Lexical Functions for Ants Based Semantic Analysis Didier Schwab Computer-Aided Translation Unit (UTMK) School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia Email: didier@cs.usm.my Mathieu Lafourcade Équipe TAL Université Montpellier II-LIRMM, Montpellier, France Email: lafourcade@lirmm.fr Index Terms—semantic analysis, lexical functions, conceptual vectors, lexico-semantic networks, Ant colony algorithms Abstract—Semantic analysis (SA) is a central operation in natural language processing. We can consider it as the reso- lution of 5 problems: lexical ambiguity, references, prepositional attachments, interpretative paths and lexical functions instanci- ation. In this article, we show the importance of this last and explain why these tasks should be simultaneously carried out using thematic (conceptual vectors) and lexical (semantico-lexical network) information. We present an ant colony model which fulfill these criteria. We show the feasability of our approach using a small corpus and the contribution of lexical functions for solving the problem. This ant colony model offers new and interesting research perspectives. Many Natural Language Processing applications, like auto- matic summarization, information retrieval or machinal trans- lation, can take advantage of semantic analysis (SA) which consists of, among other things, computing a thematic rep- resentation for the whole text and for its subparts. In our case, thematic information is computed as conceptual vectors which represent ideas and provide a quick estimation if texts, paragraphs, sentences or words are in the same semantic field, i.e. if they share ideas or not. At least five main problems should be solved during a SA. (1) lexical ambiguities (2) references i.e. resolving anaphora and identity referencing ; (3) prepositional attachments i.e. to find the syntactic head to which a prepositional phrase is linked ; (4) interpretation paths which concerns the resolution of compatible ambiguities; (5) the most important for us in this article, instanciation of lexical function (LF). LFs model typical relations between terms and include synonymy, the different types of antonymies, intensification (“strong fear”, “heavy rain”) or the typical relation of instru- ment (knife is the typical instrument of to cut, shovel of to dig). In this article, we show that we need lexical functions to model world knowledge (“Napoleon was an emperor”) or language knowledge (destiny is synonym of fate) and the central role they play both in SA while contributing to the resolution of ambiguities mentioned earlier and also adressing specific problems of individual applications. We will see that their detection in texts require thematic and lexical information. Thematic information is handled using conceptual vectors which allows us to describe ideas contained in any textual segment (document, paragraph, sentence, phrase, . . . ) . Lexical information is addressed using a lexical network. Thus, our objective is to solve the five phenomena using a semantic lexical base whose lexical objects are linked to each others by typical relations and associated with conceptual vectors describing ideas they convey. Usually, resolution of these phenomena are done separatly. Thus, anaphora resolution, prepositional attchment problem and especially lexical disambiguation are independently stud- ied. However, this is not the approach we adopt here. Instead, our work is based on the reasonable assumption that these ambiguities are often interdependent and that it would be advantageous to undertake these tasks in a holistic way. A way to holisticly deal with these various problems is to use a technique resulting from the distributed artificial intelligence, meta-heuristic of ant colony algorithms. Inspired by the collective behavior of biological ants, these algorithms are used to resolve difficult problems, in particular those related to graphs (TSP, partitionings, . . . ) and are used in operational research or to solve network routings problems. Ant colony algorithms are used in a different way for SA. It is not a method among others to solve a problem but rather a method which allows the simultaneous and interdependent resolution of these various tasks. Each ant caste corresponds to a heuristic which helps to solve a particular problem (in the model presented, detection of a particular lexical function) and has a behaviour influenced in part by the other ant activities. The environment is made up of both the text morpho-syntactic tree and a lexical network which contains typical relations between terms. We have one nest for each word meaning (acceptions) which competes during resource foraging. Ants build bridges between compatible acceptions which can be considered as sentence interpretations. We demonstrate the efficiency of this approach in order to solve SA problems. I. SEMANTIC ANALYSIS (SA) Five semantic phenomena can be solved during a SA: (1) Lexical Ambiguity : Words can have several meanings. This well-known phenomenon leads to one of the most im- portant problems in NLP, lexical disambiguation (also often called Word Sense Disambiguation). It involves selecting the most appropriate acception of each word in the text. We define an acception as a particular meaning of a lexical item acknowledged and recognized by usage. It is a semantic unit acceptable in a given language. For example, we can consider that mouse has three acceptions: the nouns for the computer device and for the rodent and the verb for the hunt of the animal. Contrary to lexical items, acceptions