WO2004015592A1

WO2004015592A1 - Method of and apparatus for receiving an illustration of text

Info

Publication number: WO2004015592A1
Application number: PCT/JP2003/009416
Authority: WO
Inventors: Philip Glenny Edmonds; Victor Poznanski
Original assignee: Sharp Kabushiki Kaisha
Priority date: 2002-08-07
Filing date: 2003-07-24
Publication date: 2004-02-19
Also published as: AU2003250534A1; GB2391648A; GB0218297D0

Abstract

A method is provided for retrieving an illustration of a segment of text from a database of illustrations. One or more keywords are identified in the segment and, from the keywords, a set of related terms is derived. The related terms are clustered into subsets such that the terms in each subset have a similar meaning and terms in different subsets have different meanings. A query is formed from each subset and comprises at least some of the terms of the subset optionally together with one or more of the associated keywords. The queries are issued to a search engine for the database of illustrations and illustrations retrieved from the database are presented in any appropriate way to a user for selection so as to illustrate the segment of text.

Description

DESCRIPTION

METHOD OF AND APPARATUS FOR RETRIEVING AN ILLUSTRATION OF

TEXT

TECHNICAL FIELD

The present invention relates to a method of and an apparatus for retrieving an illustration of a segment of text from a database of illustrations. The invention also relates to a computer program for programming a computer to perform such a method, a storage medium containing such a program, and a computer programmed by such a program. Such techniques may be used, for example, to provide assistance in the authoring of multimedia documents . For example, this technique may be used for automatically finding a set of multimedia items which may be suggested to a user in order to illustrate a short segment of text, such as an SMS text message on a mobile terminal or handset, based on the meaning of the text.

BACKGROUND ART

Multimedia documents consist of content from more than one medium-for example text and graphics or text and sound. Multimedia documents can be amuchmore powerful (and fun) way to communicate than using plain text documents. Consequently, multimedia authoring is becoming increasingly popular, especially given the increasingly better capabilities of computing systems for generating, playing, and transmitting multimedia data. Multimedia messaging services (MMS) on mobile phone networks are being touted as one of the most important upcoming services on next-generation mobile phone networks. However, authoring multimedia documents is nevertheless difficult. It can take time and skill in browsing/searching a large database of multimedia content (images, icons, graphics, animations, video clips, sound clips, etc. ) to find the right multimedia items that will succinctly convey a message.

On a mobile terminal, composing multimedia messages is particularly difficult because of the small screen (which hampers browsing) and the possibly high bandwidth-costs associated with downloading many possibly irrelevant multimedia items. There is a need to help the user by analysing input text in order to find quickly a small set of multimedia items related to the input text that can then be suggestedas possible illustrations of the text . However, there is no known method that can always interpret exactly what the user intended by a text or message.

US 6 021 412 discloses a method of illustrating a concept expressed by a portion of text in a document that has the steps of (a) identifying a "concept" in the document that corresponds to at least one graphic image in a database, (b) showing the corresponding graphic images to the user, and (c) allowing the user to select an image to be inserted into the document. The step (a) identifies the concept by using a static (i.e., preset) list of all words used as annotations of images . The list is consulted for each word in the document. If a word is in the static list, it is considered to be a concept, and all images annotated by the word are suggested to the user. This method also includes an extra step of mapping from the word to its synonyms , which are then looked up in the static list.

One problemis that , if there is more than onematching image, then there is no way to rank the images in terms of howrelatedtheyare to a concept . This wouldbecome a serious problem for a very large multimedia database where hundreds of multimedia items might be annotated with the same concept word. A secondproblem is that concept words can be ambiguous . For example, the word "party" can refer to a political party or to a festive occasion. There is no provision to identify the different interpretations of a word. Finally, there is also no provision for handling multiple input words simultaneously. This would help both in the disambiguation problem above (for example, "birthday party" clearly disambiguates "party") and in finding images that relate to more than one word, and thus in ranking the images. This technique can handle only a single word/concept at a time.

