Restructuring the Inter-Lingual Index

Version 1, January 4, 1999, Draft

Wim Peters
Ivonne Peters

NLP group
University of Sheffield

Deliverable 2D004, WP11, 
EuroWordNet, LE4-8328

Identification number
	LE4-8328-2D004	
Type
	Document	
Title
	Restructuring the Inter-Lingual Index
	
Status
	Draft	
Deliverable
	2D004	
Work Package
	WP11	
Task
	n.a.	
Period covered
	March 1998 –October 1998	
Date
	January 4, 1999	
Version
	1	
Number of pages
	21	
Authors
	Wim Peters, Ivon Peters, University of Sheffield	
WP/Task responsible	SHE	
Project contact point	 Piek Vossen
 University of Amsterdam
 Spuistraat 134
 1012 VB Amsterdam
 The Netherlands
 tel. +31 20 525 4669
 fax. +31 20 525 4429
 e-mail: Piek.Vossen@hum.uva.nl	
EC project officer	Ray Hudson	
Status
	Public	
Actual distribution
	Project Consortium
	
Suplementary notes
	n.a.	
Key words	Semantic databases, Information Retrieval, Language Engineering
	
Abstract	This document reports on the restructuring of the Inter-Lingual-Index to provide a more efficient mapping across the wordnets. By adding so-called Composite ILIs that group closely related senses it is possible to connect synsets that cannot be matched otherwise.	
Status of the abstract
	Complete	
Received on	
	
Recipient’s catalogue number		

Executive Summary

This document reports on the restructuring of the Inter-Lingual-Index (ILI) to provide a more efficient mapping across the wordnets. By adding so-called Composite ILIs that group closely related senses it is possible to connect synsets that cannot be matched otherwise.

Within the ILI word-based sense clusters have been identified on the basis of the following criteria:
•	Aspects of the internal hierarchical structure of WordNet1.5
•	Any external resource which has been mapped onto WordNet1.5 

Each criterion has yielded its own specific set of clusters. The types of clusters identified fall into two main types: 
•	Generalization: semantically underspecified clusters of fine-grained sense distinctions having a high level of similarity;
•	Metonymy: cases of systematic polysemy. Recurrent systematic patterns occur where the relation between the senses is not based on coarse similarity but reflects denotational alternations such as organization-building, person-social group, tree-wood, material-product, container-quantity and grinding types such as animal-body covering and foodstuff-flora.

The present ILI clusters are dependent on English lexicalization patterns, and therefore lack descriptive power beyond the linguistic and boundaries of the English language. However, since the ILI is originally based on WordNet1.5 concepts, and the partners effectively map onto WordNet1.5, generalization clusters are expected to provide a valid contribution for all languages. Clusters depending on lexicalization clusters in other languages will be incorporated when the data arrives. The following types of clusters have been distinguished:

Manual
In the first clustering phase sense clusters have been identified manually for the base concepts. Both selected as well as rejected synsets with relevant synset members have been taken into account. For a detailed description of the selection procedure of the base concept set see deliverable D17D34D36. The following criteria and sources of information have been used: lexicographic introspection, gloss overlap, semantic context in WordNet, and synset members. In total 173 clusters have been identified. 

The other types of clusters have been identified automatically by using only WordNet15 by concentrating on its hierarchical structure (clustering strategy 2, 3 and 7), and exploiting information from other resources.

