FROM “Meaning”s FROM “Meaning”s

TO WordsTO Words

İlknur DURGAR

EL-KAHLOUT

Problem

For a given definition, find the appropriate word (or words), that has a similar definition– traditional dictionary no use

Examples

Akımı ölçmek için kullanılan alet akımölçer(A device that is used to measure the current ammeter)

akımölçer: elektrik akımının şiddetini ölçmeye yarayan araç, ampermetre

(ammeter: a device that measures the intensity of electrical current, amperemeter)

Examples

Çalıştığı işten kendi isteği ile ayrılmak istifa(Leaving one’s job voluntarily resignation)

istifa: kendi isteği ile görevden ayrılma(resignation: leaving voluntarily, of a position)

Applications

Computer-assisted language learning Solving crossword puzzles Reverse dictionary

Outline

Problem Statement Challenges Our Approach Methods Results Result Summary Conclusion

Problem Statement

For example, one knows the meaning of the word akımölçer (ammeter):

Akımı ölçmek için kullanılan alet (A device that is used to measure the current)

However the actual definition of the word in the dictionary is:

elektrik akımının şiddetini ölçmeye yarayan araç, ampermetre

(a device that measures the intensity of electrical current, amperemeter)

Problem Statement

Find the similarity between two definitions Akımı ölçmek için kullanılan alet (A device that is used to measure the current)

elektrik akımının şiddetini ölçmeye yarayan araç, ampermetre

(a device that measures the intensity of electrical current, amperemeter)

Meaning-to-Word (MTW)

Meaning-to-Word System (MTW)– attacks the problem of finding the appropriate

word (or words), whose meaning “matches” the given definition

Challenges

Two challenging problems

– finding words whose definitions are "similar" to the query in some sense.

– ranking the candidate words using a variety of ways.

Information flow in MTW

User Definition

Search in Dictionary

Rank Candidates

query

candidates

List of words

Meanings To Words (MTW)

The problem of retrieving words from their "meaning"s at first sight seems to be an information retrieval problem

Information Retrieval (IR)

responds to the user's query by selecting documents from a database and ranking them in terms of relevance.

uses (mostly) statistical and symbolic techniques to retrieve documents for a given query, employing shallow natural language analysis.

Similarities between MTW and IR

Goals – Select relevant items from a collection based

on a query

Collections

– Collection Dictionary

Documents: – Documents Definitions

Similarities between MTW and IR

Approaches:– compare the user request with each of the

information in the collection Ranking:

– most important task– But ranking strategies are different

Differences between IR and MTW

Expected results:– Many relevant documents vs. only one correct word

Query Expression:– Keywords vs. sentence (or phrases)

Space size: – Long documents (avg. 300 - 400 words ) vs. one

sentence long definitions (avg. 10 - 20 words)– Huge collection(106-109doc) vs. medium dictionary

(105 word definitions)

Available Resources

Turkish Dictionary Turkish Wordnet

Normalization

User Definition

Search in Dictionary

Rank Candidates

query

candidates

List of words

Normalization

Normalization

Tokenization: – All inter-word (non-word, non-digit) symbols eliminated (ex.

Punctuation). – Each word is a term

Stemming: – same stem but different affixes– enables matching different morphological variants of the original

definition's words Stop Word Elimination:

– have little or no meaning– Frequency (very frequent words)– Linguistic (determiners, prepositions, pronouns,..)

Query Processing

User Definition

Search in Dictionary

Rank Candidates

query

candidates

List of words

Query Processing

Query Processing

Subset Generation:– Search with different set of words– Select informative words from user’s query

Query: hiç evlenmemiş kişi (a person who has never been married)

* {önce, evlenmemiş, kişi}(before, unmarried, person)

* {evlenmemiş, kişi} {önce, kişi} {önce, evlenmemiş} (unmarried, person) (before, person) (before, unmarried)

*{evlenmemiş} {önce} {kişi} (unmarried) (before) (person)