US 5 873 107 discloses a text authoring system comprising, among other components, a keyword extractor, an information source (e.g., a collection of documents), and a search engine for querying the information source. The method is to extract a single keyword from the text being authored (based on words of the text and/or based on the information source). Once a keyword is found, it is issued to the search engine as a query. The results of the query are displayed in a separate pane of the user interface.

The system is dynamic in that it runs continuously while one is authoring text. However, it cannot handle ambiguous words and can use only a single keyword at a time. US Patent Application 20010049596 is FunMail's core patent for their "Relevancy Engine" and "Rendering Engine" . It is a method of turning a plain text (a short message) into an animated sequence. The method is to 1) generate a set of concepts fromatext string; 2) selectvarious animation components associated with the concepts (including stories , backgrounds, characters, props, music, and so on); and 3) compose an animation out of the individual components . The method for stage 1 is to filter out unimportant words (using a list of unimportant words) and to map the remaining words and/or strings of words (called phrases) to concepts using a table (or "library") that maps words/phrases to concepts. Plural words are first converted to their singular forms . The resulting concepts are prioritised using an undisclosed method. The set of concepts is a separate domain from the set of words (e.g., the phrase "hot dog" might map to the concept FOOD) . Stage 2 is accomplished by finding animation components labelled with the top priority concept using a simple table lookup procedure .

This technique provides no method for ranking animation components should more than one be associated with a concept. Similarly, a method is provided for mapping from just a single concept to animation components. A method for using multiple concepts at once is not provided. This technique also suffers from the same problem of misinterpretation when a word/phrase can have several different concepts as interpretations .

US Patent 5 926 811 discloses a method of query expansion that uses apurpose-built "statistical thesaurus" . Each term has a record in the thesaurus consisting of terms relatedbyco-occurrenceinaparticulardocument collection. Pre-existing methods are used for determining strength of co-occurrence. Each document collection has a separate thesaurus. To do query expansion, the system indexes the thesaurus as if it were a standard document collection and then issues queries to the thesaurus consisting of terms in the user's query. The resulting terms are used to expand the query, which is then submitted to a search engine indexed on the collection itself.

GB 2 330 930 discloses a system for automatically grouping documents together by appearance. The system automatically determines the visual characteristics of document images and collects documents together according to the relative similarity of their document images . A text string or image feature is used to search a database and. where several documents are returned, they are grouped into clusters based on similarity of extracted feature.

WO 97/34242 and US 5 469 355 disclose methods of generating thesauri. In each case and as disclosed in GB

2330930 , clustering of results is performed after individual queries have been used to interrogate an appropriate response.

DISCLOSURE OF THE INVENTION

According to a first aspect of the invention, there is providedamethodof retrievingan illustration of a segment of text from a database of illustrations, comprising: (a) identifying at least one keyword from the segment;

(b) derivingfromthe at least onekeyworda set of related terms ;

(c) clustering the set of related terms in to at least one subset, the or each of which comprises a plurality of the related terms of similar meaning;

(d) forming from the or each subset a query comprising at least one of the terms of the subset;

(e) issuing the or each query to a search engine for the database; and (f) presenting at least one retrieved illustration.

The or each illustration may comprise at least one of an image, an icon, a graphic, an animation, a text, a video clip and a sound clip.

Each related termmay comprise at least one of aword, a plurality of words, a phrase and an image. The or each word may be in its base form. The or each word may comprise a noun, a verb or an adjective.

The or each query may comprise at least one of the at least one keyword.

The segment of text may comprise at least one text abbreviation and the step (a) may comprise mapping the at least one text abbreviation to a word.

The step (a) may comprise reducing the at least one keyword to its base form.

The step (a) may comprise labelling the at least one keyword with a part of speech tag. The step (a) may comprise identifying any phrase.

The step (c) may comprise clustering the set into a plurality of subsets with the terms in different subsets having different meanings .

The related terms may be related by at least one of co-occurrence, similarity of meaning and word association.

Each related term may be associated with a score representing its relatedness to the at least one keyword. The step (d) may comprise forming the query from the related terms of highest scores of the or each subset. The step (c) may comprise ranking the or each subset in accordance with the scores of the terms thereof.

