Datasets:

WINGNUS
/

ACL-OCL

	{
	"paper_id": "P10-1045",
	"header": {
	"generated_with": "S2ORC 1.0.0",
	"date_generated": "2023-01-19T09:19:42.235891Z"
	},
	"title": "Latent variable models of selectional preference",
	"authors": [
	{
	"first": "Diarmuid\u00f3",
	"middle": [],
	"last": "S\u00e9aghdha",
	"suffix": "",
	"affiliation": {
	"laboratory": "",
	"institution": "University of Cambridge Computer Laboratory United Kingdom",
	"location": {}
	},
	"email": "do242@cl.cam.ac.uk"
	}
	],
	"year": "",
	"venue": null,
	"identifiers": {},
	"abstract": "This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data.",
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	"paper_id": "P10-1045",
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	"abstract": [
	{
	"text": "This paper describes the application of so-called topic models to selectional preference induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word cooccurrences, are presented and evaluated on datasets of human plausibility judgements. Compared to previously proposed techniques, these models perform very competitively, especially for infrequent predicate-argument combinations where they exceed the quality of Web-scale predictions while using relatively little data.",
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	"section": "Abstract",
	"sec_num": null
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	"body_text": [
	{
	"text": "Language researchers have long been aware that many words place semantic restrictions on the words with which they can co-occur in a syntactic relationship. Violations of these restrictions make the sense of a sentence odd or implausible:",
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	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "(1) Colourless green ideas sleep furiously.",
	"cite_spans": [],
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	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
	},
	{
	"text": "(2) The deer shot the hunter.",
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "Recognising whether or not a selectional restriction is satisfied can be an important trigger for metaphorical interpretations (Wilks, 1978) and also plays a role in the time course of human sentence processing (Rayner et al., 2004) . A more relaxed notion of selectional preference captures the idea that certain classes of entities are more likely than others to fill a given argument slot of a predicate. In Natural Language Processing, knowledge about probable, less probable and wholly infelicitous predicateargument pairs is of value for numerous applications, for example semantic role labelling (Gildea and Jurafsky, 2002; Zapirain et al., 2009) . The notion of selectional preference is not restricted to surface-level predicates such as verbs and modifiers, but also extends to semantic frames (Erk, 2007) and inference rules (Pantel et al., 2007) .",
	"cite_spans": [
	{
	"start": 127,
	"end": 140,
	"text": "(Wilks, 1978)",
	"ref_id": "BIBREF29"
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	"start": 211,
	"end": 232,
	"text": "(Rayner et al., 2004)",
	"ref_id": "BIBREF20"
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	"start": 603,
	"end": 630,
	"text": "(Gildea and Jurafsky, 2002;",
	"ref_id": "BIBREF10"
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	"start": 631,
	"end": 653,
	"text": "Zapirain et al., 2009)",
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	"text": "(Erk, 2007)",
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	"text": "(Pantel et al., 2007)",
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	"section": "Introduction",
	"sec_num": "1"
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	{
	"text": "The fundamental problem that selectional preference models must address is data sparsity: in many cases insufficient corpus data is available to reliably measure the plausibility of a predicate-argument pair by counting its observed frequency. A rarely seen pair may be fundamentally implausible (a carrot laughed) or plausible but rarely expressed (a manservant laughed). 1 In general, it is beneficial to smooth plausibility estimates by integrating knowledge about the frequency of other, similar predicate-argument pairs. The task thus share some of the nature of language modelling; however, it is a task less amenable to approaches that require very large training corpora and one where the semantic quality of a model is of greater importance.",
	"cite_spans": [],
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	"section": "Introduction",
	"sec_num": "1"
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	"text": "This paper takes up tools (\"topic models\") that have been proven successful in modelling document-word co-occurrences and adapts them to the task of selectional preference learning. Advantages of these models include a well-defined generative model that handles sparse data well, the ability to jointly induce semantic classes and predicate-specific distributions over those classes, and the enhanced statistical strength achieved by sharing knowledge across predicates. Section 2 surveys prior work on selectional preference modelling and on semantic applications of topic models. Section 3 describes the models used in our experiments. Section 4 provides details of the experimental design. Section 5 presents results for our models on the task of predicting human plausibility judgements for predicate-argument combinations; we show that performance is generally competi-tive with or superior to a number of other models, including models using Web-scale resources, especially for low-frequency examples. In Section 6 we wrap up by summarising the paper's conclusions and sketching directions for future research.",
