LIS 618 lecture 2 Thomas Krichel 2002 09

LIS 618 lecture 2 Thomas Krichel 2002 -09 -22

Structure of talk • General round trip on theoretical matters, part – Information retrieval models • vector model • Probabilistic model – Retrieval performance evaluation – Query languages • Introduction to online searching • Introduction to DIALOG – Overview – bluesheets

vector model • associates weights with each index term appearing in the query and in each database document. • relevance can be calculated as the cosine between the two vector, i. e. their cross product divided be the square roots of the squares of each vector. This measure varies between 0 and 1.

tf/idf weighting technique • Let n_i be the number of documents where the term d_i appears. Let F_i_j be the number of times term i appears in the document j. • The normalized frequency is f_i_j, given by f_i_j=(F_i_j/max_l(F_l_j) that is the raw frequency divided by the maximum raw frequency achieved by any term in the document j.

tf/idf weighting technique • Let N be the number of documents. • Then the most frequently used weighting scheme is w_i_j=f_i_j * log(N/n_i) • There are other methods, but these are variations on this one.

advantages of vector model • • term weighting improves performance sorting is possible easy to compute, therefore fast results are difficult to improve without – query expansion – user feedback circle

probabilistic model (outline only) • starts with the assumption that there is a subset of documents that form the ideal answer set • query process specifies properties of the answer set • query terms can be used to form a probability that a document is part of the answer • then we start an iterative process with the user to gain more characteristics about the answer set

recursive method • If we assume that the probability that the documents that are relevant among a set of initially retrieved documents is proportional to the appearance of index terms that are part of the query, the probability can further be refined.

probabilistic model • For any user requirement, we assume that there is an answer set and that the probability that the user finds a document interesting only depends on the document and the query • Then the similarity of the document to the query can be expressed as • s=(probability that the document is part of the answer set / probability that it is not part of the answer set). • There are ways to calculate this (with some more assumptions).

retrieval performance evaluation • There are two classic measures. Both assume that there is an answer set. • Recall is the fraction of the relevant documents that the query result has captured. • Precision is the fraction of the retrieved documents that is relevant.

recall and precision curves • assume that all the retrieved documents arrive at once and are being examined. • during that process, the user discover more and more relevant documents. Recall increases. • during the same process, at least eventually, there will be less and less useful document. Precision declines (usually).

Example • let the answer set be {4, 7, 5, 3, 6, 1, 0, 8, 9} and non-relevant documents represented by letters. • A query reveals the following result: 7, a, 3, b, c, 9, n, j, l, 5, r, o, s, e, 4. • for the first document, (recall, precison) is (10%, 100%), for the third, (20%, 60%), then follow (30%, 50%), (40%, 40%), (50%, 27%)

recall/precision curves • Such curves can be formed for each query. • An average curve, for each recall level, can be calculated for several queries. • Recall and precision levels can also be used to calculate two single-valued summaries.

average precision at seen document • sum all the precision level for each new relevant document and divide by the total number of relevant documents is the query. • In our example, it is 0. 57 • This measure favors retrieval methods that get the relevant documents to the top.

R-precision • • a more ad-hoc measure. Let R be the size of the answer set. Take the first R results of the query. Find the number of relevant documents Divide by R. In our example, the R-precision is. 4. An average can be calculated for a number of queries.

critique of recall & precision • recall has to be estimated by an expert • recall is very difficult to estimate in a large collection • measures most appropriate to a situation where queries are run in batch mode, they are difficult to reconcile with the idea of interactive use. • there are some other measures.

simple queries • single-word queries – one word only – Hopefully some word combinations are understood as one word, e. g. on-line • Context queries – phrase queries (be aware of stop words) – proximity queries, generalize phrase queries • Boolean queries

simple pattern queries • • • prefix queries (e. g. "anal" for analogy) suffix queries (e. g. "oral" for choral) substring (e. g. "al" for talk) ranges (e. g. form "held" to "hero") within a distance, usually Levenshtein distance (i. e. the minimum number of insertions, deletions, and replacements) of query term

regular expressions • come from UNIX computing • build form strings where certain characters are metacharacters. • example: "pro(blem)|(tein)s? " matches problem, protein and proteins. • example: New. *y matches "New Jersey" and "New York City", and "New Delhy". • great variety of dialects, usually very powerful. • Extremely important in digital libraries.