The step (d) may comprise forming a plurality of queries and the step (f) may comprise merging lists of retrieved illustrations corresponding to the queries.

The step (f) may comprise presenting the at least one retrieved illustration for user selection.

Themethodmaycomprisethefurthersteps of selecting at least one retrieved illustration and forming a message comprising the segment of text and the at least one retrieved illustration.

According to a second aspect of the invention, there is provided a computer program for programming a computer to perform a method according to the first aspect of the invention.

According to a third aspect of the invention, there is provided a storage medium containing a program according to the second aspect of the invention.

According to a fourth aspect of the invention, there is provided a computer programmed by a program according to the second aspect of the invention.

According to a fifth aspect of the invention, there is provided an apparatus for retrieving an illustration of a segment of text from a database of illustrations, characterised by comprising: means for identifying at least one keyword from the segment; means for deriving from the at least one keyword a set of related terms; means for clustering the set of related terms into at least one subset , the or each of which comprises aplurality of the related terms of similar meaning; means for forming from the or each subset a query comprising at least one of the terms of the subset; means for issuing the or each query to a search engine for the database; and means for presenting at least one retrieved illustration.

The apparatus may comprise a mobile telephone.

The apparatus may comprise a personal digital assistant.

The apparatus may comprise a multimedia search engine .

It is thus possible to provide a technique which automatically links text to suitable illustrations. This technique can provide a wider and more structured selection of multimedia content than known techniques by identifying various possible interpretations of an input message. Account can be taken of several keywords in the input message and these may then be expanded and clustered to identify different possible interpretations of the message. The result of submitting queries to an information retrieval system can be ranked according to relevancy to the different interpretations of the input message . Analysis of the words of the input message to identify their parts of speech may also be performed so as to reduce the possibility of misinterpretation.

This technique performs clustering before searching is performed and differs from the prior art (in particular the published documents described hereinbefore), in which only clustering of results after searching is performed. This may be thought of as automatically "interpreting" the query instead of only "interpreting" the result. Forming queries following clustering results in multiple searches which provide better results than for known techniques . For example, more appropriate and/or interesting illustrations can be found.

BRIEF DESCRIPTION OF THE DRAWINGS The invention will be further described, by way of example, with reference to the accompanying drawings, in which:

Figure 1 illustrates a method of retrieving an illustration of a segment of text constituting an embodiment of the invention; and

Figure 2 illustrates an apparatus for performing the method of Figure 1 and constituting an embodiment of the invention.

BEST MODE FOR CARRYING OUT THE INVENTION

One embodiment of the invention takes a short segment of text (e.g., 1-30 words) as input. The input could be a message intended for a recipient in a multimedia messaging service on mobile terminals/phones , or it could be a sentence in amultimedia presentation that an author is writing, among others .

The method involves the following steps 1 to 8 as illustrated in Figure 1 : 1. Receive an input message entered by a user.

2. Identify one or more most important keywords in the message. Optionally pre-analyse the input message by mapping from "texting" abbreviations to words such as real words , by reducing words to their base forms, by labelling the words with part-of-speech tags, and/or by identifying phrases such as common phrases .

3. For the combined set of keywords , find a scored and ranked set of N terms related to any of the keywords or any combination of the keywords .

4. Cluster the set of N related terms into M subsets such that the subsets contain terms similar (in meaning) to each other, but different in meaning from the terms in the other subsets . This gives several clusters that represent different ' interpretations ' of the keywords . Optionally rank the clusters according to the scores of terms in the clusters .

5. From each cluster, form a query from (at least) its K top terms and optionally the keywords. 6. Issue eachqueryto a searchengine that searches various databases of multimedia content .

7. Optionally merge the result lists of the individual searches into one results list.

8. Suggest one or more items of multimedia content from each results list to the user (the user selects one or more of them for inclusion in the multimedia message or presentation) .

Figure 2 illustrates anexampleofapparatus suitable for performing such amethod. The apparatus comprises a data processor 10, whichmaybe of thehard-wiredtype orcontrolled by firmware or may be of the programmable type. Where the data processor is of the type which is controlled by an installed program, it comprises a read-only memory (ROM) 11 for storing the software for controlling the operation of the data processor. Also, where appropriate, random access memory (RAM) 12 may be provided for temporary storage and may be volatile and/or non-volatile.