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	"section": "Introduction",
	"sec_num": "1"
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	{
	"text": "2 Related work",
	"cite_spans": [],
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	"eq_spans": [],
	"section": "Introduction",
	"sec_num": "1"
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	"text": "The representation (and latterly, learning) of selectional preferences for verbs and other predicates has long been considered a fundamental problem in computational semantics (Resnik, 1993) . Many approaches to the problem use lexical taxonomies such as WordNet to identify the semantic classes that typically fill a particular argument slot for a predicate (Resnik, 1993; Clark and Weir, 2002; Schulte im Walde et al., 2008) . In this paper, however, we focus on methods that do not assume the availability of a comprehensive taxonomy but rather induce semantic classes automatically from a corpus of text. Such methods are more generally applicable, for example in domains or languages where handbuilt semantic lexicons have insufficient coverage or are non-existent. Rooth et al. (1999) introduced a model of selectional preference induction that casts the problem in a probabilistic latent-variable framework. In Rooth et al.'s model each observed predicateargument pair is probabilistically generated from a latent variable, which is itself generated from an underlying distribution on variables. The use of latent variables, which correspond to coherent clusters of predicate-argument interactions, allow probabilities to be assigned to predicate-argument pairs which have not previously been observed by the model. The discovery of these predicate-argument clusters and the estimation of distributions on latent and observed variables are performed simultaneously via an Expectation Maximisation procedure. The work presented in this paper is inspired by Rooth et al.'s latent variable approach, most directly in the model described in Section 3.3. Erk (2007) and Pad\u00f3 et al. (2007) describe a corpusdriven smoothing model which is not probabilistic in nature but relies on similarity estimates from a \"semantic space\" model that identifies semantic similarity with closeness in a vector space of cooccurrences. Bergsma et al. (2008) suggest learning selectional preferences in a discriminative way, by training a collection of SVM classifiers to recognise likely and unlikely arguments for predicates of interest. Keller and Lapata (2003) suggest a simple alternative to smoothing-based approaches. They demonstrate that noisy counts from a Web search engine can yield estimates of plausibility for predicate-argument pairs that are superior to models learned from a smaller parsed corpus. The assumption inherent in this approach is that given sufficient text, all plausible predicate-argument pairs will be observed with frequency roughly correlated with their degree of plausibility. While the model is undeniably straightforward and powerful, it has a number of drawbacks: it presupposes an extremely large corpus, the like of which will only be available for a small number of domains and languages, and it is only suitable for relations that are identifiable by searching raw text for specific lexical patterns.",
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	"start": 176,
	"end": 190,
	"text": "(Resnik, 1993)",
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	"start": 359,
	"end": 373,
	"text": "(Resnik, 1993;",
	"ref_id": "BIBREF22"
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	"start": 374,
	"end": 395,
	"text": "Clark and Weir, 2002;",
	"ref_id": "BIBREF7"
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	"start": 396,
	"end": 426,
	"text": "Schulte im Walde et al., 2008)",
	"ref_id": "BIBREF25"
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	"start": 771,
	"end": 790,
	"text": "Rooth et al. (1999)",
	"ref_id": "BIBREF24"
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	"start": 1657,
	"end": 1667,
	"text": "Erk (2007)",
	"ref_id": "BIBREF8"
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	"start": 1672,
	"end": 1690,
	"text": "Pad\u00f3 et al. (2007)",
	"ref_id": "BIBREF18"
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	"start": 1920,
	"end": 1941,
	"text": "Bergsma et al. (2008)",
	"ref_id": "BIBREF0"
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	"start": 2123,
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	"text": "Keller and Lapata (2003)",
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	"section": "Selectional preference learning",
	"sec_num": "2.1"
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	"text": "The task of inducing coherent semantic clusters is common to many research areas. In the field of document modelling, a class of methods known as \"topic models\" have become a de facto standard for identifying semantic structure in documents. These include the Latent Dirichlet Allocation (LDA) model of Blei et al. (2003) and the Hierarchical Dirichlet Process model of Teh et al. (2006) . Formally seen, these are hierarchical Bayesian models which induce a set of latent variables or topics that are shared across documents. The combination of a well-defined probabilistic model and Gibbs sampling procedure for estimation guarantee (eventual) convergence and the avoidance of degenerate solutions. As a result of intensive research in recent years, the behaviour of topic models is well-understood and computationally efficient implementations have been developed. The tools provided by this research are used in this paper as the building blocks of our selectional preference models.",
