We show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. We present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new co...

There are some sorts of ‘Preposition + Noun’ combinations in Farsi that apparently a Prepositional Phrase almost behaves as Compound Prepositions. As they are not completely behaving as compounds, it is doubtful that the process of word formation is a morphological one. The analysis put forward by this paper proposes “incorporation” by which an N is incorporated to a P constructing a compound p...

We propose a Case-driven account of the “reanalysis” operation assumed by some researchers to underlie pseudo-passivization. Contra previous analyses we argue that reanalyzed prepositions do not form a unit with the verb at any level of representation. Rather, reanalyzed prepositions raise to a v/V-medial Agr head, and the complement of the preposition raises to the specifier of this complex he...

We present an unsupervised approach that can be applied to test corrections tasks such as real-word error correction, near-synonym choice, and preposition choice, using n-grams from the Google Web 1T dataset. We present in details the method for correcting preposition errors, which has two phases. We categorize the n-gram types based on the position of the gap that needs to be replaced with a p...

The paper discusses the syntax and semantics of the Norwegian preposition med, which denotes a variety of concomitance relations. The c-structural, f-structural and semantic properties of the preposition are examined. Special emphasis is put on the constructions which involve syntax-semantics mismatches, such as bare noun phrases denoting sets of states instead of sets of individuals and it is ...

Considerable recent progress has been made in preposition disambiguation using the SemEval 2007 corpus, with results reaching accuracy of over 88 percent. However, with a new corpus of tagged instances, use of the models shows a decline in performance to around 43 percent. This suggests that recent efforts suffer from an out-of-domain problem. Detailed examination of the dimensions of this prob...

Prepositions in English are a well-known challenge for language learners, and the computational analysis of preposition usage has attracted significant attention. Such research generally starts out by developing models of preposition usage for native English based on a range of features, from shallow surface evidence to deep linguistically-informed properties. While we agree that ultimately a c...

Our previous study on disambiguating the preposition “with” (using WordNet for hypernym and meronym relations, LCS for verb and preposition lexical information, and features of head and complement) looked promising enough to warrant study for other prepositions. Through investigation of ten frequently used prepositions, this paper describes general senses of prepositions and sense-case definiti...