structured queries • make use of document structures • simplest example is when the documents are database records, we can search for terms is a certain field only. • if there is sufficient structure to field contents, the field can be interpreted as meaning something different than the word it contains. example: dates

query protocols • There are some standard languages – Z 39. 50 queries – CCL, "common command language" is a development of Z 39. 50 – CD-RDx "compact disk read only data exchange" is supported by US government agencies such as CIA and NASA – SFQL "structure full text query language" built on SQL

document preprocessing • operations done on the documents before indexing – lexical analysis – elimination of stop words – stemming of words – selection of index term – construction of term categorization structures • receives a decline in attention

lexical analysis • divides a stream of characters into a stream of words • seems easy enough but…. • should we keep numbers? • hyphens. compare "state-of-the-art" with "b -52" • removal of punctuation, but "333 B. C. " • casing. compare "bank" and "Bank"

elimination of stop words • some words carry no meaning and should be eliminated • in fact any word that appears in 80% of all documents is pretty much useless, but • consider a searcher for "to be or not to be".

stemming • in general, users search for the occurrence of a term irrespective of grammar • plural, gerund forms, past tense can be subject to stemming • important algorithm by Porter • evidence on effect on retrieval is mixed

index term selection • some engines try to capture nouns only • some nouns that appear heavily together can be considered to be one index term, such as "computer science" • Most web engines, however, index all words, why?

thesauri • a list of words and for each word, a list of related words – synonyms – broader terms – narrower terms • used – to provide a consistent vocabulary for indexing and searching – to assist users with locating terms for query formulation – allow users to broaden or narrow query

use of thesauri • most users want to get a quick response • often the selection of terms is erroneous • frequently the relationship between terms in the query is badly served by the relationships in the query. Thus thesaurus expansion of an initial query (if performed automatically) can lead to bad results.

Online database searching

before a search • what is purpose – brief overview – comprehensive search • What perspective on the topic – scholarly – technical – business – popular I

before search • What type of information – – Fulltext Bibliographic Directory Numeric • Are there any known sources? – – Authors Journals Papers Conferences II

before search • • III What are the language restrictions? What, if any, are the cost restrictions? How current need the data to be? How much of each record is required?

DIALOG

Literature http: //training. dialog. com/sem_info/courses/ pdf_sem/dlg 1. pdf http: //training. dialog. com/sem_info/courses/ pdf_sem/dlg 2. pdf http: //training. dialog. com/sem_info/courses/ pdf_sem/dlg 3. pdf http: //training. dialog. com/sem_info/courses/ pdf_sem/dlg 4. pdf

databank • over 500 different databases – – – references and abstracts for published literature, business information and financial data; complete text of articles and news stories; statistical tables Directories • Two interfaces to all this stuff. – Guided search (for neophytes) – Command search (for Masters) for us!!

Four steps in a search • Use the Databases Selection Tool to select databases • Identify search terms • Use Dialog basic commands to conduct a search • View records online

B E S T strategy • begin – b 630, 636 – b papersmj, not 630 • expand – e co=long island university – e au=krichel, t I

BEST II • select – s (mate? (N)drink? ) or (lex(N)para? ) – s s 1 and s 2 • type – type s 1/3/1, 6

Command search • The first thing to be done is to select a database. • 8 categories – – Government News Business Reference – Medicine & Pharmaceuticals – Science & Technology – Intellectual property – Social Sciences and Humanities • there we go to command search

databases menus • databases are ordered in hierarchical fashion • at each level a Boolean search can be executed – on all of Dialog – on the databases in the current hierarchical level

searching • result may be a just a blank screen • otherwise, a table with the file number, the database name and the number of hits appears • wait until the display is complete…. • sorting of database is possible by the number of hits for the current query

blue sheet • each database name is linked to a blueish pop-up window called the blue sheet for the database • Contents of bluesheet is covered later • at this stage we choose a database and hit "begin". We see that there is a command selected: "be numbers" where numbers are the ones for the databases selected, separated by comma.

finding a database • file 411 contains the database of databases • 'sf category' selects files belonging to a category • categories are listed at http: //library. dialog. com/bluesheets • 'rank files' will rank the results • 'b ref, ref' will select databases using rank references.

closer look at the bluesheet • file description • subject coverage (free vocabulary) • format options, lists all formats – by number (internal) – by dialog web format (external, i. e. crossdatabase) • search options – basic index, i. e. subject contents – additional index, i. e. non-subject

search options: basic index • select without qualifiers searches in all fields in the basic index • bluesheet lists field indicators available for a database • also note if field is indexed by word or phrase. proximity searching only works with word indices.

other search options • additional indices lists those terms that can lead a query. Often, these are phrase indexed. • special features will list other features that the database has and that can be used in queries

http: //openlib. org/home/krichel Thank you for your attention!