What's in a Food Name: Knowledge Induction from Gazetteers of Food Main Ingredient
Authors: Bernardo Magnini, Vevake Balaraman, Simone Magnolini, Marco Guerini (Fondazione Bruno Kessler, Università di Trento, 2018)
Topic: Lexical and Semantic Resources and Analysis

Abstract
We investigate head-noun identiEnglish. We investigate head-noun identiﬁcation in complex noun-compounds (e.g. table is the head-noun in three legs table with white marble top). The task is of high relevancy in several application scenarios, including utterance interpretation for dialogue systems, particularly in the context of e-commerce applications, where dozens of thousand of product descriptions for several domains and different languages have to be analyzed. We deﬁne guidelines for data annotation and propose a supervised neural model that is able to achieve 0.79 F1 on Italian food noun-compounds, which we consider an excellent result given both the minimal supervision required and the high linguistic complexity of the domain.

Introduction
Noun-compounds are nominal descriptions that hold implicit semantic relations between their constituents (Shwartz and Dagan, 2018). For instance, an apple cake is a cake made of apples. While in the literature there has been a large interest in interpreting noun-compounds by classifying them with a ﬁxed set of ontological relations (Nakov and Hearst, 2013), in this paper we focus on automatic recognition of the head-noun in noun-compounds. We assume that in each nouncompound there is a noun which can be considered as the more informative, as it brings the most relevant information that allows the correct interpretation of the whole noun-compound. For instance, in the apple cake example, we consider cake as the head-noun, because it brings more information than apple about the kind of food the compound describes (i.e. a dessert), its ingredients (i.e. likely, ﬂour, milk and eggs), and the typical amount a person may eat (i.e. likely, a slice). While in simple noun-compounds the head-noun usually corresponds to the syntactic head of the compound, this is not the case for complex compounds, where the head-noun can occur in different positions of the compound, making its identiﬁcation challenging. As an example, in the Italian food description ﬁletto di vitellone senza grasso visibile, there are three nouns (i.e. ﬁletto, vitellone and grasso) which are candidates to be the headnoun of the compound. There are a number of tasks and application domains where identifying noun-compound headnouns is relevant. A rather general context is ontology population (Buitelaar et al., 2005), where entity names automatically recognized in text are confronted against entity names already present in an ontology, and have to be appropriately matched in the ontology taxonomy. Our speciﬁc application interest is conversational agents for the ecommerce domain. Particularly, understanding names of products (e.g. food, furniture, clothes, digital equipment) as expressed by users in different languages, requires the capacity to distinguish the main element in a product name (e.g. a table in I am looking for a three legs table with white marble top), in order to match them against vendor catalogues and to provide a meaningful dialogue with the user. The task is made much more challenging by the general lack of annotated data, so that fully supervised approaches are simply not feasible. Along this perspective, the long term goal of our work is to develop unsupervised techniques that can identify head-nouns in complex nouncompounds by learning properties on the base of the noun-compounds included in, possibly large, gazetteers, regardless of the domain and language in which they are described. In this paper we propose a supervised approach based on a neural sequence-to-sequence model (Lample et al., 2016) augmented with noun-compound structural features (Guerini et al., 2018). This model identiﬁes the more informative token(s) in the noun-compound, that are ﬁnally tagged as the head-noun. We run experiments on Italian food names, and show that, although the domain is very complex, results are promising. The paper is structured as follow: we ﬁrst deﬁne noun-compound head-noun identiﬁcation, with speciﬁc reference to complex noun-compound (Section 2). Then we introduce the neural model we have implemented (Section 3), and ﬁnally the experimental setting and the results we have obtained (Section 4).

Conclusion
We have addressed head-noun identiﬁcation in complex noun-compounds, a task of high relevancy in utterance interpretation for dialogue systems. We proposed a neural model, and experiments on Italian food noun-compounds show that the model is able to outperform strong baselines even with a small amount of data. For the future we plan to extend our investigation to other domain and languages.