Rodrigo Agerri


  1. QWN-PPV: Q-WordNet via Personalized PageRank
  2. ILF-WN: Intermediate Logic Forms from WordNet glosses
  3. Q-WordNet: Extracting polarity from WordNet senses

QWN-PPV: Generate polarity lexicons on demand

A new Q-WordNet version based on applying Personalized PageRanking to the original Q-WordNet approach. It is a simple, robust and (almost) unsupervised dictionary-based method (Q-WordNet by Personalized PageRanking Vector) to automatically generate polarity lexicons.

The extrinsic evaluations performed show that qwn-ppv outperforms other automatically generated lexicons. It also shows very competitive and robust results with respect to manually annotated ones. Results suggest that no single lexicon is best for every task and dataset and that the intrinsic evaluation of polarity lexicons is not a good indicator of good performance on a Sentiment Analysis task.

Our method is easily applicable to create qwn-ppv(s) other languages, and we demonstrate it by providing polarity lexicons for English and Spanish. The qwn-ppv method allows to easily create quality polarity lexicons whenever no domain-based annotated corpora are available for a given language.

ILF-WN: Automatic Generation of Intermediate Logic Forms for WordNet glosses

A lexical resource which consists of the automatically generated Intermediate Logic Forms (ILFs) of WordNet’s glosses. Intermediate Logic Forms (ILFs) include extreme neo-davidsonian reification in a simple and flat syntax close to natural language form. We believe that ILFs provide a precise representation suitable to perform semantic inference without the brittleness that characterizes approaches based on first-order logic and theorem proving. In its current form, the representation allows to tackle semantic phenomena such as coreference and anaphora resolution. Moreover, it can be further specified to deal with other specific semantic issues such as quantification.

Intermediate Logic Forms are straightforwardly obtained from the output of pipeline consisting of a part of speech tagger, a dependency parser and our own Intermediate Logic Form generator (all freely available tools). We apply the pipeline to the glosses of WordNet to obtain a lexical resource ready to be used as knowledge base or common knowledge resource for a variety of tasks involving some kind of semantic inference.

If you use ILF-WN, please cite this paper:

Q-WordNet: Extracting Polarity from WordNet senses

Q-WordNet is a lexical resource consisting of WordNet senses automatically classified by Positive and Negative polarity. Q-WordNet has been built for versions 1.6, 1.7, 2.0 and 3.0 of WordNet. Version 2.0 has been compared to SentiWordNet 1.0, also built from WordNet 2.0, with very promising results. Q-WordNet first version has been released for the LREC 2010, and included in the Resources Map.

Polarity classification amounts to decide whether a text (sense, sentence, document) is associated to a positive or negative connotation. This task is becoming important for determining opinions about commercial products, on companies reputation management, brand monitoring, or to track attitudes by mining online forums, blogs, etc. Inspired by work on classification of word senses by polarity in SentiWordNet, and taking WordNet as a starting point, we create Q-WordNet, but instead of applying supervised classifiers, we decided to effectively maximize the linguistic information contained in WordNet (by human annotators). A quantitative evaluation of Q-WordNet as a binary classification task shows important improvements with respect to SentiWordNet.

  • Download (including reference paper): Q-WordNet

If you use Q-WordNet please cite this paper: