A Lesk-inspired Unsupervised Algorithm for Lexical Choice from WordNet Synsets
Authors: Valerio Basile (University of Groningen, 2014)
Topic: Lexical and Semantic Resources and Analysis

Abstract
The generation of text from abstract meaning representations involves, among other tasks, the production of lexical items for the concepts to realize. Using WordNet as a foundational ontology, we exploit its internal network structure to predict the best lemmas for a given synset without the need for annotated data. Experiments based on re-generation and automatic evaluation show that our novel algorithm is more effective than a straightforward frequency-based approach.

Introduction
Many linguists argue that true synonyms don’t exist (Bloomfield, 1933; Bolinger, 1968). Yet, words with similar meanings do exist and they play an important role in language technology where lexical resources such as WordNet (Fellbaum, 1998) employ synsets, sets of synonyms that cluster words with the same or similar meaning. It would be wrong to think that any member of a synset would be an equally good candidate for every application. Consider for instance the synset food, nutrient , a concept whose gloss in WordNet is “any substance that can be metabolized by an animal to give energy and build tissue”. In (1), this needs to be realized as “food”, but in (2) as “nutrient”. 1. It said the loss was significant in a region where fishing provides a vital source of foodnutrient. 2. The Kind-hearted Physician administered a stimulant, a tonic, and a foodnutrient, and went away. A straightforward solution based on n-gram models or grammatical constraint (“a food” is ungrammatical in the example above) is not always applicable, since it would be necessary to generate the complete sentence first, to exploit such features. This problem of lexical choice is what we want to solve in this paper. In a way it can be regarded as the reverse of WordNet-based Word Sense Disambiguation, where instead of determining the right synset for a certain word in a given context, the problem is to decide which word of a synset is the best choice in a given context. Lexical choice is a key task in the larger framework of Natural Language Generation, where an ideal model has to produce varied, naturalsounding utterances. In particular, generation from purely semantic structures, carrying little to no syntactic or lexical information, needs solutions that do not depend on pre-made choices of words to express generic concepts. The input to a lexical choice component in this context is some abstract representation of meaning that may specify to different extent the linguistic features that the expected output should have. Questo e-book appartiene a AlessandroLenci WordNet synsets are good candidate representations of word meanings, as WordNet could be seen as a dictionary, where each synset has its own definition in written English. WordNet synsets are also well suited for lexical choice, because they consist in actual sets of lemmas, considered to be synonyms of each other in specific contexts. Thus, the problem presented here is restricted to the choice of lemmas from WordNet synsets. Despite its importance, the task of lexical choice problem is not broadly considered by the NLGcommunity,oneofthereasonsbeingthatitis hard to evaluate. Information retrieval techniques fail to capture not-so-wrong cases, i.e. when a system produces a different lemma from the gold standard but still appropriate to the context. In this paper we present an unsupervised methodtoproducelemmasfromWordNetsynsets, inspired by the literature on WSD and applicable to every abstract meaning representation that provides links from concepts to WordNet synsets.

Conclusion
In this paper we presented an unsupervised algorithm for lexical choice from WordNet synsets called Ksel that exploits the WordNet hierarchy of hypernyms/hyponyms to produce the most appropriate lemma for a given synset. Ksel performs better than an already high baseline based on the frequency of lemmas in an annotated corpus. The future direction of this work is at least twofold. On the one hand, being based purely on a lexical resource, the Ksel approach lends itself nicely to be applied to different languages by leveraging multi-lingual resources like BabelNet (Navigli and Ponzetto, 2012). On the other hand, we want to exploit existing annotated corpora such as the GMB to solve the lexical choice problem in a supervised fashion, that is, ranking candidate lemmas based on features of the semantic structure, in the same track of our previous work on generation from work-aligned logical forms (Basile and Bos, 2013).