Articles

NLP systems for tasks such as question answering and information extraction typ- ically rely on statistical parsers. But the ef- ficacy of such parsers can be surprisingly low, particularly for sentences drawn from heterogeneous corpora such as the Web. We have observed that incorrect parses of- ten result in wildly …...

We propose a supervised, two-phase framework to address the problem of para- phrase recognition (PR). Unlike most PR systems that focus on sentence similarity, our framework detects dissimilarities be- tween sentences and makes its paraphrase judgment based on the significance of such dissimilarities. The ability to differenti- ate significant dissimilarities …...

In this paper we introduce an empirical approach to the semantic interpretation of superlative adjectives. We present a cor- pus annotated for superlatives and pro- pose an interpretation algorithm that uses a wide-coverage parser and produces se- mantic representations. We achieve F- scores between 0.84 and 0.91 for detecting attributive …...

Spoken monologues feature greater sen- tence length and structural complexity than do spoken dialogues. To achieve high parsing performance for spoken mono- logues, it could prove effective to sim- plify the structure by dividing a sentence into suitable language units. This paper proposes a method for dependency pars- ing of …...

We present two discriminative methods for name transliteration. The methods correspond to local and global modeling approaches in modeling structured output spaces. Both methods do not require alignment of names in different lan- guages – their features are computed di- rectly from the names themselves. We perform an experimental evaluation …...

Named-entity recognition systems extract entities such as people, organizations, and locations from unstructured text. Rather than extract these mentions in isolation, this paper presents a record extraction sys- tem that assembles mentions into records (i.e. database tuples). We construct a probabilistic model of the compatibility between field values, then employ …...

In this paper we approach word sense disambiguation and information extrac- tion as a unified tagging problem. The task consists of annotating text with the tagset defined by the 41 Wordnet super- sense classes for nouns and verbs. Since the tagset is directly related to Wordnet synsets, the tagger returns …...

This paper explores the use of two graph algorithms for unsupervised induction and tagging of nominal word senses based on corpora. Our main contribution is the op- timization of the free parameters of those algorithms and its evaluation against pub- licly available gold standards. We present a thorough evaluation comprising …...

In this paper we show that generative models are competitive with and some- times superior to discriminative models, when both kinds of models are allowed to learn structures that are optimal for dis- crimination. In particular, we compare Bayesian Networks and Conditional log- linear models on two NLP tasks. We …...

We propose a general method for reranker construction which targets choosing the candidate with the least expected loss, rather than the most probable candidate. Different approaches to expected loss ap- proximation are considered, including es- timating from the probabilistic model used to generate the candidates, estimating from a discriminative model …...

The end-to-end performance of natural language processing systems for com- pound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline archi- tecture, which causes errors to propagate and compound at each stage. We present a novel architecture, which models these pipelines …...

Word clustering is important for automatic thesaurus construction, text classification, and word sense disambiguation. Recently, several studies have reported using the web as a corpus. This paper proposes an unsupervised algorithm for word clus- tering based on a word similarity mea- sure by web counts. Each pair of words is …...

This paper presents an empirical eval- uation of the quality of publicly avail- able large-scale knowledge resources. The study includes a wide range of manu- ally and automatically derived large-scale knowledge resources. In order to establish a fair and neutral comparison, the qual- ity of each knowledge resource is indi- …...

The Arabic language is a collection of spoken dialects with important phonolog- ical, morphological, lexical, and syntac- tic differences, along with a standard writ- ten language, Modern Standard Arabic (MSA). Since the spoken dialects are not officially written, it is very costly to obtain adequate corpora to use for training …...

Multimodal grammars provide an expres- sive formalism for multimodal integra- tion and understanding. However, hand- crafted multimodal grammars can be brit- tle with respect to unexpected, erroneous, or disfluent inputs. Spoken language (speech-only) understanding systems have addressed this issue of lack of robustness of hand-crafted grammars by exploiting classification techniques …...

We describe an implementation of data- driven selection of emphatic facial dis- plays for an embodied conversational agent in a dialogue system. A corpus of sentences in the domain of the target dia- logue system was recorded, and the facial displays used by the speaker were anno- tated. The data …...

We present the psycholinguistically moti- vated task of predicting human plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task, and com- pare it with a standard role labeller. For both tasks, our model benefits from class- …...

All questions are implicitly associated with an expected answer type. Unlike previous approaches that require a prede- fined set of question types, we present a method for dynamically constructing a probability-based answer type model for each different question. Our model evaluates the appropriateness of a poten- tial answer by the …...

We investigate the lexical and syntactic flexibility of a class of idiomatic expres- sions. We develop measures that draw on such linguistic properties, and demon- strate that these statistical, corpus-based measures can be successfully used for dis- tinguishing idiomatic combinations from non-idiomatic ones. We also propose a means for automatically …...

In this paper we present TroFi (Trope Finder), a system for automatically classi- fying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses lit- eral and nonliteral seed …...