A) automatic on the basis of internal hierarchical structure
2) Sorority
Clusters of this type consist of word senses that share the same hypernym. The terminology comes from WordNet, which applies the same clustering principle in the interface at run time in order to compute similarity of senses. They are identified as instances of generalization. Strictly speaking sister nodes should be incompatible and are not expected to overlap (Cruse (1986) p. 150). In the case of WordNet this prerequisite is valid for a lot of sisters, because they constitute biological taxonomies. In the composite ILI formed by sisters the gloss of the hypernym has been adopted as the gloss for the cluster. Where there was no gloss available in WN15, a new one has been coined.
3) Autohyponymy
The term autohyponymy is used to refer to words whose senses pair as both a hypernym and hyponym. All hyponyms at any level in the chain below are covered. This has resulted in 26 noun and 864 verb clusters. The gloss of the cluster is the gloss of the hypernym. 1037 noun clusters and 864 verb clusters have been identified
B) On the basis of other resources
4) WordNet1.6
The sense numbering and sense distinctions in WordNet16 differ from those in WordNet1.5. Sense distinctions have been changed or taken out on the basis of the semantic tagging experience with Semcor. It is unclear which lexicographic strategies have been applied to change the WordNet16 number of senses and glosses with respect to WordNet15. The WordNet1.6 package provides an automatically created mapping between the two versions only for nouns. The other parts of speech haven't been mapped yet, but the intention has been expressed to do the same for verbs. 108 clusters have been identified. 
5) Levin
Beth Levin (1993) has distinguished 57 main semantic verb classes which determine the syntactic behaviour of their members. (Door 1996) has created links between verbs occurring in Levin’s classes and WordNet1.5 for 2846 verbs involving 5246 senses. 1378 clusters have been created where more than one verb sense maps onto the same Levin class. A total number of 1378 verb clusters have been identified.

Table Of Content
1. Introduction	7
2. The clusters	9
2.1 Manual clustering	9
2.2 Automatic clustering on the basis of the internal hierarchical structure of Wordnet1.5	11
C) Unique beginner combinations	14
2.3 On the basis of other resources	17
2.4. Overview	20
2.5 Comparing the clusters	21
References	21

1. Introduction
Within EuroWordNet the University of Sheffield is responsible for the maintenance of the Interlingual Index (ILI). In the last months much effort has gone into the adaptation of this index in order to accommodate various needs in the area of NLP applications, and to facilitate the linking of the EuroWordNet language-specific wordnets.

The basic idea behind this is the following. WordNet contains many polysemous wordforms. In many cases the level of polysemy is rather high. For instance, around 3000 nouns have 3 senses or more. These sense distinctions are often very fine-grained and show a considerable degree of relatedness.

The adaptation of the ILI consists of the combination of these fine-grained WordNet sense distinctions into larger clusters in order to introduce more systematicity into the ILI and allow a more coarse-grained level of sense distinction. There are several reasons for doing this:

Lexicography and Psychology
Sense distinctions vary widely across lexical resources. Different dictionaries divide the semantic space a word occupies in different ways along the broad lines of homonymy and polysemy. The level of lumping and splitting (Evens 1988), i.e. where the cut-off point is determined between fine-grained and coarse-grained sense distinctions, is strongly influenced by the purpose of the resource, its authoritative character and users (Kilgarriff, to appear). Resulting sense distinctions are often arbitrary (Atkins, 1991) and the many fine-grained sense distinctions that are made in resources are related (Kilgarriff, 1991).

This high level of polysemy is often the result of the expertise of professional lexicographers, who are trained in recognizing fine-grained sense distinctions. Jorgensen (1990) found that subjects unfamiliar with lexicographical practices generally distinguish around 3 senses for very polysemous dictionary entries. Therefore, we argue that clustering related senses in WordNet will yield a more structured lexicon exhibiting a higher degree of psychological reality.

NLP Applications and WordNet
Although WordNet is used as a resource for semantic information in many NLP applications, the sense distinctions in WordNet are too fine-grained for a number of NLP tasks (Kilgarriff [1997], Slator and Wilks [1987]). For instance, in multilingual information retrieval, which is the envisaged application area of EuroWordNet, the amount of noise in query results rises exponentially when the original set of polysemous search words is being extended with synonyms from the synsets to which they belong in WordNet. It is expected that precision and recall will benefit from a reduced level of ambiguity, resulting in more sensible query expansion techniques. By clustering senses into groups the semantic search space is reduced. Moreover, if these coarser sense distinctions follow systematic semantic patterns in the lexicon, it will be possible to apply selective query expansion by solely including synset members from semantically related senses of the original search word. In this way, only relevant word senses are being used as a supplement to word-based indexing, which is in line with the recommendation of Krovetz (1997).

Another area of potential benefit is word sense disambiguation, where algorithms are frequently faced with multiple correct choices, or with a difficult choice between highly related senses (Dolan, 1994). Disambiguation tasks will also benefit from a reduced number of sense candidates resulting from sense clustering.

Compatibility of Language-Specific Wordnets within EuroWordNet
The wordnets in EuroWordNet are treated as autonomous language-specific systems. This makes it possible to build the wordnets relatively independently, which is necessary because the construction takes place at different sites with very different starting points in terms of available resources and tools. Each wordnet is a unique, language-specific structure. To create a multilingual knowledge base, we store the language-specific wordnets in a central lexical database while the equivalent word meanings across the languages are linked to each other in the ILI. The ILI forms the superset of all concepts encountered in all the languages involved and started off as an unstructured list of concepts represented by all WordNet1.5 synsets. Each synset in the monolingual wordnets has at least one equivalence relation with a record in this ILI. Language-specific synsets linked to the same ILI-record should thus be conceptually equivalent across the languages.

Because of the independence of the language specific wordnets in the development stage we need mechanisms to ensure compatibility when the wordnets are integrated. It is here that we find the third, very practical reason for clustering word senses. Typically, many mismatches between the language specific wordnets result from differences in the sense differentiation across the resources. Because of the high level of sense differentiation in WordNet1.5 there is a danger that conceptual equivalences across the wordnets are not linked to exactly the same sense of the English translational equivalent but are instead connected to distinct ILI concepts reflecting different senses of the same word. For example, where two project partners selected the verbal concept break, damage (inflict damage upon) as the translational and conceptual equivalent of their local concept, a third project partner selected both break ("He broke the glass") and break, bust, cause to break (which has no gloss in WordNet1.5). All three senses are so similar that each matching is equally probable, and any selection must be regarded as arbitrary. In order to account for these diverging mappings from local wordnets onto ILI concepts, composite ILI records are introduced that constitute a grouping of ILI concepts. These more global concepts are subsequently used to overcome mismatches. 

A few caveats are in order to conclude this section. A very important fact to bear in mind is that the clustering within the ILI is based on American English lexicalisation patterns, and therefore cannot pretend to have any predictive power beyond the linguistic boundaries of the English language. 

The justification for using the ILI as the place where the clustering is taking place is that the concepts that constitute the ILI have been derived from WordNet1.5 and are therefore American English in nature. Any coarser-grained level of semantic description as well as any observed systematicity between senses will bring the ILI a step closer to being a true interlingua, and will facilitate the mappings between the wordnets and the ILI.

Moreover, in order to keep track of the origin of the clusters, the ILI based clusters will be marked as such in the database. Any new clusters derived from multiple mappings between language specific and the ILI can be integrated into the ILI and marked with information about the source language. The ILI clusters will function as recommendations for clustering for the other partners. Access to the individual WordNet senses constituting the cluster is always possible.

A last thing to mention is that the clustering has been performed on the basis of the sense distinctions for individual words. In the case of generalization, fine-grained sense distinctions for individual words yield conceptual clusters of all the synsets involved. We think this is a reasonable position to take because of the nature of fine-grainedness. In the case of metonymy this is a less felicitous situation, because these regular sense extension possibilities usually do not apply to the other synset members of the synsets the word belongs to.

2. The clusters
The clusters have been created on criteria and strategies listed below. The following have been considered valid criteria:

•	Aspects of the internal hierarchical structure of WordNet1.5
•	Any external resource which has bee mapped onto WordNet1.5 

Each criterion has yielded its own specific set of clusters. Some of them overlap, some of them complement each other (for a brief discussion of the latter case see section 4).

The types of clusters identified fall into two main types: 
•	Generalization: semantically underspecified clusters of fine-grained sense distinctions having a high level of similarity;
•	Metonymy: cases of systematic polysemy. Recurrent systematic patterns occur where the relation between the senses is not based on coarse similarity but reflects denotational alternations such as organization-building, person-social group, tree-wood, material-product, container-quantity and grinding types such as animal-body covering and foodstuff-flora.

2.1 Manual clustering
In the first clustering phase sense clusters have been identified manually for the base concepts. Both selected as well as rejected synsets with relevant synset members have been taken into account . For a detailed description of the selection procedure of the base concept set see deliverable D17D34D36.

The following criteria and sources of information have been used: lexicographic introspection, gloss overlap, semantic context in WordNet, and synset members. In total 173 clusters have been identified:

141 noun clusters: 

108 Generalization 
Examples:

business
1 a commercial or industrial enterprise and the people who constitute it; "he bought his brother's business"; "a small mom-and-pop business"; "a racially integrated business concern"
2 the activity of providing goods and services involving financial and commercial and industrial aspects; "computers are now widely used in business"
3 the principal activity in your life; "he's not in my line of business"
7 the volume of business activity: "business is good today"; "show me where the business was today"

land
3 territory over which rule or control is exercised
4 the territory occupied by a nation; "he returned to the land of his birth"

•	33 Metonymy

association
3 the state of being connected together as in memory or imagination; "his association of his father with being beaten was too strong to break"
4 the process of bringing ideas or events together in memory or imagination; "conditioning is a form of learning by association"

32 verb clusters: 

29 Generalization, 
Example:

bring
1 take something or somebody somewhere; "Bring me the box from the other room"; also metaphorical, as in "This brings me to the main point"
4 bring or fetch; "Get me those books over there, please"; "Could you bring over the wine?"; "The dog fetched the hat"
11 take something to a certain destination; "Take these letters to the boss"; "Bring this present to the birthday boy"

1 metonymy 

take
1 get into one's hands, take physically; "Take a cookie!" "Can you take this bag, please"
27 have with oneself; have on one's person; "She always takes an umbrella"; "I always carry money"; "She packs a gun when she goes into the mountains"

2 diathesis
Example:

play
3 play (music) on an instrument; "The band played all night long"
6 perform music on (a musical instrument); "He plays the flute"; "Can you play on this old recorder?"

This manual clustering process has a number of disadvantages. First, it is very labour intensive. It is impossible to obtain a substantial amount of data. We have limited the scope of manual clustering to the set of base concepts (1024 in total), in which process gained valuable insights into level of fine-grainedness of the WordNet1.5 senses, and the many ways in which these senses can be clustered.

Secondly, it is virtually impossible to use objective criteria for manual clustering, especially when dealing with patterns of generalization. Lexicographers’ intuitions vary, both on Princeton’s side during the compilation of the original WordNet1.5 sense distinctions, and on our side during our attempts to cluster these senses. It is often possible to generate multiple groupings depending on the grain level one adopts for the clusters and the inferences one can make on the basis of the often sparse information in the WordNet glosses. 

2.2 Automatic clustering on the basis of the internal hierarchical structure of Wordnet1.5

The obvious next step was to switch over to automatic clustering. This can be done in two ways:
using only WordNet15 by concentrating on its hierarchical structure (clustering strategies 2,3 and 4) exploiting information from other resources(clustering strategies 5 and 6).

The following two type of clustering are based on the hierarchical structure of WordNet1.5. 

A) Sorority

Clusters of this type consist of word senses that share the same hypernym. The terminology comes from WordNet, which applies the same clustering principle in the interface at run time in order to compute similarity of senses. They are identified as instances of generalization. Strictly speaking sister nodes should be incompatible and are not expected to overlap (Cruse p. 150). In the case of WordNet this prerequisite is valid for a lot of sisters, because they constitute biological taxonomies. However, the distinction between different species is often very small:

bear grass
1 yucca of S US having a clump of basal grasslike leaves and a central stalk with a terminal raceme of small whitish flowers
2 yucca of WC US having a clump of basal grasslike leaves and a central stalk with a terminal raceme of small whitish flowers

Common hypernym: yucca 1: any of several evergreen plants of the genus Yucca having usu tall stout stems and a terminal cluster of white flowers; warmer regions of N America
Often it is very clear that derived clusters are unsuitable for domain specific IR: 

thorax
1 the middle region of the body of an arthropod between the head and the abdomen; in insects it bears the true legs and wings
2 the part of the human body between the neck and the diaphragm or the corresponding part in other vertebrates
3 part of an insect's body that bears the wings and legs

Common hypernym: body part 1: any part of an organism such as an organ or extremity

As natural kind terms the senses above show a high degree of rigidity in their labeling, because they are persistent phenomena in the natural world. Other types displaying such rigid labels are geographical terms and proper names. Clustering in these cases is consequently unintuitive:

St Petersburg
1 a city in W Florida on Tampa Bay
2 a city in the European part of Soviet Russia; former capital of Russia

Common hypernym: city 1: a large and densely populated urban area; may include several independent administrative districts; "Ancient Troy was a great city"

Roosevelt
2 1882-1945
3 1858-1919

Common hypernym: President of the United States 1: the person who holds the office of head of state of the US government; "the President likes to jog every morning". Clustering these will still enable us to perform more semantically underspecified keyword searches. For instance, in the latter example, we will still be able to distinguish the presidents of the USA (captured by this cluster) from the writer (sense 1).