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	"start": 303,
	"end": 321,
	"text": "Blei et al. (2003)",
	"ref_id": "BIBREF1"
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	"start": 370,
	"end": 387,
	"text": "Teh et al. (2006)",
	"ref_id": "BIBREF26"
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	"section": "Topic modelling",
	"sec_num": "2.2"
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	"text": "Hierarchical Bayesian modelling has recently gained notable popularity in many core areas of natural language processing, from morphological segmentation (Goldwater et al., 2009) to opinion modelling (Lin et al., 2006 ). Yet so far there have been relatively few applications to traditional lexical semantic tasks. Boyd-Graber et al. (2007) integrate a model of random walks on the WordNet graph into an LDA topic model to build an unsupervised word sense disambiguation system. Brody and Lapata (2009) adapt the basic LDA model for application to unsupervised word sense induction; in this context, the topics learned by the model are assumed to correspond to distinct senses of a particular lemma. Zhang et al. (2009) are also concerned with inducing multiple senses for a particular term; here the goal is to identify distinct entity types in the output of a pattern-based entity set discovery system. Reisinger and Pa\u015fca (2009) use LDA-like models to map automatically acquired attribute sets onto the WordNet hierarchy. Griffiths et al. (2007) demonstrate that topic models learned from document-word co-occurrences are good predictors of semantic association judgements by humans.",
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	"start": 154,
	"end": 178,
	"text": "(Goldwater et al., 2009)",
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	"start": 200,
	"end": 217,
	"text": "(Lin et al., 2006",
	"ref_id": "BIBREF16"
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	"start": 315,
	"end": 340,
	"text": "Boyd-Graber et al. (2007)",
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	"start": 479,
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	"text": "Brody and Lapata (2009)",
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	"start": 700,
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	"text": "Zhang et al. (2009)",
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	"start": 905,
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	"text": "Reisinger and Pa\u015fca (2009)",
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	"start": 1025,
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	"text": "Griffiths et al. (2007)",
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	"section": "Topic modelling",
	"sec_num": "2.2"
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	"text": "Simultaneously to this work, Ritter et al. (2010) have also investigated the use of topic models for selectional preference learning. Their goal is slightly different to ours in that they wish to model the probability of a binary predicate taking two specified arguments, i.e., P (n 1 , n 2 \|v), whereas we model the joint and conditional probabilities of a predicate taking a single specified argument. The model architecture they propose, LinkLDA, falls somewhere between our LDA and DUAL-LDA models. Hence LinkLDA could be adapted to estimate P (n, v\|r) as DUAL-LDA does, but a preliminary investigation indicates that it does not perform well in this context. The most likely explanation is that LinkLDA generates its two arguments independently, which may be suitable for distinct argument positions of a given predicate but is unsuitable when one of those \"arguments\" is in fact the predicate.",
	"cite_spans": [
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	"start": 29,
	"end": 49,
	"text": "Ritter et al. (2010)",
	"ref_id": "BIBREF23"
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	"section": "Topic modelling",
	"sec_num": "2.2"
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	"text": "The models developed in this paper, though intended for semantic modelling, also bear some similarity to the internals of generative syntax models such as the \"infinite tree\" (Finkel et al., 2007) . In some ways, our models are less ambitious than comparable syntactic models as they focus on specific fragments of grammatical structure rather than learning a more general representation of sentence syntax. It would be interesting to evaluate whether this restricted focus improves the quality of the learned model or whether general syntax models can also capture fine-grained knowledge about combinatorial semantics.",
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	"text": "(Finkel et al., 2007)",
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	"sec_num": "2.2"
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	{
	"text": "3 Three selectional preference models",
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	"section": "Topic modelling",
	"sec_num": "2.2"
	},
	{