Query Processing

Subset Sorting:– Unordered list of subsets are insufficient

• Top-down sorting

– Rank the generated subsets 1) By the number of words

Ex: {önce,evlenmemiş, kişi} (before, unmarried, person) vs. {evlenmemiş, kişi} (unmarried, person)

2) By the sum of frequency logarithmEx:{evlenmemiş, kişi} (unmarried, person) vs. {önce, kişi} (before, person)

Searching for “Meaning”s

User Definition

Search in Dictionary

Rank Candidates

query

candidates

List of words

Searching for “Meaning”s

Two methods – Stem Match– Query Expansion

Stem Match

Morphological normalization of words– Find meanings that contain morphological

variants of the original definition

Stem Match (Ex.)

{A device that is used to measure the current}

{ akımı ölçmek için kullanılan alet }

ak (white) ölç (measure) için (to) kullan (use) alet (device)

akım (current) iç (drink) kul (slave)

akı (flux)

Stem Match

akımı ölçmek için kullanılan alet - A device that is used to measure the current

elektrik akımının şiddetini ölçmeye yarayan araç, ampermetre - a device that measures the intensity of electrical current, amperemeter

Stem Match Drawback:

– Conflate two words with very different meanings to the same stem

(ex: yüksek (high) yüksek (high), yük (load)

ilim (science, my city), ilde (in the city) il (city))

– Cant find relations between similar words

(ex: kimse (someone) kişi (person) ,

bölüm (part) kısım (portion))

Query Expansion

The users of retrieval systems often use different words to describe the concepts in their queries than the authors use to describe the same concept in their documents.

In experiments, two people use the same term to describe an object less than 20% of the time.(Furnas 1987).

Using Query Expansion

Two different approaches:• Expand query with relations (synonyms,

specializations, generalizations)• Expand query with unexpanded query’s

relevant answers

Synonym relation used in MTW Ex:{besin,gıda} (food, nourishment)

{iyileş,düzel} (to get better) /{iyileş,geliş} (to improve)

Query Expansion (Ex.)

{A device that is used to measure the current}

{ akımı ölçmek için kullanılan alet }

*ak (white) ölç (measure) için (to) ***kullan (use) alet (device)

akım (current) iç (drink)****kul (slave)

**akı (flux)

*beyaz ölçüm ***faydalan araç

**debi ***yararlan gereç

**akış ****köle

Query Expansion (Ex.)

akımı ölçmek için kullanılan alet - A device that is used to measure the current

elektrik akımının şiddetini ölçmeye yarayan araç, ampermetre - a device that measures the intensity of electrical current, amperemeter

Ranking

User Definition

Search in Dictionary

Rank Candidates

query

candidates

List of words

Ranking

The main goal of a retrieval system is to find the documents that are relevant to a query.

Documents that are likely to be more relevant

should be ranked at the top and documents that are likely to be less relevant should be ranked at the bottom of the ranked list. (Hiemstra 1999)

Ranking

Most important part of MTW– Having the right answer in the retrieved set is

not enough– Aim is to have the right answer at top of the

retrieved set (Ex: in first top 50 answers)

Ranking

Simple but effective methods– Subset informativeness (subset sorting)– Number of matched words (subset sorting)– Length of the candidate definition– Longest Common Subsequence

Some statistics Train sets:

– 50 queries from real users

– 50 queries from a dictionary Test sets:

– 50 queries from real users – 50 queries from a dictionary

Test set 1 Train set 2 Test set 1 Train set 2

# of queries 50 50 50 50

Avg. # of query words

5.66 4.64 9.24 13.98

Max. # of query words

17 12 23 45

Min. # of query words

2 1 1 6

Stem Match (all stems included)

Rank Test set 1 Train set 1 Test set 2 Train set 2

1-10 13 (26%) 18 (36%) 45 (90%) 41 (82%)

11-50 7 (14%) 12 (24%) 2 (4%) 5 (10%)

51-100 4 (8%) 1 (2%) 1 (2%) 2 (4%)

101-300 3 (6%) 3 (6%) 2 (4%) 1 (2%)