Where the labeling is not as straightforward as in the cases mentioned above (e.g. nominal kind terms) there are a lot of cases in which the sense distinctions determining the sisters are so blurred that one can speak of virtually indistinguishable senses. In these cases the clusters indicate a lexicographically possibly useful reduction of the polysemy of the word in WordNet, apart from the IR advantages. Where sense distinctions are more straightforward the existing polysemy can be retained. Other examples of sorority clusters:

landing 
3 the act of coming down to the earth (or other surface); "the plane made a smooth landing"; "his landing on his feet was catlike"
4 the act of coming to land after a voyage

Common hypernym: arrival 2: the act of arriving at a certain place; "they awaited her arrival"

hall 
2 a large entrance or reception room or area
3 a large room for public gatherings or entertainment
5 the large room of a manor or castle

Common hypernym: room 1: an area within a building enclosed by walls and floor and ceiling; "the rooms were very small but they had a nice view"

graverobber 
1 someone who steals valuables from graves or crypts
2 someone who takes bodies from graves and sells them for anatomical dissection

Common hypernym: thief 1: a criminal who takes property belong to someone else with the intention of keeping it)

inheritance
3 attributes acquired via biological heredity
4 any attribute that passes from parent to offspring

Common hypernym: heredity 2: the total of inherited attributes

kinship
1 a close connection marked by community of interests or similarity in nature or character: "found a natural affinity with the immigrants"; "felt a deep kinship with the other students"; "anthropology's kinship with the humanities"
2 state of relatedness or connection by blood or marriage or adoption

Common hypernym: relation 1: an abstraction belonging to or characteristic of two entities or parts together

skin
1 a natural protective covering of the body; site of the sense of touch
4 body covering of a living animal

Common hypernym: body covering 1: any covering for the body or a body part

In the composite ILI formed by sisters the gloss of the hypernym has been adopted as the gloss for the cluster. Where there was no gloss available in WN15, a new one has been coined.

1037 noun clusters and 864 verb clusters have been identified.

read
1 interpret something that is written or printed; "read the advertisement"; "Have you read Salman Rushdie?"
4 obtain data from magnetic tapes; "This dictionary can be read by the computer"
6 interpret something in a certain way; convey a particular meaning or impression; "I read this address as a satire"; "How should I take this message?"

Common hypernym: interpret 1: assign a meaning to

adjust
2 get used to something
4 "We must adjust to the bad economic situation"

Common hypernym: change 2: undergo a change; become different in essence; losing one's or its original nature; "She changed completely as she grew older"; "The weather changed last night"

B) Autohyponymy

The term autohyponymy (Cruse 1986) is used to refer to words whose senses pair as both a hypernym and hyponym. All hyponyms at any level in the chain below are covered. This has resulted in 26 noun and 864 verb clusters. The gloss of the cluster is the gloss of the hypernym.
Examples:

side 
10 sloping land (especially the slope beside a body of water); "they pulled the canoe up on the bank"; "he sat on the side of the river and watched the currents" 
(hyponym)
11 an elevated geological formation; "he climbed the steep slope"; "the house was built on the side of the mountain" 
(hypernym)

regenerate
5 replace (tissue or a body part) through the formation of new tissue; "The snake regenerated its tail" (hyponym)
8 form or produce anew; "regenerate hatred" 
(hypernym)

discover
1 "I learned that she has two grown-up children"; "I see that you have been promoted"
(hypernym)
4 make a discovery; "She discovered his less appealing characteristics"; "The story is false, so far as I can discover"
(hyponym)