	"text": "In the model descriptions below we assume a predicate vocabulary of V types, an argument vocab-ulary of N types and a relation vocabulary of R types. Each predicate type is associated with a singe relation; for example the predicate type eat:V:dobj (the direct object of the verb eat) is treated as distinct from eat:V:subj (the subject of the verb eat). The training corpus consists of W observations of argument-predicate pairs. Each model has at least one vocabulary of Z arbitrarily labelled latent variables. f zn is the number of observations where the latent variable z has been associated with the argument type n, f zv is the number of observations where z has been associated with the predicate type v and f zr is the number of observations where z has been associated with the relation r. Finally, f z\u2022 is the total number of observations associated with z and f \u2022v is the total number of observations containing the predicate v.",
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	"section": "Notation",
	"sec_num": "3.1"
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	"text": "As noted above, LDA was originally introduced to model sets of documents in terms of topics, or clusters of terms, that they share in varying proportions. For example, a research paper on bioinformatics may use some vocabulary that is shared with general computer science papers and some vocabulary that is shared with biomedical papers. The analogical move from modelling document-term cooccurrences to modelling predicate-argument cooccurrences is intuitive: we assume that each predicate is associated with a distribution over semantic classes (\"topics\") and that these classes are shared across predicates. The high-level \"generative story\" for the LDA selectional preference model is as follows:",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
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	"text": "(1) For each predicate v, draw a multinomial distribution \u0398 v over argument classes from a Dirichlet distribution with parameters \u03b1.",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
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	"text": "(2) For each argument class z, draw a multinomial distribution \u03a6 z over argument types from a Dirichlet with parameters \u03b2.",
	"cite_spans": [],
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
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	"text": "(3) To generate an argument for v, draw an argument class z from \u0398 v and then draw an argument type n from \u03a6 z",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
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	"text": "The resulting model can be written as:",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
	{
	"text": "EQUATION",
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	"start": 0,
	"end": 8,
	"text": "EQUATION",
	"ref_id": "EQREF",
	"raw_str": "P (n\|v, r) = z P (n\|z)P (z\|v, r)",
	"eq_num": "(1)"
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
	{
	"text": "EQUATION",
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	"eq_spans": [
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	"start": 0,
	"end": 8,
	"text": "EQUATION",
	"ref_id": "EQREF",
	"raw_str": "\u221d z f zn + \u03b2 f z\u2022 + N \u03b2 f zv + \u03b1 z f \u2022v + z \u03b1 z",
	"eq_num": "(2)"
	}
	],
	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
	{
	"text": "Due to multinomial-Dirichlet conjugacy, the distributions \u0398 v and \u03a6 z can be integrated out and do not appear explicitly in the above formula. The first term in (2) can be seen as a smoothed estimate of the probability that class z produces the argument n; the second is a smoothed estimate of the probability that predicate v takes an argument belonging to class z. One important point is that the smoothing effects of the Dirichlet priors on \u0398 v and \u03a6 z are greatest for predicates and arguments that are rarely seen, reflecting an intuitive lack of certainty. We assume an asymmetric Dirichlet prior on \u0398 v (the \u03b1 parameters can differ for each class) and a symmetric prior on \u03a6 z (all \u03b2 parameters are equal); this follows the recommendations of Wallach et al. (2009) for LDA. This model estimates predicate-argument probabilities conditional on a given predicate v; it cannot by itself provide joint probabilities P (n, v\|r), which are needed for our plausibility evaluation.",
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	"start": 750,
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	"text": "Wallach et al. (2009)",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
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	{
	"text": "Given a dataset of predicate-argument combinations and values for the hyperparameters \u03b1 and \u03b2, the probability model is determined by the class assignment counts f zn and f zv . Following Griffiths and Steyvers 2004, we estimate the model by Gibbs sampling. This involves resampling the topic assignment for each observation in turn using probabilities estimated from all other observations. One efficiency bottleneck in the basic sampler described by Griffiths and Steyvers is that the entire set of topics must be iterated over for each observation. Yao et al. (2009) propose a reformulation that removes this bottleneck by separating the probability mass p(z\|n, v) into a number of buckets, some of which only require iterating over the topics currently assigned to instances of type n, typically far fewer than the total number of topics. It is possible to apply similar reformulations to the models presented in Sections 3.3 and 3.4 below; depending on the model and parameterisation this can reduce the running time dramatically.",
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	"text": "Yao et al. (2009)",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
