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Domain-Based Sense Disambiguation in Multilingual Structured Data Domain-Based Sense Disambiguation in Multilingual Structured Data Gabor´ Bella and Alessio Zamboni and Fausto Giunchiglia1 Abstract. Natural language text is pervasive in structured data Techniques such as cross-lingual semantic matching [2], seman- sets—relational database tables, spreadsheets, XML documents, tic search [7], or semantic service integration [13] were designed RDF graphs, etc.—requiring data processing operations to possess to tackle diversity in data and therefore invariably have some kind some level of natural language understanding capability. This, in of built-in meaning extraction capabilities. In semantic search, nat- turn, involves dealing with aspects of diversity present in structured ural language queries should be interpreted and matched to data in data such as multilingualism or the coexistence of data from multi- a robust way so that search is based on meaning and not on sur- ple domains. Word sense disambiguation is an essential component face forms of words (a tourist’s query on ‘bars’ should also return of natural language understanding processes. State-of-the-art WSD establishments indicated as ‘winebar’, cf. fig. 1 a, but preferably techniques, however, were developed to operate on single languages no results on the physical unit of pressure). In classification tasks, and on corpora that are considerably different from structured data natural-language labels indicating classes need to be formalised sets, such as articles, newswire, web pages, forum posts, or tweets. and compared to each other (establishments categorised as ‘malga’, In this paper we present a WSD method that is designed for short i.e., Alpine huts specific to the region of Trento, should be classified text typically present in structured data, applicable to multiple lan- as lodging facilities, cf. fig. 1 a). In service integration, on the schema guages and domains. Our proof-of-concept implementation reaches level, attribute names need to be mapped using schema matching an all-words F-score between 60% and 80% on both English and Ital- techniques (the English ‘address’ mapped to the Italian ‘indirizzo’); ian data. We consider these as very promising first results given the while on the data level, heterogeneous terminology used across data known difficulty of WSD and the particularity of the corpora targeted sets needs to be mapped to common meanings in order to allow in- with respect to more conventional text. teroperability (‘PhD thesis’ equivalent to ‘doctoral thesis’). We argue that sense disambiguation that relies on conventional 1 INTRODUCTION natural language processing methods and toolkits, while still applica- While current formal or semi-formal data models—spreadsheets, ble, is suboptimal for dealing with diversity in structured data. First, XML trees, RDF graphs, etc.—were designed to ease the process- NLP tools and resources tend to be developed for single languages ing of data by machines, structured data sets still tend to contain a and the representations they use for word senses do not always allow large amount of informal text expressed in natural language within cross-lingual interoperability. Secondly, the type of text appearing in schema elements, data values, and metadata. structured data is considerably different from those targeted by state- Ever more often, applications need to exploit data sets—link, inte- of-the-art NLP tools. Most existing efforts on NLP concentrate ei- grate, query, and search them—facing various aspects of diversity ther on ‘conventional’ text with full, grammatically correct sentences in textual data in the process, e.g., the diversity of the languages and standard orthography (e.g., newswire, encyclopedia entries, lit- used, of terminology, or of the domains covered. Picture multilin- erature, general web content) or on short and noisy text (e.g., tweets, gual Switzerland where a French application on tourism may need forum comments). to use travel information available in German as well as geograph- Compared to these cases, text in structured data tends to be shorter ical open data in English, needing to connect data in multiple lan- and follows different conventions of orthography and syntax. We be- guages and from multiple domains. Another example is medical data lieve the best-fitting linguistic category to be that of block language, of patients being exchanged across countries for research purposes, defined in [4] as ‘abbreviated structures in restricted communicative again expressed in different languages and using different national contexts, especial use being made of the word or phrase, rather than standards.Fig. 1 shows examples of natural language text content ex- the clause or sentence.’ In such text ‘communicative needs strip lan- tracted from real-world data sets that we will use as running exam- guage of all but the most information-bearing forms’ [3]. ples: The result is that techniques and resources typically used in NLP, open government data in Italian and English from the tourism do- • such as machine learning models trained on ‘conventional’ corpora, main containing points of interest in Trento; can only be applied to structured data with a loss in accuracy. Re- healthcare data in English containing dosages of drugs; • training is not in itself a satisfying answer as sequence labelling on university data in English and French on papers published by staff • words—the usual mode of operation of machine learning tools in containing abstracts, keywords, titles, etc. NLP—relies on an adequate amount of co-text, i.e., preceding and 1 University of Trento, via Sommarive 5, 38123 Trento, Italy. following words, the lack of which in structured data again leads to gabor.bella, alessio.zamboni, fausto.giunchiglia @unitn.it. lower accuracy. { } 53 Nome Categoria Desc EN Indirizzo AI VICOLI ristorante Restaurant and Winebar Piazza Santa Teresa Verzieri, Trento ORSO GRIGIO ristorante typical restaurant Via degli Orti, 19, Trento MALGA CANDRIAI malga Alpine hut with typical restaurant Strada di Candriai, 2, Monte Bondone (a) Open data from Trentino, Italy, on points of interest. active substance name note dosage apixaban Eliquis 5 mg of 60 film coated tablets 10 milligrams oral warfarin Coumadin 30 tablets 5 mg 7.5 milligrams oral, 7.5 milligrams injected (b) A data set from a healthcare agency on available drugs and their dosages. Title Abstract Date Type Keywords Concept Search: Semantics- The goal of information retrieval is 2010 PhD thesis semantic search - information retrieval - Enabled Information Retrieval to map a natural language query. classification L’oublie la presence´ le jeu 2009 article the´atreˆ - comedie´ - representation´ (c) A data set of university publications. Figure 1. Simplified examples extracted from real-world data sets, showing various types of natural language text commonly found in structured data. This paper provides an approach to word sense disambiguation 2 TEXT IN STRUCTURED DATA that is adapted to text in structured data and is based on the following principles. 2.1 Linguistic Features A language-independent representation of meaning. The disam- In this section we briefly examine the linguistic characteristics of text biguation method is designed to be applicable to multiple lan- in structured data. guages. The hypothesis underlying this design choice is that meanings of words can efficiently be represented as language- Languages. It is not uncommon for data sets to mix languages if independent concepts. We tackle multilingual diversity at design they were aggregated from heterogeneous sources or if they were time through the use of multilingual NLP preprocessors, followed produced in geographical areas or usage domains where multi- by a language-agnostic WSD method that operates on the concept lingualism is common practice. The language may change across level and can thus be applied to any language. records (fig. 1 c) or across attributes (fig. 1 a). Domain-Based WSD suits structured data. The backbone of our Text length. The typical length of textual attribute values is that of method is a domain-based WSD algorithm, for two reasons. First, a single phrase with less than 10 tokens (words and punctuation). we observe that contents of structured data tend to be domain- In attribute names 1–3 tokens are typical. specific. Secondly, we argue that formalising the notion of domain Orthography. The divergence from standard orthography is consid- is the first step towards tackling this aspect of diversity in struc- erable: tured data. We tackle the diversity of domains at runtime through capitalisation is used arbitrarily: ALL CAPITALS or no capitals • automated domain extraction and domain-based WSD. are frequent, as is Capitalisation Of Each Word (all of which Weaker reliance on co-text. Due to the shortness of text, the output can be found in fig. 1 a); capitals are therefore not reliable lin- of machine learning methods trained on long text using sequence guistic indicators and, worse, they can confuse machine learn- labelling—such as state-of-the-art part-of-speech taggers—has to ing components trained on conventional text, be considered as less reliable and ‘taken with a pinch of salt.’ For punctuation is often omitted or inconsistently used (e.g., dashes this reason we only very minimally rely on co-text in our WSD • instead of commas are used to separate enumerated items, approach. fig. 1 c), Stronger reliance on structural context. Instead of relying on sur- rounding words, the context encoded in the surrounding data abbreviations are frequent, especially
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