C) Unique beginner combinations

This section describes work which is still ongoing. We are in the process of examining combinations of WordNet1.5 unique beginners that share the same wordform as a hyponym. The hypothesis is that combinations of these unique beginners yield patterns of systematic sense shifts at a very general level of description. Work in this area has been done by Buitelaar who extracted 126 classes displaying various types of systematic polysemy from WordNet1.5. There are 24 unique beginners in the WordNet hierarchy. Examples are:

person-1 		a human being; “there was too much for one person to do”
artefact-1		a man-made object
shape-2		the spatial arrangement of something as distinct from its substance
phenomenon-1	any state or process known through the senses rather than by intuition or reasoning

Then we combine all unique beginners with one another. Below are some examples of combinations together with the number of words that occur as hyponyms of a particular unique beginner combination. 

artefact-group 		249 polysemous words
animal-food 			185 polysemous words
artefact-communication 	349 polysemous words
state-group 			 68 polysemous words
state-location 		 76 polysemous words

The following words occur both in an artefact and a group sense:

institution-1 an organization founded for a specific purpose
institution-2 a building or complex of buildings where an organization for the promotion of some cause is situated

guard-5 a group of men who escort and protect some important person
guard-3 a device designed to prevent injury

menagerie-2 the facility where wild animals are housed for exhibition
menagerie-1 a collection of live animals for study or display

shower-5 a party held by friends to present gifts to a person
shower-1 a plumbing fixture that sprays water over you

type-6 a small block of metal bearing a raised character on one end; produces a printed character when inked and pressed on paper; “he dropped a case of type so they made him pick them up”
type-3 (biol) the taxonomic group whose characteristics are used to define the next higher taxon

unit-6 a combination of interrelated interacting elements designed to work as a coherent entity
unit-2 an organization regarded as part of a larger social group

The above examples show some disadvantages of this approach. Clustering on the basis of particular unique beginner combinations: 

(1)	regularly leads to odd clusters, e.g. shower and type, 
(2)	often clusters words on the basis of the same unique beginner combination that are not semantically similar; groups of these words are thus not homogenous in nature in the sense that they display the same metonymic relation.
(3)	prevents the identification of subgroups that are more semantically similar from being extracted

Improvements: 

1.	extend the UB combinations to groups of 3 or 4, or adopt Corelex (Buitelaar 1998).

2.	find combinations of hypernyms at a more specific level, i.e. using nodes in the WordNet hierarchy that are not as general as the unique beginners. These we call ‘conceptual signposts’.

We have explored the second option. Criteria for conceptual signposts are the folowing:

(1)	particular node pairs must function as a hypernym of at least three words having at least one sense in both UB branches.

(2)	the distance of these senses to the hypernym in terms of node traversal in the WordNet1.5 taxonomy is never greater than 4.

(3)	the combination of nodes must be captured by a description of the relation that exists between them, and this relation should hold for all hyponyms. 

With parameters set at three members minimum and a maximum distance of 4, we find 31 hierarchical node combinations. Below are some examples:

press-3 & publisher-1 (paper, newspaper, magazine)

musical composition-1 & gathering-1 (duet, duette, movement, trio, sextet, sextette, score, suite)

defensive structure-1 & organization-1 (bastion, defense, defence, line of defense)

music-1 & arrangement-2 (arrangement, chorus, duet, duette, folk, line, sextet, sextette, movement, suite, trio)

artefact-1 & unit-1 (block, column, defense, defence, establishment, field, formation, instrumentality, line, menagerie, mound)

instrumentality-3 & organization-1 (battery, cell, instrumentality, machine, merchant, marine, package, parcel, system, unit)

Time permitting, we will investigate the possibility to determine the set of valid conceptual signposts for the UB combinations.

2.3 On the basis of other resources

A) WordNet1.6 (henceforth WN16)

The sense numbering and sense distinctions in WN16 differ from those in WordNet1.5. Sense distinctions have been changed or taken out on the basis of the semantic tagging experience with Semcor.. It is unclear which lexicographic strategies have been applied to change the WN16 number of senses and glosses with respect to WN15. 

