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<Paper uid="C02-1023">
  <Title>A Chart-Parsing Algorithm for Efficient Semantic Analysis</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
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    <Paragraph position="0"> In some contexts, well-formed natural language cannot be expected as input to information or communication systems. In these contexts, the use of grammar-independent input (sequences of uninflected semantic units like e.g. language-independent icons) can be an answer to the users' needs. However, this requires that an intelligent system should be able to interpret this input with reasonable accuracy and in reasonable time. Here we propose a method allowing a purely semantic-based analysis of sequences of semantic units. It uses an algorithm inspired by the idea of &amp;quot;chart parsing&amp;quot; known in Natural Language Processing, which stores intermediate parsing results in order to bring the calculation time down.</Paragraph>
    <Paragraph position="1"> Introduction As the mass of international communication and exchange increases, icons as a mean to cross the language barriers have come through in some specific contexts of use, where language independent symbols are needed (e.g. on some machine command buttons). The renewed interest in iconic communication has given rise to important works in the field of Design (Aicher and Krampen, 1996; Dreyfuss, 1984; Ota, 1993), on reference books on the history and development of the matter (Frutiger, 1991; Liungman, 1995; Sassoon and Gaur, 1997), as well as newer studies in the fields of Human-Computer Interaction and Digital Media (Yazdani and Barker, 2000) or Semiotics (Vaillant, 1999).</Paragraph>
    <Paragraph position="2"> We are here particularly interested in the field of Information Technology. Icons are now used in nearly all possible areas of human computer interaction, even office software or operating systems. However, there are contexts where richer information has to be managed, for instance: Alternative &amp; Augmentative Communication systems designed for the needs of speech or language impaired people, to help them communicate (with icon languages like Minspeak, Bliss, Commun-I-Mage); Second Language Learning systems where learners have a desire to communicate by themselves, but do not master the structures of the target language yet; Cross-Language Information Retrieval systems, with a visual symbolic input.</Paragraph>
    <Paragraph position="3"> In these contexts, the use of icons has many advantages: it makes no assumption about the language competences of the users, allowing impaired users, or users from a different linguistic background (which may not include a good command of one of the major languages involved in research on natural language processing), to access the systems; it may trigger a communication-motivated, implicit learning process, which helps the users to gradually improve their level of literacy in the target language. However, icons suffer from a lack of expressive power to convey ideas, namely, the expression of abstract relations between concepts still requires the use of linguistic communication.</Paragraph>
    <Paragraph position="4"> An approach to tackle this limitation is to try to &amp;quot;analyse&amp;quot; sequences of icons like natural language sentences are parsed, for example. However, icons do not give grammatical information as clues to automatic parsers. Hence, we have defined a method to interpret sequences of icons by implementing the use of &amp;quot;natural&amp;quot; semantic knowledge. This method allows to build knowledge networks from icons as is usually done from text.</Paragraph>
    <Paragraph position="5"> The analysis method that will be presented here is logically equivalent to the parsing of a dependency grammar with no locality constraints. Therefore, the complexity of a fully recursive parsing method grows more than exponentially with the length of the input. This makes the reaction time of the system too long to be acceptable in normal use. We have now defined a new parsing algorithm which stores intermediate results in &amp;quot;charts&amp;quot;, in the way chart parsers (Earley, 1970) do for natural language.</Paragraph>
    <Paragraph position="6"> 1 Description of the problem Assigning a signification to a sequence of information items implies building conceptual relations between them. Human linguistic competence consists in manipulating these dependency relations: when we say that the cat drinks the milk, for example, we perceive that there are well-defined conceptual connections between 'cat', 'drink', and 'milk'--that 'cat' and 'milk' play given roles in a given process.</Paragraph>
    <Paragraph position="7"> Symbolic formalisms in AI (Sowa, 1984) reflect this approach. Linguistic theories have also been developed specifically to give account of these phenomena (Tesniere, 1959; Kunze, 1975; Mel'Vcuk, 1988), and to describe the transition between semantics and various levels of syntactic description: from deep syntactic structures which actually reflect the semantics contents, to the surface structure whereby messages are put into natural language.</Paragraph>
    <Paragraph position="8"> Human natural language reflects these conceptual relations in its messages through a series of linguistic clues. These clues, depending on the particular languages, can consist mainly in word ordering in sentence patterns (&amp;quot;syntactical&amp;quot; clues, e.g. in English, Chinese, or Creole), in word inflection or suffixation (&amp;quot;morphological&amp;quot; clues, e.g. in Russian, Turkish), or in a given blend of both (e.g. in German). Parsers are systems designed to analyse natural language input, on the base of such clues, and to yield a representation of its informational contents.</Paragraph>
    <Paragraph position="10"> ''[The man] [drinks] [the water].'' In contexts where icons have to be used to convey complex meanings, the problem is that morphological clues are of course not available, when at the same time we cannot rely on a precise sentence pattern. null We thus should have to use a parser based on computing the dependencies, such as some which have been written to cope with variable-word-order languages (Covington, 1990). However, since no morphological clue is available either to tell that an icon is, e.g., accusative or dative, we have to rely on semantic knowledge to guide role assignment. In other words, an icon parser has to know that drinking is something generally done by living beings on liquid objects.</Paragraph>
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