Papers Prepositional Phrase Attachment

How Well Do Large Language Models Understand Syntax? An Evaluation by Asking Natural Language Questions

While recent advancements in large language models (LLMs) bring us closer to achieving artificial general intelligence, the question persists: Do LLMs truly understand language, or do they merely mimic comprehension thro…

Resolving Prepositional Phrase Attachment Ambiguities with Contextualized Word Embeddings

This paper applies contextualized word embedding models to a long-standing problem in the natural language parsing community, namely prepositional phrase attachment. Following past formulations of this problem, we use da…

Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge

We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity. To date, proposed solutions are either rule-based, where explicit grammar rules direct how to resolve ambiguities; or statis…

Grounded PCFG Induction with Images

Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text. This work propos…

When Beards Start Shaving Men: A Subject-object Resolution Test Suite for Morpho-syntactic and Semantic Model Introspection

In this paper, we introduce the SORTS Subject-Object Resolution Test Suite of German minimal sentence pairs for model introspection. The full test suite consists of 18,502 transitive clauses with manual annotations of 8 …

Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics

An important characteristic of English written text is the abundance of noun compounds - sequences of nouns acting as a single noun, e.g., colon cancer tumor suppressor protein. While eventually mastered by domain expert…

Using Automatically Extracted Minimum Spans to Disentangle Coreference Evaluation from Boundary Detection

The common practice in coreference resolution is to identify and evaluate the maximum span of mentions. The use of maximum spans tangles coreference evaluation with the challenges of mention boundary detection like prepo…

SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags

In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly w…

Madly Ambiguous: A Game for Learning about Structural Ambiguity and Why It's Hard for Computers

Madly Ambiguous is an open source, online game aimed at teaching audiences of all ages about structural ambiguity and why it{'}s hard for computers. After a brief introduction to structural ambiguity, users are challenge…

Tel(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery

Incorporating Contextual Information for Language-Independent, Dynamic Disambiguation Tasks

Adding Syntactic Annotations to Flickr30k Entities Corpus for Multimodal Ambiguous Prepositional-Phrase Attachment Resolution

Prepositional Phrase Attachment over Word Embedding Products

We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task). Our model exploits tensor products of word embeddings, capturing all possible conjunctions of latent e…

Correcting prepositional phrase attachments using multimodal corpora

PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a cor…

Ontology-Aware Token Embeddings for Prepositional Phrase Attachment

Type-level word embeddings use the same set of parameters to represent all instances of a word regardless of its context, ignoring the inherent lexical ambiguity in language. Instead, we embed semantic concepts (or synse…

Predicting Target Language CCG Supertags Improves Neural Machine Translation

Neural machine translation (NMT) models are able to partially learn syntactic information from sequential lexical information. Still, some complex syntactic phenomena such as prepositional phrase attachment are poorly mo…

Machine Reading with Background Knowledge

Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Underst…

Resolving Language and Vision Ambiguities Together: Joint Segmentation \& Prepositional Attachment Resolution in Captioned Scenes

Human-in-the-Loop Parsing

Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes

We present an approach to simultaneously perform semantic segmentation and prepositional phrase attachment resolution for captioned images. Some ambiguities in language cannot be resolved without simultaneously reasoning…