The WordNet1.6 package provides an automatically created mapping between the two versions only for nouns. The other parts of speech haven't been mapped yet, but the intention has been expressed to do the same for verbs. The mapping has been performed on the basis of a comparison of:
•	the hypernyms (up to two levels higher) 
•	overlap of synset members. 
•	The lexicographer file assignment (one out of 26 for each noun synset)

The score for each algorithm was added up to a cumulative score with a maximum of 100, and a minimum of 20. Most mappings have a maximum score. There are two mapping tables. The first covers words that are monosemic in both resources, the second words which are polysemous in both. The table containing the links between nouns that are polysemic in both WN15 and 16 consists of 32475 word/sense pairs. For 1161 pairs more than one WN15 word sense is mapped onto the same WN16 word sense, and this yields 552 noun clusters. These clusters express generalization patterns such as:

activity 
WN15
3 The effective concentration of a substance in a chemical system
4 The capacity of a substance to take part in a chemical reaction
WN16 
4 Capacity of a substance to take part in a chemical reaction; "catalytic activity"

Further examination of these 552 clusters has shown that in one cluster there often is a combination of a high score and one or more low scores (20/30). These are generally invalid clusters, because the clustered senses are unrelated. The following example illustrates this:

WN15 senses of 'account' with scores:
100	12: "turned her writing skills to good account"
20	10: a fund that a customer has entrusted to a securities brokerage
20	9: a fund that a customer has entrusted to a bank
30	8: a business or business relationship established to provide for regular services and dealings and other financial transactions

WN16 account 10:
the quality of taking advantage; "she turned her writing skills to good account"

The precise validity of all clusters has been established manually for a subset of the clusters. A score threshold for sense combinations of 80 or higher and 40 or higher yields 108 reasonable clusters for 93 distinct nouns. This number will be further reduced after close manual inspection. The WN16 gloss has been taken as the definition of the composite ILI.

viewer
1 someone who looks at something (especially an exhibition of some kind)
2 a person who is watching television

WN16 sense 1: a close observer; someone who looks at something (such as an exhibition of some kind); "the spectators applauded the performance"; "television viewers"; "sky watchers discovered a new star"

tissue
2 (score 70) a piece of soft absorbent paper (usually two or more thin layers) used as a disposable handkerchief
3 (score 100) thin usu. translucent paper

WN16 sense 2: a soft thin (usually translucent) paper

latitude
1 (score 70) an imaginary line around the earth parallel to the equator
3 (score 90) an imaginary line around the moon

WN16 sense 3: an imaginary line around the Earth parallel to the equator

theme
2 (score 100) a unifying idea that is a recurrent element in a literary or artistic work
5 (score 70) the idea that is expanded in a document or discourse

WN16 sense 2: a unifying idea that is a recurrent element in a literary or artistic work; "it was the usual `boy gets girl'' theme"

B) Levin

Beth Levin (1993) has distinguished 57 main semantic verb classes that determine the syntactic behaviour of their members. Lexicographers at the University of Maryland have created links between verbs occurring in Levin’s classes and WordNet1.5 for 2846 verbs involving 5246 senses (Dorr 1996). 1378 clusters have been created where more than one verb sense maps onto the same Levin class. These classes are often related to verb diathesis alternations, but also phenomena such as different subcategorised prepositional phrases. The intersection of these syntactic patterns defined by semantic class membership on the one hand, and the syntactic patterns of the clustered WN senses on the other, has not been identified exhaustively. For now the verb senses involved have been included as Generalization clusters on the basis of the observation that they show considerable semantic overlap. If time permits, the clustered senses displaying diathesis alternation patterns will be marked after manual inspection. A total number of 1378 verb clusters has been identified.
Examples:

demonstrate
2 establish the validity of something; "This behavior shows his true nature"
3 "This improvement shows the effect of the drug most clearly"

These senses have been linked up to the Levin class of Transfer of a Message (37-1). The semantics of the verb highlight the nature of the message and the way in which it is communicated.

stop
1 put an end to a state or an activity
6 prevent completion
8 "The war ended after three months"

These senses are linked to aspectual begin verbs (55-1) which describe the initiation, termination or continuation of an activity.

lie
1 be located or situated somewhere; occupy a certain position
2 be lying, be prostrate; be in a horizontal position
5 have a place in relation to something else: "The fate of Bosnia lies in the hands of the West"; "The responsibility rests with the Allies"
8 assume a resting position, as on a flat surface