The data processor 10 may comprise a computer such as a personal computer or personal digital assistant (PDA) . However, the data processor and associated blocks may be provided in or embodied by other types of equipment. For example, the data processor 10 may comprise part of a mobile or ("cellular") telephone.

The data processor 10 is connected to a keyboard 13 to permit manual entry of data, instructions and the like.

The data processor 10 is also connected to a display 14 for displaying output information. Other forms of output devices may also be provided, such as a printer.

The data processor 10 is shown as being connected to a modem 15 and a wireless interface 16. Although both may be provided, only one of the modem 15 and the wireless interface 16 may be provided. Both devices permit two-way communication, for example via the internet in the case of the modem 15 and via a cellular telephone network in the case of the wireless interface 16.

The modem 15 and/or the wireless interface 16 communicate with an information retrieval system 17. The system 17 may be disposed locally or remotely. The system 17 comprises a search engine with access to storage 19 in any suitable form containing one or more databases of multimedia content.

In a typical example where the apparatus comprises a mobile telephone communicating via a cellular telephone network with the internet, the telephone comprises the data processor 10, its keyboard or keypad 13 and its display 14 together with the wireless interface 16 for cellular telephone communication. The information retrieval system 17 is an internet resource.

In this example, a user enters a message by means of the keyboard 13 and the data processor 10 performs the steps 2 to 8. The step 6 includes sending the query via the wireless interface 16 to the search engine 18, which interrogates the database 19 and sends results back via the interface 16 to the data processor 10. The step 8 includes displaying results on the display 14 and the keyboard 13 and the display 14 may be used interactively to examine and select one or more items of multimedia content for illustrating the message.

The method illustrated in Figure 1 is described in more detail hereinafter. In the step 1 , a user enters a short text part of amultimedia message in the form of a text string. The text string can be any relatively short (1-30+ words) string of freeform text, grammatical or not. It can even include ' texting' -style abbreviations. The text string may be entered on a mobile terminal such as a mobile phone or PDA (personal digital assistant) . As an alternative, it may be entered into an application on a standard computer.

The step 2 identifies one or more keywords , which are the most important words or terms in the input string. Various techniques for this are well known in the literature, and any suitable technique may be used. For example, one technique is to choose the terms with the greatest frequency in a large corpus of text (after ignoring very common and "meaningless" words such as "the"). Techniques developed for information retrieval for term weighting may also be used. Term weighting techniques, such as inverse document frequency (IDF) , compute a weight based on the distribution of a term within a corpus of documents. For example, the importance of a term t could be calculated as log(N/df) , where N is the number of documents in the collection and dfis the number of documents containing the term t. However, in the preferred embodiment , RIDF (residual inverse document frequency) is used. Here the importance of t is log(N/df) - log (l/(l -e^~af/tl ) ) , ^' where cf is the number of times t is usedin thewhole collection, as disclosedinManning & Schutze "Foundations of Statistical Natural Language Processing", MIT Press (1999; Chap 15).

After assigning an importance score to each term in the input message, one or more of the top scoring terms are selected as keywords .

Optionally, the input string can be pre-analysed before computing importance scores. The pre-analysis can include normalisation or analysis or both using techniques such as (but not limited to) the following:

■ Normalisation: Map words to normal forms including converting 'texting' -style abbreviations (e.g., "c u

18r" to "see you later") or converting words to their base forms (e.g., "running" to "run", "apples" to "apple", "quickly" to "quick".).

■ Analysis: Label thewordswithpart-of-speechtags using morphological analysis, a part-of-speech tagger, or other methods. Any set of labels/tags can be used.

Inapreferredembodiment , allof theabove techniques are used and morphological analysis is used to identify part-of-speech tags of verbs, nouns, and adjectives.

In the step 3 , terms related to the keywords are found and ranked. This step builds a list of Nterms that are most relatedto thekeywords . Terms canbewords, phrases, images, or any other type of term used in annotating the multimedia collection. In a preferred embodiment, related terms are base-form nouns, verbs, and adjectives.

