This paper explores the task of building an accurate prepositional phrase attachment corpus for new genres while avoiding a large investment in terms of time and money by crowdsourcing judgments. We develop and present a system to extract prepositional phrases and their potential attachments from ungrammatical and informal sentences and pose the subsequent disambiguation tasks as multiple choic...

This project studies semi-supervised discovery of named entities, relational entities and prepositional phrase attachments within a read-the-web framework. Meanings of an entity can be improvised and updated faster in the internet world than printed references. The main idea of this project is to study the feasibility of characterizing entities by web content directly. The approach is that cont...

We have developed a method to resolve ambiguities in prepositional phrase (PP) attachment in German. We measure on the one hand the cooccurrence strength between nouns (N) and prepositions (P) and on the other hand between verbs (V) and prepositions. The competing values of N+P versus V+P are used to decide whether to attach a prepositional phrase to the noun or to the verb. We calculate the co...

Prepositional phrase attachment is a common source of ambiguity in natural language processing. We present an unsupervised corpus-based approach to prepositional phrase attachment that achieves similar performance to supervised methods. Unlike previous unsupervised approaches in which training data is obtained by heuristic extraction of unambiguous examples from a corpus, we use an iterative pr...

This paper investigates the use of machine learning algorithms to label modifier-noun compounds with a semantic relation. The attributes used as input to the learning algorithms are the web frequencies for phrases containing the modifier, noun, and a prepositional joining term. We compare and evaluate different algorithms and different joining phrases on Nastase and Szpakowicz’s (2003) dataset ...

The paper describes an experiment in detecting a specific type of multiword expressions in Russian, namely expressions starting with a preposition. This covers not only prepositional phrases proper, but also fixed syntactic constructions like v techenie (‘in the course of’). First, we collect lists of such constructions in a corpus of 50 mln words using a simple mechanism that combines statisti...

This paper examines the information humans use to deal with ambiguous prepositional phrase attachments. The work is done using a large corpus of eye-tracking data for both English and French. Multiple regression and linear mixed effect models are used to examine the significance of various factors. Variables that have been shown to effect reading time in experimental settings such as attachment...

Chinese maximal-length phrases (maximal-length noun phrases and prepositional phrases) possess notable linguistic properties. Bidirectional labeling results of the Chinese maximal-length phrases obtained by sequential classifiers reveal the complementary properties in the two directions of Chinese sentences. In this paper, both left-right and right-left sequential labeling are used to identify ...

In this paper we will describe a process for mining syntactical verbal subcategorization, i.e. the information about the kind of phrases or clauses a verb goes with. We will use a large text corpus having almost 10,000,000 tagged words as our resource material. Loglinear modeling is used to analyze and automatically identify the subcategorization dependencies. An unsupervised clustering algorit...

In this paper, an efficient algorithm to compute the set of prime implicants of a prepositional formula in Conjunc-tive Normal Form (CNF) is presented. The proposed algorithm uses a concept of representing the formula as a bi-nary matrix and computing paths through the matrix as implicants. The algorithm finds the prime implicants as theprime paths using the divide-and-conquer techn...