Techniques that compare short text segments using dependency paths (or simply, paths) appear in a wide range of automated language processing applications including question answering (QA).

In this paper, we introduce a flexible notion of paths that describe chains of words on a dependency path .

Dependency paths (or simply, paths) are compared using techniques such as tree edit distance (Punyakanok et al., 2004; Heilman and Smith, 2010), relation probability (Gao et al., 2004) and parse tree alignment (Wang et al., 2007; Park et al., 2011).

Much work on sentence similarity using dependency paths focuses on question answering (QA) where textual inference requires attention to linguistic detail.

In this paper, we explore a flexible application of dependency paths that overcomes this difficulty.

Techniques that compare short text segments using dependency paths are applied to a wide range of automated language processing tasks, including paraphrasing, summarization, entailment detection, QA, machine translation and the evaluation of word, phrase and sentence similarity.

A generic approach uses a matching function to compare a dependency path between any two stemmed terms cc and y in a sentence A with any dependency path between cc and y in sentence B.

The match score for A and B is computed over all dependency paths in A.

where s and 7“ denote “source” and “receiver” arguments, which are political actor entities in a predefined set 5, t is a timestep (i.e., a 7-day period) derived from the article’s published date, and wpredpath is a textual predicate expressed as a dependency path that typically includes a verb (we use the terms “predicate-pat ” and “verb-pat ” interchangeably).

Verb paths are identified by looking at the shortest dependency path between two mentions in a sentence.

Many of our dependency paths, when traversed from the source to receiver direction, also follow surface order, due to English’s SVO word order.6 Therefore we convert each path to a word sequence and match against the TABARI lexicon—plus a few modifications for differences in infinitives and stemming—and find 528 dependency path matches.

We also create a baseline El-regularized logistic regression that uses normalized dependency path counts as the features (10,457 features).

o For each frame k, draw a multinomial distribution of dependency paths, gbk; N Dir(b / V) (where V is the number of dependency path types).

The vanilla model is capable of inducing frames through dependency path co-occurences, when multiple events occur in a given context.