First, foreachkeyword, themethodgenerates aranked and scored list of terms related to it . Various techniques for finding related terms are also well known in the research literature. For example, it is possible to use words that are relatedby co-occurrence (e.g. , words that co-occur often in sentences such as "dog" and "walk") in a large corpus using any of a number of scoring measures . Such measures include mutual information scores and t-scores as described in Manning & Sσhutze (1999; Chap 5). It is also possible to use a " statistical thesaurus ' , which links words by similarity of usage in a large corpus, for example, as disclosed in Lin, Dekang. 1998. Automatic Retrieval and Clustering of SimilarWords . In Proceedings of the 36^th Annual Meeting of the Association for Computational Linguistics (COLING-ACL '98), pp. 768-773. Hand-built lists of words such as synonym dictionaries and word association lists may also be used. In a preferred embodiment, a technique which merges lists generated from any number of other techniques into a single list is used. Such a technique is disclosed in our co-pending British patent application No .0218296.2.

Second, the ranked and scored related-terms lists for each of the keywords are joined into a single list by summing the scores for terms that occur in more than one list. Thus, the more keywords that a term is related to the higher it will be ranked in the combined list. Finally, the top -V words by score are extracted from the list and used in the next step.

In the step 4, the related words are clustered by similarity to each other and to the keywords . The purpose of this step is to generate one or more ^* interpretations ' of the input message, each of which is representedby a cluster of related terms . A clustering technique is used to generate Mclusters out of the Nrelated words such that each cluster contains terms similar to each other and dissimilar to terms in other clusters . In order for the clusters to represent coherent 'interpretations' of the input string, the measure of similarity must be similarity of meaning. In a preferred embodiment, similarity of meaning is computed as similarity of usage in a large corpus. For example, in a sufficiently large corpus of English, "cat" is more similar in usage to "dog" than it is to "appointment" by virtue of the other words that each of "dog", "cat", and "appointment" tend to co-occur with. Lin (1998) describes a method based on comparing tables of significantly co-occurring words. Other techniques for computing similarity of meaning also exist; for example, as disclosed in Resnik, Philip. 1999. Semantic Similarity in a taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language. In Journal of Artificial Intelligence Research, vol. 11, pp. 95-130.

Of course, many clustering algorithms are also known in the literature. In principle, any clustering algorithm may be used. In one embodiment, the following algorithm, based on an algorithm given by Lin (1998), is used:

■ Initialise a similarity tree to consist of a root w₀, where w₀ is one (or more) of the keywords (of step 2) .

■ For i=l, 2,...,N, insert w_ as a child of wj such that W_j is the most similar term to nr_ in { w₀, w_lt..._r w_±-_ } , where . Wι,..., w_N ^~) is the set of related terms. ■ This process will have formed M subtrees of the root W_o: the sets of terms inthe//subtrees eachformacluster.

A final optional step is to rank the clusters . Each cluster can be scored by using the scores of the related terms in the clusters (as computed in step 3 ) . In a preferred embodiment, the score of a cluster is the average score of the terms in the cluster, but other methods may be considered such as using the maximum score over terms in the cluster.

In order to find multimedia content relevant to each interpretation, the step 5 formulates a query from both the terms in each cluster and the keywords of the message. The form of the query, taking in its structure, operators, and in query terms, is determined by the information retrieval (IR) system. In a preferred embodiment, the IR system uses a probabilistic matcher and a query is formulated as a list of the top K terms in the cluster, by score, and all of the keywords. Optionally, term weights are assigned. Related terms are assigned a weight twice that of the keywords. The terms themselves might have to be converted to a form that the IR engine is expecting. For example, if the terms have an associated part-of-speech tag, the tag is removed if the IR engine does not match based on tags . In other embodiments , a Boolean or ranked Boolean matcher may be used. Examples of other techniques include information retrieval models and query formulation techniques given by Baeza-Yates, Ricardo and Berthier Ribeiro-Neto.1999; Modern Information Retrieval . Addison Wesley (chaps. 2 and 4.).