	},
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	"text": "Unlike some topic models such as HDP (Teh et al., 2006) , LDA is parametric: the number of topics Z must be set by the user in advance. However, Wallach et al. (2009) demonstrate that LDA is relatively insensitive to larger-than-necessary choices of Z when the Dirichlet parameters \u03b1 are optimised as part of model estimation. In our implementation we use the optimisation routines provided as part of the Mallet library, which use an iterative procedure to compute a maximum likelihood estimate of these hyperparameters. 2",
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	"start": 37,
	"end": 55,
	"text": "(Teh et al., 2006)",
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	"text": "Wallach et al. (2009)",
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	"section": "Latent Dirichlet Allocation",
	"sec_num": "3.2"
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	{
	"text": "In Rooth et al.'s (1999) selectional preference model, a latent variable is responsible for generating both the predicate and argument types of an observation. The basic LDA model can be extended to capture this kind of predicate-argument interaction; the generative story for the resulting ROOTH-LDA model is as follows:",
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	"start": 3,
	"end": 24,
	"text": "Rooth et al.'s (1999)",
	"ref_id": null
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	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "(1) For each relation r, draw a multinomial distribution \u0398 r over interaction classes from a Dirichlet distribution with parameters \u03b1.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "(2) For each class z, draw a multinomial \u03a6 z over argument types from a Dirichlet distribution with parameters \u03b2 and a multinomial \u03a8 z over predicate types from a Dirichlet distribution with parameters \u03b3.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "(3) To generate an observation for r, draw a class z from \u0398 r , then draw an argument type n from \u03a6 z and a predicate type v from \u03a8 z .",
	"cite_spans": [],
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	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "The resulting model can be written as:",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "P (n, v\|r) = z P (n\|z)P (v\|z)P (z\|r) (3) \u221d z f zn + \u03b2 f z\u2022 + N \u03b2 f zv + \u03b3 f z\u2022 + V \u03b3 f zr + \u03b1 z f \u2022r + z \u03b1 z (4)",
	"cite_spans": [],
	"ref_spans": [],
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	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "As suggested by the similarity between (4) and (2), the ROOTH-LDA model can be estimated by an LDA-like Gibbs sampling procedure.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "Unlike LDA, ROOTH-LDA does model the joint probability P (n, v\|r) of a predicate and argument co-occurring. Further differences are that information about predicate-argument co-occurrence is only shared within a given interaction class rather than across the whole dataset and that the distribution \u03a6 z is not specific to the predicate v but rather to the relation r. This could potentially lead to a loss of model quality, but in practice the ability to induce \"tighter\" clusters seems to counteract any deterioration this causes.",
	"cite_spans": [],
	"ref_spans": [],
	"eq_spans": [],
	"section": "A Rooth et al.-inspired model",
	"sec_num": "3.3"
	},
	{
	"text": "In our third model, we attempt to combine the advantages of LDA and ROOTH-LDA by clustering arguments and predicates according to separate class vocabularies. Each observation is generated by two latent variables rather than one, which potentially allows the model to learn more flexible interactions between arguments and predicates.:",
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	"text": "(1) For each relation r, draw a multinomial distribution \u039e r over predicate classes from a Dirichlet with parameters \u03ba.",
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	"text": "(2) For each predicate class c, draw a multinomial \u03a8 c over predicate types and a multinomial \u0398 c over argument classes from Dirichlets with parameters \u03b3 and \u03b1 respectively.",
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	"text": "(3) For each argument class z, draw a multinomial distribution \u03a6 z over argument types from a Dirichlet with parameters \u03b2.",
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	"text": "(4) To generate an observation for r, draw a predicate class c from \u039e r , a predicate type from \u03a8 c , an argument class z from \u0398 c and an argument type from \u03a6 z .",
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	"text": "The resulting model can be written as:",
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	"text": "To estimate this model, we first resample the class assignments for all arguments in the data and then resample class assignments for all predicates.",
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	"text": "Other approaches are possible -resampling argument and then predicate class assignments for each observation in turn, or sampling argument and predicate assignments together by blocked samplingthough from our experiments it does not seem that the choice of scheme makes a significant difference.",