These senses all belong to Levin’s class of Spatial Configuration (47-6) and specify the spatial configuration of an entity with respect to some location. Interestingly, sense 5 has been selected although a metaphorical shift has occurred from the base sense exemplified by senses 1 and 2.

clean
1 make clean by removing dirt, filth, or unwanted substances from; "Clean the stove!"; "The dentist cleaned my teeth"
2 remove unwanted substances from, such as feathers or pits, as of chickens or fruit; "Clean the turkey"
6 remove all contents or possession from, or empty completely; "The boys cleaned the sandwich platters"; "The trees were cleaned of apples by the storm"; deprive wholly of money in a gambling game, robbery, etc.; "The other players cleaned him completely"
7 remove in making clean; "Clean the spots off the rug"
8 as in chemistry
9 remove shells or husks from; "clean grain before milling it"

All these senses belong to Levin’s class of ‘clear’ verbs (10-3). According to Levin they relate to the removal of a substance from a location.

2.4. Overview
The following table lists the number of clusters for each clustering principle and the ranges of add-on ids the composite ILIs have received in the database.

CLUSTER TYPE	NUMBER OF CLUSTERS + POS	WORDS INVOLVED

	OFFSETS INVOLVED	ADD_ON ID	
MANUAL	 142 N
  32 V	106 N
 34 V	312 N
 75 V	1 – 123, 156-199
124 - 155	
SISTER	1037 N
 864 V	1010 N
 778 V	1948 N
1554 V	1001 – 2815
2011 - 2903	
AUTOHYPONYMY	  26 N
 319 V	 27 N
283 V	51 N
568 V	3001 – 3028
3029 - 3352	
WN16	 552 N	 93 N	185 N	3501 - 4052	
LEVIN	1378 V	1251 V	2764 V	5001 - 6691	
TOTAL	1757 N
2593 V	around 4000	around 7000		

2.5 Comparing the clusters
The clusters found by means of the different strategies show a certain amount of overlap. Sometimes identical clusters are obtained by applying different clustering criteria. On other occasions the extracted clusters show partial overlap. If cluster doublings have been found by applying the different strategies, the unique identifiers of the clusters extracted by means of the first strategy applied are preserved, and any new add-on composite ILI which mirrors an already existing one, will be taken out. If clusters show partial overlap they have all been preserved. In this case it is possible to build larger clusters that are a combination of two or more originally extracted clusters. For instance, for the verb 'seize' has the following senses:

1 take hold of; also metaphorically: "Fear seized the prisoners"
2 take without permission
3 "The enemy seized the city"
4 take by legal authority
5 as of land

Senses 2, 3 and 5 have been created on the basis of Levin. The sister criterion yields two clusters, 1/3 and 2/5. A larger generalization cluster consisting of seize 1/2/3 and 5 can result from the combination of these clusters.

References

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Buitelaar, P (1998). Corelex: Systematic Polysemy and Underspecification, Ph.D., Department of  Computer Science, Brandeis University, Boston, U.S.A.

Cruse, D.A. (1986). Lexical Semantics, Cambridge University Press, Cambridge, U.K.

Dolan, W.B., (1994). Word Sense Ambiguation: Clustering Related Senses, COLING, Kyoto, Japan

Dorr, B., The Role of Word Sense Disambiguation in Lexical Acquisition: Predicting Semantics from Syntactic Clues, In: Proceedings of Coling ‘96

Evens, M.W. (ed.) (1988). Relational Models of the Lexicon: Representing knowledge in semantic networks, Cambridge, CUP

Jorgensen, J., (1990). The Psychological Reality of Word Senses, In: Journal of Psycholinguistic  Research vol. 19 no. 3

Kilgarriff, A., (1997) Evaluating Word Sense Disambiguation Programs: Progress Report, In:  Proceedings SALT workshop on Evaluation in Speech and Language Technology, Sheffield, U.K

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Krovetz, R., (1996).Homonymy and Polysemy in Information Retrieval, In: Proceedings ACL97, Madrid, Spain

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Peters, W. Peters, I. and Vossen, P., Automatic Sense Clustering in EuroWordNet In: Proceedings of the First International Conference on Language Resources and Evaluation, Granada 1998