In the step 6, each query is issued to a multimedia IR search engine . The IR engine takes each query and returns a ranked list of multimedia content based on matching the query against annotations of multimedia content. The annotations can take any form that is indexable by the IR engine. In a preferred embodiment, the annotations are freeform text strings and the IR engine uses stemming. For each query, the IR engine returns a scored list of results, where each result is an item of multimedia content and the score is based on how well the annotation of the item matches the query (based on the IR engine matching formula) .

In the optional step 7 , all of the results lists from the separate searches of the step 6 are merged into a single results list. The results are merged by taking into account the scores assigned by the IR engine in the step 6. Any techniques for merging results can be used and a survey of such techniques is given in Callan, Jamie.2000. Distributed Information Retrieval . In Advances in In forma tion Re trieval : Recent Research from the Center for Intelligent Information Retrieval , pp. 127-150. In a preferred embodiment, all of the results are merged and low-scoring results and duplicates are removed.

Finally, one ormore items of multimedia content from each results list are suggested to the user in the step 8. The number of items suggested from each list and the number of lists presented depend on the output characteristics of the system the user is using. For mobile terminals with very small screens or with high bandwidth-costs, such as mobile phones or PDAs, just one or two items from each list may be presented. If the interpretations are ranked, then items from only the first few results lists may be presented. The lists may even be presented one at a time, allowing the user to move to the next list.

For larger screens and/or lower bandwidth-costs, a more sophisticated user interface may be provided. For example, a user interface on a mobile phone may first show the interpretations as text options . After the user chooses an option, a first multimedia item for the interpretation is downloaded to the phone and shown to the user. The user then chooses the item to include in the message or moves to a seconditemor to adifferent interpretation. On a larger screen, such as a PDA, the interpretations could be shown as text with the top interpretation 'expanded' to show one or more multimedia items . The user can easily select a different interpretation, which would 'close' the first interpretation and 'expand' the newly chosen one.

For example, if the input message were "It's really cold today", the keywords "cold" and "today" might be identified in the step 2. The step 3 might then find the following list of words related to both "cold" and "today": "{ice, snow, water, bath, temperature, weather, air winter, morning, pipe, day}". Step 4 might identify the following clusters of keywords : "{ice, winter, snow}", "{water, bath, pipe}", "{day, morning}", "{temperature, weather, air}", which represents different "interpretations" of the input message . In step 5 , aqueryis formedfromeachof theclusters and the keywords. In this case, choosing the top two words of each cluster and assuming that in this embodiment queries are formed as strings of words, the four queries are "cold today ice winter" , "cold today water bath" , "cold today day morning", "cold today temperature weather". Step 6 issues each of the queries in turn to a search engine, which would return a set of multimedia illustrations for each query. Steps 7 and 8 arrange and output these results .

This system has applications in at least multimedia messaging services (MMS), multimedia authoring, and multimedia search engines . A preferred application is as a component of a multimedia-message composition tool for a mobile terminal (mobile phone, PDA, etc.) for creating multimedia messages to be sent by a multimedia messaging service (MMS). In current short messaging services (SMS), the user can easily and quickly create and send text-only messages. This system can extend this service to MMS by allowing the user to easily, quickly, and cheaply create and send multimedia messages. This system can help because it automatically analyses a short text message and suggests severalmultimedia items that couldappropriately illustrate the message. Also, since related words are used, the clips will not necessarily be pictures that are explicitly describedbythemessage (as inregular image searchengines) , but the pictures could be metaphorically related or form puns on the input message. This leads to a fun service.

Other fun messaging services involving games are possible. For instance, given a message such as "hot dog", two multimedia items can be suggested, one for each word, rather than suggesting a single multimedia item for both words (in this case a picture of the sun followed by a picture of a poodle could be suggested, rather than a picture of a hot dog) . Automatic illustration can be used in a more general game of automatically generating rebuses from a text message.

Another game involves communities ofmessage senders . Automatic illustration can be used by such communities to generate run-on stories. The first sender creates the first sentence of the story and chooses one of the suggested illustrations . The first sender sends themessage to asecond person. That personwrites the second sentence of the story, illustrates it, and sends it on to a third person, and so on. The texts of the message may be shown or not. At the end, the users may browse the whole story.