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	"text": "In the document modelling literature, probabilistic topic models are often evaluated on the likelihood they assign to unseen documents; however, it has been shown that higher log likelihood scores do not necessarily correlate with more semantically coherent induced topics (Chang et al., 2009) . One popular method for evaluating selectional preference models is by testing the correlation between their predictions and human judgements of plausibility on a dataset of predicate-argument pairs. This can be viewed as a more semantically relevant measurement of model quality than likelihood-based methods, and also permits comparison with nonprobabilistic models. In Section 5, we use two plausibility datasets to evaluate our models and compare to other previously published results.",
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	"text": "We trained our models on the 90-million word written component of the British National Corpus (Burnard, 1995) , parsed with the RASP toolkit (Briscoe et al., 2006) . Predicates occurring with just one argument type were removed, as were all tokens containing non-alphabetic characters; no other filtering was done. The resulting datasets consisted of 3,587,172 verb-object observations with 7,954 predicate types and 80,107 argument types, 3,732,470 noun-noun observations with 68,303 predicate types and 105,425 argument types, and 3,843,346 adjective-noun observations with 29,975 predicate types and 62,595 argument types.",
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	"text": "During development we used the verb-noun plausibility dataset from Pad\u00f3 et al. (2007) to direct the design of the system. Unless stated otherwise, all results are based on runs of 1,000 iterations with 100 classes, with a 200-iteration burnin period after which hyperparameters were reestimated every 50 iterations. 3 The probabilities estimated by the models (P (n\|v, r) for LDA and P (n, v\|r) for ROOTH-and DUAL-LDA) were sampled every 50 iterations post-burnin and averaged over three runs to smooth out variance. To compare plausibility scores for different predicates, we require the joint probability P (n, v\|r); as LDA does not provide this, we approximate P LDA (n, v\|r) = P BN C (v\|r)P LDA (n\|v, r), where P BN C (v\|r) is proportional to the frequency with which predicate v is observed as an instance of relation r in the BNC.",
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	"text": "For comparison, we reimplemented the methods of Rooth et al. (1999) and Pad\u00f3 et al. (2007) . As mentioned above, Rooth et al. use a latent-variable model similar to (4) but without priors, trained via EM. Our implementation (henceforth ROOTH-EM) chooses the number of classes from the range (20, 25, . . . , 50) through 5-fold cross-validation on a held-out log-likelihood measure. Settings outside this range did not give good results. Again, we run for 1,000 iterations and average predictions over LDA Pad\u00f3 et al. (2007) , a refinement of Erk (2007) , is a non-probabilistic method that smooths predicate-argument counts with counts for other observed arguments of the same predicate, weighted by the similarity between arguments. Following their description, we use a 2,000-dimensional space of syntactic co-occurrence features appropriate to the relation being predicted, weight features with the G 2 transformation and compute similarity with the cosine measure. Table 1 shows sample semantic classes induced by models trained on the corpus of BNC verb-object co-occurrences. LDA clusters nouns only, while ROOTH-LDA and ROOTH-EM learn classes that generate both nouns and verbs and DUAL-LDA clusters nouns and verbs separately. The LDA clusters are generally sensible: class 0 is exemplified by agreement and contract and class 1 by information and datum. There are some unintuitive blips, for example country appears between knowledge and understanding in class 2. The ROOTH-LDA classes also feel right: class 0 deals with nouns such as force, team and army which one might join, arm or lead and class 1 corresponds to \"things that can be opened or closed\" such as a door, an eye or a mouth (though the model also makes the questionable prediction that all these items can plausibly be locked or slammed). The DUAL-LDA classes are notably less coherent, especially when it comes to clustering verbs: DUAL-LDA's class 0V, like ROOTH-LDA's class 0, has verbs that take groups as objects but its class 1V mixes sensible conflations (turn, round) with very common verbs such as see and have and the unrelated break. The general impression given by inspection of the DUAL-LDA model is that it has problems with mixing and does not manage to learn a good model; we have tried a number of solutions (e.g., blocked sampling of argument and predicate classes), without overcoming this brittleness. Unsurprisingly, ROOTH-EM's classes have a similar feel to ROOTH-LDA; our general impression is that some of ROOTH-EM's classes look even more coherent than the LDAbased models, presumably because it does not use priors to smooth its per-class distributions. Keller and Lapata (2003) Keller and Lapata (2003) collected a dataset of human plausibility judgements for three classes of grammatical relation: verb-object, noun-noun modification and adjective-noun modification. The items in this dataset were not chosen to balance plausibility and implausibility (as in prior psycholinguistic experiments) but according to their corpus frequency, leading to a more realistic task. 30 predicates were selected for each relation; each predicate was matched with three arguments from different co-occurrence bands in the BNC, e.g., naughty-girl (high frequency), naughty-dog (medium) and naughty-lunch (low). Each predicate was also matched with three random arguments . .278 Table 2 : Results (Pearson r and Spearman \u03c1 correlations) on Keller and Lapata's (2003) plausibility data with which it does not co-occur in the BNC (e.g., naughty-regime, naughty-rival, naughty-protocol).",