301-500 2 (4%) 2 (4%) 0 (0%) 1 (2%)

501-1000 6 (12%) 2 (4%) 0 (0%) 0 (0%)

Over 1000 4 (8%) 2 (4%) 0 (0%) 0 (0%)

Not found 11 (22%) 10 (20%) 0 (0%) 0 (0%)

Stem Match (longest stem included)

Rank Test set 1 Train set 1 Test set 2 Train set 2

1-10 14 (28%) 21 (42%) 46 (92%) 43 (86%)

11-50 5 (10%) 9 (18%) 1 (2%) 5 (10%)

51-100 4 (8%) 1 (2%) 1 (2%) 1 (2%)

101-300 3 (6%) 1 (2%) 2 (4%) 1 (2%)

301-500 2 (4%) 3 (6%) 0 (0%) 0 (0%)

501-1000 5 (10%) 2 (4%) 0 (0%) 0 (0%)

Over 1000 4 (8%) 2 (4%) 0 (0%) 0 (0%)

Not found 13 (26%) 11 (22%) 0 (0%) 0 (0%)

Query Expansion Match (all stems included)

Rank Test set 1 Train set 1 Test set 2 Train set 2

1-10 14 (28%) 24 (48%) 45 (90%) 41 (82%)

11-50 9 (18%) 9 (18%) 2 (4%) 5 (10%)

51-100 3 (6%) 3 (6%) 1 (2%) 2 (4%)

101-300 7 (14%) 2 (4%) 2 (4%) 1 (2%)

301-500 0 (0%) 1 (2%) 0 (0%) 1 (2%)

501-1000 4 (8%) 5 (10%) 0 (0%) 0 (0%)

Over 1000 4 (8%) 1 (2%) 0 (0%) 0 (0%)

Not found 9 (18%) 5 (10%) 0 (0%) 0 (0%)

Query Expansion Match (longest stem included)

Rank Test set 1 Train set 1 Test set 2 Train set 2

1-10 14 (28%) 24 (48%) 41 (82%) 39 (78%)

11-50 6 (12%) 8 (16%) 5 (10%) 6 (12%)

51-100 5 (10%) 5 (10%) 0 (0%) 2 (4%)

101-300 7 (14%) 2 (4%) 0 (0%) 2 (4%)

301-500 1 (2%) 1 (2%) 0 (0%) 0 (0%)

501-1000 5 (10%) 3 (6%) 0 (0%) 0 (0%)

Over 1000 3 (6%) 2 (4%) 1 (2%) 1 (2%)

Not found 9 (18%) 5 (10%) 0 (0%) 0 (0%)

Data fusion

No single method is better than all others in all cases

Merging results from different methods seems to be promising approach for achieving improved performance

Many data fusion methods including min, max, average, sum, weighted average and other linear combination functions

Data Fusion

Weighted Sum

21 *)(_*)(_)( wwscoreQEwwscoreSMwScore

Data Fusionc1= 0.7 (stem match const.)

c2= 0.3 (query expansion const.)

Rank Test set 1 Train set 1

1-10 15 (30%) 22 (44%)

11-50 10 (20%) 14 (28%)

51-100 4 (8%) 1 (2%)

101-300 3 (6%) 2 (4%)

301-500 3 (6%) 0 (0%)

501-1000 5 (10%) 3 (6%)

Over 1000 -- --

Not found 11 (22%) 8 (16%)

Result Summary

Stem Match (longest stem included)• 60% real user queries

• 96% dictionary queries

Query Expansion (all stems included)• 68% real user queries

• 92% dictionary queries

Data Fusion (longest stem included)• 72% real user queries

Conclusion

Meaning to Word system is implemented for Turkish language

Results on unseen data are rather satisfactory Query expansion is better

• Although, it can not find the words for all queries

• 68% of real user queries and 90% of dictionary queries are found in the first 50 results

Data fusion has a better performance • 72% of real user queries are found in first 50% results

THANK YOU !!THANK YOU !!