Coarse Lexical Frame Acquisition at the Syntax–Semantics Interface Using a Latent-Variable PCFG Model

Abstract

We present a method for unsupervised lexical frame acquisition at the syntax-semantics interface. Given a set of input strings derived from dependency parses, our method generates a set of clusters that resemble lexical frame structures. Our work is motivated not only by its practical applications (e.g., to build, or expand the coverage of lexical frame databases), but also to gain linguistic insight into frame structures with respect to lexical distributions in relation to grammatical structures. We model our task using a hierarchical Bayesian network and employ tools and methods from latent variable probabilistic context free grammars (L-PCFGs) for statistical inference and parameter fitting, for which we propose a new split and merge procedure. We show that our model outperforms several baselines on a portion of the Wall Street Journal sentences that we have newly annotated for evaluation purposes.

SemEval-2019 Task 2: Unsupervised Lexical Frame Induction

“…The baseline systems, the unsupervised method of Kallmeyer et al (2018) Table 2: Summary of Results. The BASELINE for Task A is 1CPH, and for B.1 and B.2 is 1CPHG.…”

“…Since the focus is usually to build language resources, most systems (Pennacchiotti et al (2008); Green et al (2004)) have used a lexical semantic resource like WordNet (Miller, 1995) to extend coverage of a resource like FrameNet. Some methods, e.g., Modi et al (2012) and Kallmeyer et al (2018), tried to extract FrameNetlike resources automatically without additional semantic information. Others (Ustalov et al (2018); Materna (2012)) addressed frame induction only for verbs with two arguments.…”

HHMM at SemEval-2019 Task 2: Unsupervised Frame Induction using Contextualized Word Embeddings

“…For the 123 baseline, we assign its index to each highlighted slot filler. For example, if five slots need to be labelled, the first one will be labelled as 1 and the last one will be labelled as 5. formed LPCFG (Kallmeyer et al, 2018) and all the standard baselines, including cluster per dependency role (OneClustPerGrType), on the development dataset. 4 Similarly to our solution for Subtask A (Section 2), we tried different clustering algorithms to cluster arguments of verbs to generic roles and found that the best clustering performance is shown by agglomerative clustering with Euclidean affinity, Ward's method linkage, and two clusters.…”

L2F/INESC-ID at SemEval-2019 Task 2: Unsupervised Lexical Semantic Frame Induction using Contextualized Word Representations

“…On the test set, we also consider a random assignment to the gold number of clus- ters as a baseline. Due to space constraints, we do not report the results of the remaining baselines proposed by Kallmeyer et al (2018). We report the results of an additional baseline for Task B.2 which considers both the argument's syntactic relation to the head verb and its Part-of-Speech (POS) tag (Dep + POS).…”