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	"text": "In this way two datasets (Seen and Unseen) of 90 items each were assembled for each predicate. Table 2 presents results for a variety of predictive models -the Web frequencies reported by Keller and Lapata (2003) for two search engines, frequencies from the RASP-parsed BNC, 4 the reimplemented methods of Rooth et al. (1999) and Pad\u00f3 et al. (2007) , and the LDA, ROOTH-LDA and DUAL-LDA topic models. Following Keller and Lapata, we report Pearson correlation coefficients between log-transformed predicted frequencies and the goldstandard plausibility scores (which are already logtransformed). We also report Spearman rank correlations except where we do not have the original predictions (the Web count models), for completeness and because the predictions of preference models are may not be log-normally distributed as corpus counts are. Zero values (found only in the BNC frequency predictions) were smoothed by 0.1 to facilitate the log transformation; it seems natural to take a zero prediction as a non-specific prediction of very low plausibility rather than a \"missing value\" as is done in other work (e.g., Pad\u00f3 et al., 2007) .",
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	"text": "Despite their structural differences, LDA and ROOTH-LDA perform similarly -indeed, their predictions are highly correlated. ROOTH-LDA scores best overall, outperforming Pad\u00f3 et al.'s (2007) method and ROOTH-EM on every dataset and evaluation measure, and outperforming Keller and Lapata's (2003) Web predictions on every Un-seen dataset. LDA also performs consistently well, surpassing ROOTH-EM and Pad\u00f3 et al. on all but one occasion. For frequent predicate-argument pairs (Seen datasets), Web counts are clearly better; however, the BNC counts are unambiguously superior to LDA and ROOTH-LDA (whose predictions are based entirely on the generative model even for observed items) for the Seen verb-object data only. As might be suspected from the mixing problems observed with DUAL-LDA, this model does not perform as well as LDA and ROOTH-LDA, though it does hold its own against the other selectional preference methods.",
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	"text": "To identify significant differences between models, we use the statistical test for correlated correlation coefficients proposed by Meng et al. (1992) , which is appropriate for correlations that share the same gold standard. 5 For the seen data there are few significant differences: ROOTH-LDA and LDA are significantly better (p < 0.01) than Pad\u00f3 et al.'s model for Pearson's r on seen noun-noun data, and ROOTH-LDA is also significantly better (p < 0.01) using Spearman's \u03c1. For the unseen datasets, the BNC frequency predictions are unsurprisingly significantly worse at the p < 0.01 level than all smoothing models. LDA and ROOTH-LDA are significantly better (p < 0.01) than Pad\u00f3 et al. on every unseen dataset; ROOTH-EM is significantly better (p < 0.01) than Pad\u00f3 et al. on Unseen adjectives for both correlations. Meng et al.'s test does not find significant differences between ROOTH-EM and the LDA models despite the latter's clear advantages (a number of conditions do come close). This is because their predictions are highly correlated, which is perhaps unsurprising given that they are structurally similar models trained on the same data. We hypothesise that the main reason for the superior numerical performance of the LDA models over EM is the principled smoothing provided by the use of Dirichlet priors, which has a small but discriminative effect on model predictions. Collating the significance scores, we find that ROOTH-LDA achieves the most positive outcomes, followed by LDA and then by ROOTH-EM. DUAL-LDA is found significantly better than Pad\u00f3 et al.'s model on unseen adjectivenoun combinations, and significantly worse than the same model on seen adjective-noun data.",
	"cite_spans": [
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	"start": 132,
	"end": 150,
	"text": "Meng et al. (1992)",
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	"sec_num": "5.2"
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	"text": "Latent variable models that use EM for inference can be very sensitive to the number of latent variables chosen. For example, the performance of ROOTH-EM worsens quickly if the number of clusters is overestimated; for the Keller and Lapata datasets, settings above 50 classes lead to clear overfitting and a precipitous drop in Pearson correlation scores. On the other hand, Wallach et al. (2009) demonstrate that LDA is relatively insensitive to the choice of topic vocabulary size Z when the \u03b1 and \u03b2 hyperparameters are optimised appropriately during estimation. Figure 1 plots the effect of Z on Spearman correlation for the LDA model. In general, Wallach et al.'s finding for document modelling transfers to selectional preference models; within the range Z = 50-200 performance remains at a roughly similar level. In fact, we do not find that performance becomes significantly less robust when hyperparameter reestimation is