Intelligent Information Access by Exploiting Lexical Knowledge and Machine Learning

Algorithms designed to support users in retrieving relevant information base their relevance computations on user profiles, in which representations of the users interests are maintained. This paper focuses on the use of supervised machine learning techniques to induce user profiles for Intelligent Information Access. The access must be personalized by profiles allowing users to retrieve information on the basis of conceptual content. To address this issue, we propose a method to learn sense-based user profiles based on WordNet, a lexical database. I. WORDNET-BASED DOCUMENT REPRESENTATION Personalization is an important method for digital libraries to take a more active role in dynamically tailoring its information and service offer to individuals. Novel solutions for personalized information access exploit machine learning algorithms to induce a structured model of a user’s interests, referred to as user profile, from text documents. We propose a strategy to induce semantic user profiles in which keywords are replaced with their meanings, defined in lexicons or ontologies. We adopted WordNet [1] as a reference lexicon. As regards document representation, we propose a model called bag of synsets (BOS), in which the senses corresponding to the words in the documents are considered as features. The adopted sense repository is WordNet (version 1.7.1). In WordNet, nouns, verbs, adjectives and adverbs are organized into synsets (synonym sets), each representing one underlying lexical concept. The problem of determining which of the senses of an ambiguous word is invoked in a particular use of the word (word sense disambiguation, WSD) has to be solved [2]. We propose a WSD strategy based on the idea that semantic similarity between synsets a and b is inversely proportional to the distance between them in the WordNet IS-A hierarchy, measured by the number of nodes in the path from a to b. More details about the WSD procedure are reported in [3]. A document is mapped into a list of WordNet synsets according to the following three rules: a) each monosemous word w in a slot of a document d is mapped into the corresponding WordNet synset; b) for each pair of words 〈noun, noun〉 or 〈adjective, noun〉, a search in WordNet is made in order to verify if at least one synset exists for the bigram 〈w1, w2〉. In the positive case, the WSD strategy is applied on the bigram, otherwise it is applied separately on w1 and w2; c) each polysemous unigram w is disambiguated using a set of words surrounding w as the context of w. The WSD strategy has been used to process documents in the EachMovie dataset, a collection made of 1,628 movie descriptions1. The BOS representation of a document consists of a list of synsets recognized from the words in the document. II. THE ITEM RECOMMENDER PROFILING SYSTEM ITem Recommender (ITR) is a content-based profiling system able to induce semantic user profiles as naive Bayesian classifiers by learning from documents represented according to the BOS model [4]. Given a set of documents in which each document is labeled by a specific user with a rating representing his degree of interest on that document, the system is able to learn the user profiles as a Bayesian text classifier able to classify a new document as interesting or uninteresting for that user. Naive Bayes is a probabilistic approach to inductive learning. The learned probabilistic model estimates the a posteriori probability, P (cj |di), of document di belonging to class cj . This estimation is based on the a priori probability, P (cj), i.e. the probability of observing a document in class cj , P (di|cj), that is the probability of observing document di given cj , and P (di), the probability of observing the instance di at all. Using these probabilities, Bayesian classifiers apply Bayes theorem to calculate P (cj |di). To classify a document di, the class with the highest probability is selected. As a working model for the naive Bayes classifier, we use the multinomial event model [5]: P (cj |di) = P (cj) ∏