Word access is an obligatory step in language production. In order to achieve his communicative goal, a speaker/writer needs not only to have something to say, he must also find the corresponding word(s). Yet, knowing a word, i.e. having it stored in a data-base or memory (human mind or electronic device) does not imply that one is able to access it in time. This is a clearly a case where computers (electronic dictionaries) can be of great help.In this paper we present our ideas of how an enhanced electronic dictionary can help people to find the word they are looking for. The yet-to-be-built resource is based on the age-old notion of association: every idea, concept or word is connected. In other words, we assume that people have a highly connected conceptuallexical network in their mind. Finding a word amounts thus to entering the network at any point by giving the word or concept coming to their mind (source word) and then following the links (associations) leading to the word they are looking for(target word).Obviously, in order to allow for this kind of access, the resource has to be built accordingly. This requires at least two things: (a) indexing words by the associations they evoke, (b) identification and labeling of the most frequent/useful associations. This is precisely our goal. Actually, we propose to build an associative network by enriching an existing electronic dictionary (essentially) with (syntagmatic) associations coming from a corpus, representing the average citizen's shared, basic knowledge of the world (encyclopedia). Such an enhanced electronic database resembles in many respects our mental dictionary. Combining the power of computers and the flexibility of the human mind (omnidirectional navigation and quick jumps), it emulates to some extent the latter in its capacity to navigate quickly and efficiently in a large data base.While the notions of association and spreading activation are fairly old, their use to support word access via computer is new. The resource still needs to be built, and this is not a trivial task. We discuss here some of the strategies and problems involved in accomplishing it with the help of people and computers (automation).

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Communication via a natural language requires two fundamental skills: producing ‘text’ (written or spoken) and understanding it. This chapter introduces newcomers to computational approaches to the former—natural language generation (henceforth NLG)—showing some of the theoretical and practical problems that linguists, computer scientists, and psychologists encounter when trying to explain how language production works in machines or in our minds. The chapter first defines and illustrates the abstract components of the NLG task and their distinctive roles in accounting for the coherence and appropriateness of natural texts and then sets out the principal methods that have been developed in the field for building working computational systems. Current problems, new proposals for solutions and potential applications are also briefly characterized.

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Words play a major role in language production, hence finding them is of vital importance, be it for speaking or writing. Words are stored in a dictionary, and the general belief holds, the bigger the better. Yet, to be truly useful the resource should contain not only many entries and a lot of information concerning each one of them, but also adequate means to reveal the stored information. Information access depends crucially on the organization of the data (words) and on the navigational tools. It also depends on the grouping, ranking and indexing of the data, a factor too often overlooked.We will present here some preliminary results, showing how an existing electronic dictionary could be enhanced to support language producers to find the word they are looking for. To this end we have started to build a corpus-based association matrix, composed of target words and access keys (meaning elements, related concepts/words), the two being connected at their intersection in terms of weight and type of link, information used subsequently for grouping, ranking and navigation.

A speaker or writer has to find words for expressing his thoughts. Yet, knowing a word does not guarantee its access. Who hasn't experienced the problem of looking for a word he knows, yet is unable to access (in time) ? Work done by psychologists reveals that people being in this so called tip-of-the-tongue state (TOT) know a lot about the word : meaning, number of syllables, origine, etc. Speakers are generally able to recognize the word, and if they produce an erroneous word, that token shares many things with the target word (initial/final letter/phoneme, part of speech, semantic field, etc.). This being so, one might want to take advantage of the situation and build a program that assists the speaker/writer by revealing the word that's on his/her mind (tongue/pen). Three methods will be presented, the first one being implemented.

No doubt, words play a major role in language production, hence finding them is of vital importance, be it for writing or for speaking (spontaneous discourse production, simultaneous translation). Words are stored in a dictionary, and the general belief holds, the more entries the better. Yet, to be truly useful the resource should contain not only many entries and a lot of information concerning each one of them, but also adequate navigational means to reveal the stored information. Information access depends crucially on the organization of the data (words) and the access keys (meaning/form), two factors largely overlooked. We will present here some ideas of how an existing electronic dictionary could be enhanced to support a speaker/writer to find the word s/he is looking for. To this end we suggest to add to an existing electronic dictionary an index based on the notion of association, i.e. words co-occurring in a well balanced corpus, the latter being supposed to represent the average citizen's knowledge of the world. Before describing our approach, we will briefly take a critical look at

A good dictionary contains not only many entries and a lot of information concerning each one of them, but also adequate means to reveal the stored information. Information access depends crucially on the quality of the index. We will present here some ideas of how a dictionary could be enhanced to support a speaker/writer to find the word s/he is looking for. To this end we suggest to add to an existing electronic resource an index based on the notion of association. We will also present preliminary work of how a subset of such associations, for example, topical associations, can be acquired by filtering a network of lexical co-occurrences extracted from a corpus.