deactiviated; correlation scores simply drop by a small amount (1-2 points), irrespective of the Z chosen. ROOTH-LDA (not graphed) seems slightly more sensitive to Z; this may be because the \u03b1 parameters in this model operate on the relation level rather than the document level and thus fewer \"ob-servations\" of class distributions are available when reestimating them. Bergsma et al. (2008) As mentioned in Section 2.1, Bergsma et al. (2008) propose a discriminative approach to preference learning. As part of their evaluation, they compare their approach to a number of others, including that of Erk (2007) , on a plausibility dataset collected by Holmes et al. (1989) . This dataset consists of 16 verbs, each paired with one plausible object (e.g., write-letter) and one implausible object (write-market). Bergsma et al.'s model, trained on the 3GB AQUAINT corpus, is the only model reported to achieve perfect accuracy on distinguishing plausible from implausible arguments. It would be interesting to do a full comparison that controls for size and type of corpus data; in the meantime, we can report that the LDA and ROOTH-LDA models trained on verb-object observations in the BNC (about 4 times smaller than AQUAINT) also achieve a perfect score on the Holmes et al. data. 6 6 Conclusions and future work This paper has demonstrated how Bayesian techniques originally developed for modelling the topical structure of documents can be adapted to learn probabilistic models of selectional preference. These models are especially effective for estimating plausibility of low-frequency items, thus distinguishing rarity from clear implausibility.",
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	{
	"text": "The models presented here derive their predictions by modelling predicate-argument plausibility through the intermediary of latent variables. As observed in Section 5.2 this may be a suboptimal strategy for frequent combinations, where corpus counts are probably reliable and plausibility judgements may be affected by lexical collocation effects. One principled method for folding corpus counts into LDA-like models would be to use hierarchical priors, as in the n-gram topic model of Wallach (2006) . Another potential direction for system improvement would be an integration of our generative model with Bergsma et al.'s (2008) discriminative model -this could be done in a number of ways, including using the induced classes of a topic model as features for a discriminative classifier or using the discriminative classifier to produce additional high-quality training data from noisy unparsed text.",
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	"text": "Comparison to plausibility judgements gives an intrinsic measure of model quality. As mentioned in the Introduction, selectional preferences have many uses in NLP applications, and it will be interesting to evaluate the utility of Bayesian preference models in contexts such as semantic role labelling or human sentence processing modelling. The probabilistic nature of topic models, coupled with an appropriate probabilistic task model, may facilitate the integration of class induction and task learning in a tight and principled way. We also anticipate that latent variable models will prove effective for learning selectional preferences of semantic predicates (e.g., FrameNet roles) where direct estimation from a large corpus is not a viable option.",
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	"text": "At time of writing, Google estimates 855 hits for \"a\|the carrot\|carrots laugh\|laughs\|laughed\" and 0 hits for \"a\|the manservant\|manservants\|menservants laugh\|laughs\|laughed\"; many of the carrot hits are false positives but a significant number are true subject-verb observations.",
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	"section": "",
	"sec_num": null
	},
	{
	"text": "http://mallet.cs.umass.edu/",
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	"text": "These settings were based on the MALLET defaults; we have not yet investigated whether modifying the simulation length or burnin period is beneficial.",
	"cite_spans": [],
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	"section": "",
	"sec_num": null
	},
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	"text": "The correlations presented here for BNC counts are notably better than those reported byKeller and Lapata (2003), presumably reflecting our use of full parsing rather than shallow parsing.",
	"cite_spans": [],
	"ref_spans": [],
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	"sec_num": null
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	"text": "We cannot compare our data to Keller and Lapata's Web counts as we do not possess their per-item scores.",
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	"section": "",
	"sec_num": null
	},
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	"text": "Bergsma et al. report that all plausible pairs were seen in their corpus; three were unseen in ours, as well as 12 of the implausible pairs.",
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	],
	"back_matter": [
	{
	"text": "This work was supported by EPSRC grant EP/G051070/1. I am grateful to Frank Keller and Mirella Lapata for sharing their plausibility data, and to Andreas Vlachos and the anonymous ACL and CoNLL reviewers for their helpful comments.",
	"cite_spans": [],
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	"section": "Acknowledgements",
	"sec_num": null
	}
	],
	"bib_entries": {
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