Discusión global de los resultados obtenidos

Across the NLP literature, an important task that is usually considered very related to WSD is Entity Linking (Erbs et al., 2011; Rao et al., 2013, EL). The goal of EL is to identify mentions of entities within a text, and then link (disambiguate) them with the most suitable entry in a reference knowledge base. The increasing popularity of EL is connected to the availability of semi-structured resources (cf. Chapter 1), especially Wikipedia –to the extent that Wikipedia-based EL is usually known as

Wikification (Mihalcea and Csomai, 2007; Milne and Witten, 2008; Ratinov et al.,

2011). Originally conceived as one of the fundamental steps within the broader task of Information Extraction (Section 2.3), EL is another long-standing task but, unlike WSD, lacks a standard formal definition of the problem, as well as well-established evaluation benchmarks where EL systems can be meaningfully compared (Ling et al., 2015), despite some recent efforts in this direction (Cornolti et al., 2013; Usbeck et al., 2015). For instance, among the many variants of EL, Wikification aims at linking all kinds of noun phrases to Wikipedia entities, while Named Entity Disambiguation (Bunescu and Paşca, 2006; Cucerzan, 2007) targets only named entities. In this thesis we follow the seminal work of Rao et al. (2013) and consider EL as the better defined problem of solely linking named entities.10

The EL can be split in two separate (and typically consequent) subtasks: 1. Mention Extraction, i.e. identifying the boundaries of named entities

in a target text. This subtask is closely related to a classical NLP task, Named Entity Recognition (Nadeau and Sekine, 2007, NER), where named entity mentions are extracted and classified into a predefined set of general

10

As we discuss in Section 2.2.2.3, the task of linking common noun phrases is closely related to WSD, to the extent that they can actually be considered the same task, both formally and operationally.

2.2 From Words to Senses 27

semantic classes (e.g. PERSON, LOCATION, ORGANIZATION). Seemingly simple, this subtask is actually challenging and controversial per se, not only because of name variations (e.g. abbreviations, acronyms, or spelling differences) but also because mention boundaries can easily overlap. For instance, the noun phrase ‘Portland, Oregon’ can be considered as a whole, or can be split into two individual mentions, Portland for the city and Oregon for the city’s state, with all three mentions making perfect sense;

2. Entity Disambiguation, i.e. the subtask of appropriately matching a men- tion to an entry inside a predefined knowledge base, which is often Wikipedia or a Wikipedia-derived knowledge base, such as Yago, Freebase, or DBpedia (cf. Section 2.1.2). The key issue here is, of course, ambiguous mentions, either because of polysemy (e.g. Washington being a president, a federal district, or a U.S. state) or metonymy (e.g. Moscow referring to the government of Russia rather than the actual city). Another issue is coreference, as two or more mentions can often refer to the same entity (e.g. Trump, D. Trump, Mr. President). Finally, as no standard annotation guidelines exist with respect to EL, a number of structural issues arise (Ling et al., 2015), such as how specific a linked entity should be (e.g. the mention World Cup can be legitimately linked to the Wikipage FIFA World Cup, as well as to a specific occurrence of the event, say the Wikipage 1998 FIFA World Cup), or how to deal with entities that are absent from the knowledge base.

2.2.2.1 Evaluation and Standard Benchmarks

In contrast to WSD, where well-established benchmark are provided by the Sense- val/SemEval competition series (cf. Section 2.2.1), EL systems are often compared using different datasets (Ling et al., 2015). The most common benchmarks that have been utilized over the years are: the ACE and MSNBC datasets (Cucerzan, 2007; Ratinov et al., 2011), with entity mentions extracted from newswire text and linked to Wikipedia; the TAC-KBP datasets (McNamee et al., 2009), which are however only available to the task participants; and the AIDA-CoNLL test dataset (Hoffart et al., 2011b), the largest publicly available, comprising almost 1,400 English articles and roughly 35,000 entity mentions linked to Yago. Other notable datasets are the Wikipedia-based CSAW (Kulkarni et al., 2009) and AQUAINT (Milne and Wit- ten, 2008), which annotate both concepts and named entities, and KORE50 (Hoffart et al., 2012), a small-size dataset of 50 short English texts annotated using Yago, and built with the idea of testing against a high level of mention ambiguity.

In addition to the many datasets available, a variety of metrics have also been used for evaluation, with little agreement on which ones are best (Ling et al., 2015). The most common ones include: Bag-of-Concept F1 (ACE and MSNBC datasets), where a gold bag of Wikipedia entries is evaluated against a bag of Wikipedia entities provided by the system, micro accuracy (TAC-KBP datasets), which is simply the percentage of correctly predicted links, and NER-style F1 (AIDA-CoNLL), where a link is considered correct only if the mention matches the gold boundary and the linked entity is also correct.

2.2.2.2 Approaches to EL

The earliest approaches to EL were Wikification approaches (Mihalcea and Csomai, 2007; Cucerzan, 2007), in which the local context surrounding the mentions had a primary role, similarly to supervised WSD (cf. Section 2.2.1). Based on a collective notion of coherence among the selected Wikipages, Milne and Witten (2008) focused instead on analyzing the semantic relations between the candidate entity mentions and the unambiguous context. Despite the crucial dependence on unambiguous words within the input context, their approach started a successful trend of EL models based on both local and global features (Kulkarni et al., 2009; Ratinov et al., 2011; Hoffart et al., 2011b; Mendes et al., 2011). In recent years, more sophisticated approaches have been developed, exploiting Integer Linear Programming (Cheng and Roth, 2013), generative models (Han and Sun, 2012), stacked generalization (He et al., 2013b) and deep neural networks (He et al., 2013a). Another extremely promising line of work consists in tackling EL by jointly modeling the disambiguation of entities and closely related tasks, such as NER (Sil and Yates, 2013; Nguyen et al., 2016) and coreference resolution (Hajishirzi et al., 2013; Durrett and Klein, 2014), where generative models are employed to capture inter-task interactions. This key intuition, along with the availability of knowledge resources like BabelNet, has motivated the development of joint WSD and EL approaches, which we examine in Section 2.2.2.3.

