text categorization exactly 1 category must be assigned to each

dj D

Multi-label text categorization any number of categories may be assigned

to the same dj D

Special case of single-label: binary each dj must be assigned either to category

ci or to its complement ¬ ci

Single-label, multi-label

The binary case (and, hence, the single-label case) is more general than the multi-label an algorithm for binary classification

can also be used for multi-label classification

the converse is not true

Category-pivoted vs. document-pivoted

Two different ways for using a text classifier

given a document, we want to find all the categories, under which it should be filed -> document-pivoted categorization (DPC)

given a category, we want to find all the documents that should be filed under it -> category-pivoted categorization (CPC)

Category-pivoted vs. document-pivoted

The distinction is important, since the sets C and D might not be available in their entirety right from the start

DPC: suitable when documents become available at different moments in time, e.g. filtering e-mail

CPC: suitable when new categories are added after some documents have already been classified (and have to be reclassified)

Category-pivoted vs. document-pivoted

Some algorithms may apply to one style and not the other, but most techniques are capable of working in either mode

Hard-categorization vs. ranking categorization

Hard categorization the classifier answers T or F

Ranking categorization given a document, the classifier might

rank the categories according to their estimated appropriateness to the document

respectively, given a category, the classifier might rank the documents

Applications of text categorization

Automatic indexing for Boolean information retrieval systems

document organizationtext filteringword sense disambiguationhierarchical categorization of Web

pages

Automatic indexing for Boolean IR systems

In an information retrieval system, each document is assigned one or more keywords or keyphrases describing its content keywords belong to a finite set called controlled

dictionary

TC problem: the entries in a controlled dictionary are viewed as categories k1 x k2 keywords are assigned to each

document document-pivoted TC

Document organization

Indexing with a controlled vocabulary is an intance of the general problem of document base organization

e.g. a newspaper office has to classify the incoming ”classified” ads under categories such as Personals, Cars for Sale, Real Estate etc.

organization of patents, filing of newspaper articles...

Text filtering

Classifying a stream of incoming documents dispatched in an asynchronous way by an information producer to an information consumer

e.g. newsfeed producer: news agency; consumer: newspaper the filtering system should block the delivery

of documents the consumer is likely not interested in

Word sense disambiguation

Given the occurrence in a text of an ambiguous word, find the sense of this particular word occurrence

E.g. Bank of England the bank of river Thames ”Last week I borrowed some money

from the bank.”

Word sense disambiguation

Indexing by word senses rather than by words text categorization

documents: word occurrence contexts categories: word senses

also resolving other natural language ambiguities context-sensitive spelling correction, part of

speech tagging, prepositional phrase attachment, word choice selection in machine translation

Hierarchical categorization of Web pages

E.g. Yahoo like web hierarchical catalogues

typically, each category should be populated by ”a few” documents

new categories are added, obsolete ones removed

usage of link structure in classificationusage of the hierarchical structure

Knowledge engineering approach

In the 80´s: knowledge engineering techniques building manually expert systems capable

of taking text categorization decisions expert system: consists of a set of rules

wheat & farm -> wheatwheat & commodity -> wheatbushels & export -> wheatwheat & winter & ~soft -> wheat

Knowledge engineering approach

Drawback: rules must be manually defined by a knowledge engineer with the aid of a domain expert any update necessitates again human

intervention totally domain dependent -> expensive and slow process

Machine learning approach

A general inductive process (learner) automatically builds a classifier for a category ci by observing the characteristics of a set of documents manually classified under ci or ci by a domain expert

from these characteristics the learner gleans the characteristics that a new unseen document should have in order to be classified under ci

supervised learning (= supervised by the knowledge of the training documents)

Machine learning approach

The learner is domain independent usually available ’off-the-shelf’

the inductive process is easily repeated, if the set of categories changes

manually classified documents often already available manual process may exist

if not, it still easier to manually classify a set of documents than to build and tune a set of rules

Training set, test set, validation set

Initial corpus of manually classified documents let dj belong to the initial corpus

for each pair <dj, ci> it is known if dj should be filed under ci

positive examples, negative examples of a category

Training set, test set, validation set

The initial corpus is divided into two sets a training (and validation) set a test set

the training set is used to build the classifier

the test set is used for testing the effectiveness of the classifiers each document is fed to the classifier and the

decision is compared to the manual category

Training set, test set, validation set

The documents in the test are not used in the construction of the classifier

alternative: k-fold cross-validation k different classifiers are built by partitioning

the initial corpus into k disjoint sets and then iteratively applying the train-and-test approach on pairs, where k-1 sets construct a training set and 1 set is used as a test set

individual results are then averaged

Training set, test set, validation set

Training set can be split to two partsone part is used for optimising

parameters test which values of parameters yield

the best effectivenesstest set and validation set must be

kept separate

Inductive construction of classifiers

A ranking classifier for a category ci

definition of a function that, given a document, returns a categorization status value for it, i.e. a number between 0 and 1

documents are ranked according to their categorization status value

Inductive construction of classifiers

A hard classifier for a category definition of a function that returns true

or false, or definition of a function that returns a

value between 0 and 1, followed by a definition of a thresholdif the value is higher than the threshold ->

trueotherwise -> false

Learners

Probabilistic classifiers (Naïve Bayes)decision tree classifiersdecision rule classifiersregression methodson-line methodsneural networksexample-based classifiers (k-NN)support vector machines

Rocchio method

Linear classifier methodfor each category, an explicit profile

(or prototypical document) is constructed benefit: profile is understandable even

for humans

Rocchio method

A classifier is a vector of the same dimension as the documents

weights:

classifying: cosine similarity of the category vector and the document vector

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