Document indexing is a key component for efficient information retrieval (IR). After preprocessing steps such as stemming and stop-word removal, document indexes usually store term-frequencies (tf). Along with tf (that only reflects the importance of a term in a document), traditional IR models use term discrimination values (TDVs) such as inverse document frequency (idf) to favor discriminative terms during retrieval. In this work, we propose to learn TDVs for document indexing with shallow neural networks that approximate traditional IR ranking functions such as TF-IDF and BM25. Our proposal outperforms, both in terms of nDCG and recall, traditional approaches, even with few positively labelled query-document pairs as learning data. Our learned TDVs, when used to filter out terms of the vocabulary that have zero discrimination value, allow to both significantly lower the memory footprint of the inverted index and speed up the retrieval process (BM25 is up to 3~times faster), without degrading retrieval quality.

In this article, we tackle the issue of the limited quantity of manually sense annotated corpora for the task of word sense disambiguation, by exploiting the semantic relationships between senses such as synonymy, hypernymy and hyponymy, in order to compress the sense vocabulary of Princeton WordNet, and thus reduce the number of different sense tags that must be observed to disambiguate all words of the lexical database.We propose two different methods that greatly reduce the size of neural WSD models, with the benefit of improving their coverage without additional training data, and without impacting their precision.In addition to our methods, we present a WSD system which relies on pre-trained BERT word vectors in order to achieve results that significantly outperforms the state of the art on all WSD evaluation tasks.

For humans the main functions of a dictionary is to store information concerning words and to reveal it when needed.While readers are interested in the meaning of words, writers look for answers concerning usage, spelling, grammar or word forms (lemma).We will focus here on this latter task : help authors to find the word they are looking for, word they may know but whose form is eluding them.Put differently, we try to build a resource helping authors to overcome the tip-of-the-tongue problem (ToT).Obviously, in order to access a word, it must be stored somewhere (brain, resource).Yet this is by no means sufficient.We will illustrate this here by comparing WordNet (WN) to an equivalent lexical resource bootstrapped from Wikipedia (WiPi).Both may contain a given word, but ease and success of access may be different depending on other factors like quality of the query, proximity, type of connections, etc. Next we will show under what conditions WN is suitable for word access, and finally we will present a roadmap showing the obstacles to be overcome to build a resource allowing the text producer to find the word s/he is looking for.