2.2.2.3 Joint WSD and EL: Babelfy

WSD and EL are undoubtedly similar, as in both cases text fragments have to be disambiguated according to a reference inventory. However, there are two important differences between them: the nature of sense inventory (dictionaries and lexicons for WSD, encyclopedic knowledge bases for EL), and the fact that in EL mentions in context are not guaranteed to be complete but can be (and often are) partial. As a result of these and other discrepancies, the research community has spent a lot of time tackling WSD and EL separately, not only leading to duplicated efforts and results, but also failing to exploit the fact that these two tasks are deeply intertwined. Consider the following example:

He loves driving around with his Mercedes.

where the verb driving should be resolved by a WSD system with the sense of ‘operating vehicles’, and the partial mention ‘Mercedes’ should be recognized by an EL system and linked to the automobile brand (Mercedes-Benz). In this setting, the WSD system would clearly benefit from knowing that a brand of vehicles is mentioned in the local context of driving and, at the same time, the EL system would easily take advantage of the sense-level information about driving referring to vehicles when linking the mention ‘Mercedes’.

This is where linked lexical resources like BabelNet (Section 2.1.3) play a role: by providing a large-scale encyclopedic dictionary as common ground for WSD and EL, they enable the design and development of unified WSD and EL models. The first of this kind is Babelfy (Moro et al., 2014b), based on BabelNet: Babelfy is a graph-based approach to joint WSD and EL that exploits a loose identification of candidate meanings, and a densest-subgraph algorithm to select high-coherence semantic interpretations. Babelfy disambiguates as follows:

2.2 From Words to Senses 29

Figure 2.4. Excerpt of the semantic interpretation graph for the example sentence ‘Thomas and Mario are strikers playing in Munich’, borrowed from Moro et al. (2014b). The edges connecting the correct meanings (e.g. Thomas Müller for Thomas and Mario Gomez for Mario) are in bold.

1. Given a lexicalized semantic network, such as BabelNet, a semantic signature is computed for each concept or entity. A semantic signature is a set of highly related vertices obtained by performing Random Walks with Restart (RWR) (Tong et al., 2008) for each vertex v of the semantic network. RWR

models the conditional probability P (v0|v) associated with an edge (v, v0):

P (v0|v) = weight(v, v

0₎

P

v00_∈V weight(v, v00)

(2.1)

where V is the set of vertices in the semantic network and weight(v, v0) is the weight associated with (v, v0). This is a preliminary step that needs to be performed once and for all, independently of the input text;

2. Given an input text, Babelfy extracts all the linkable fragments and lists all their possible meanings according to the sense inventory. Candidate extraction is a high-coverage procedure based on superstring (instead of exact) matching, and capable of handling partial mentions and overlapping fragments;

3. A graph-based semantic interpretation of the input text is generated using the semantic signatures of all candidate meanings. Then Babelfy extracts a

dense subgraph of this representation in order to select a coherent set of best

candidates for the target mentions. An example of semantic interpretation graph is shown in Figure 2.4. Each candidate in the graph is weighted with a measure that takes into account both semantic and lexical coherence, exploiting graph centrality among the candidates as well as the number of connected fragments. This measure is used in the dense-subgraph algorithm to iteratively remove low-coherence vertices from the semantic graph until convergence. One of the greatest advantages of Babelfy is flexibility: it can be used seamlessly for WSD, EL or even both at the same time. Also, the whole procedure is language- independent, and can be extended to any language for which lexicalizations are available inside the semantic network. In fact, Babelfy can even handle mixed text in which multiple languages are used at the same time, or work without being supplied with information as to which languages the input text contains (“language-agnostic” setting). On the other hand, as in any knowledge-based approach (Section 2.2.1), the quality of disambiguation depends crucially on the quality of the underlying

resource, and rarely achieves the same results of supervised models (Raganato et al., 2017a). BabelNet and Babelfy have inaugurated a new, broader way of looking at disambiguation in Lexical Semantics, which has been further pursued by the research community (Basile et al., 2015; Weissenborn et al., 2015) and has led to the organization of novel shared tasks focused on multilingual WSD and EL as part of the SemEval competition series, namely the SemEval-2013 task 12 on Multilingual Word Sense Disambiguation (Navigli et al., 2013), and the SemEval-2015 task 13 on Multilingual All-words Word Sense Disambiguation and Entity Linking (Moro and Navigli, 2015). Both tasks required participating systems to disambiguate a set of target words and multi-word expressions in a test corpus with the most appropriate sense from the BabelNet sense inventory (or, alternatively, from those of WordNet or Wikipedia) and for 5 and 4 languages, respectively. In particular, the SemEval-2015 task 13 has been the first disambiguation task explicitly oriented to joint WSD and EL, including features of a typical WSD task (i.e. sense annotations for all open-class parts of speech) ad well as features of a typical EL task (i.e. annotated named entities and non-specified mention boundaries).