INDUSTRIAL APPLICABILITY

In multimedia search engines , the text annotations on images are often short and basic. It can be difficult to know which keywords ought to be used in a query to find the desired image. This system can help the user, because it expands the query with a wide selection of related words , which in turn finds more related images . The system also helps the user by structuring the resulting images into clusters associatedwith different possible interpretations of the user's initial query. Thus it should be easier and more quick to browse the results . This facility can be used in plain multimedia search engines, or within multimedia composition applications (such as Microsoft Powerpoint).

Claims

1. A method of retrieving an illustration of a segment of text from a database of illustrations, comprising the steps of:

(a) identifying at least one keyword from said segment;

(b) deriving from said at least one keyword a set of related terms;

(c) clustering said set of related terms into at least one subset , eachofwhichcomprises apluralityof saidrelated terms of similar meaning;

(d) forming from each said subset a query comprising at least one of said terms of said subset;

(e) issuing each said query to a search engine for said database; and

(f) presenting at least one retrieved illustration.

2. A method as claimed in claim 1, in which each said illustration comprises at least one of an image, an icon, a graphic, an animation, a text, a video clip and a sound clip.

3. A method as claimed in claim 1 , in which each said related term comprises at least one of a word, a plurality of words, a phrase and an image.

4. A method as claimed in claim 3 , in which each said word is in its base form.

5. A method as claimed in claim 3 , in which each said word comprises one of a noun, a verb and an adjective.

6. A method as claimed in claim 1 , in which each said query comprises at least one of said at least one keywords .

7. A method as claimed in claim 1 , in which said segment of text comprises at least one text abbreviation and the step (a) comprises mapping saidat least one text abbreviation to a word.

8. A method as claimed in claim 1, in which said step (a) comprises reducing said at least one keyword to its base form.

9. A method as claimed in claim 1, in which said step (a) comprises labelling said at least one keyword with a part of speech tag .

10. A method as claimed in claim 1, in which said step (a) comprises identifying a phrase.

11. A method as claimed in claim 1, in which said step (c) comprises clustering said set into a plurality of subsets with said terms in different ones of said subsets having different meanings .

12. A method as claimed in claim 1, in which said related terms are relatedbyat least one of co-occurrence, similarity of meaning and word association.

13. A method as claimed in claim 1 , in which each said related term is associated with a score representing its relatedness to said at least one keyword.

14. A method as claimed in claim 13, in which the step (d) comprises forming said query from said related terms of highest scores of each said subset.

15. Amethodas claimed in 13 , inwhich said step (c) comprises ranking each said subset in accordance with said scores of said terms thereof.

16. A method as claimed in claim 1, in which said step (d) comprises forming a plurality of queries and said step (f) comprises merging lists of retrieved illustrations corresponding to said queries.

17. A method as claimed in claim 1, in which said step (f) comprisespresenting saidat least oneretrievedillustration for user selection.

18. A method as claimed in claim 1, comprising the further steps of: selecting at least one said retrieved illustration; and forming a message comprising said segment of text and said selectedat least one retrievedillustration.

19. A computer program for programming a computer to perform a method as claimed in claim 1.

20. A carrier medium containing a program as claimed in claim 19.

21. A carrier medium as claimed in claim 20, comprising a storage medium.

22. A computer programmed by a program as claimed in claim 19.

23. An apparatus for retrieving an illustration of a segment of text from a database of illustrations, characterised by comprising: means for identifying at least one keyword from said segment ; means for deriving from said at least one keyword a set of related terms; means for clustering said set of related terms into at least one subset, each of which comprises a plurality of said related terms of similar meaning; means for forming from each said subset a query comprising at least one of said terms of said subset; means for issuing each said query to a search engine for said database; and means for presenting at least one retrieved illustration.

24. An apparatus as claimed in claim 23, comprising a mobile telephone .

25. An apparatus as claimed in claim 23 , comprising apersonal digital assistant.

26. An apparatus as claimed in claim 23, comprising a multimedia search engine.