Articles

We analyze humorous spoken conversa- tions from a classic comedy television show, FRIENDS, by examining acoustic- prosodic and linguistic features and their utility in automatic humor recognition. Using a simple annotation scheme, we au- tomatically label speaker turns in our cor- pus that are followed by laughs as hu- morous …...

We consider the problem of constructing a directed acyclic graph that encodes tem- poral relations found in a text. The unit of our analysis is a temporal segment, a frag- ment of text that maintains temporal co- herence. The strength of our approach lies in its ability to simultaneously optimize …...

Semantic lexical matching is a prominent subtask within text understanding applica- tions. Yet, it is rarely evaluated in a di- rect manner. This paper proposes a def- inition for lexical reference which cap- tures the common goals of lexical match- ing. Based on this definition we created and analyzed a …...

We propose a conditional random field- based method for supertagging, and ap- ply it to the task of learning new lexi- cal items for HPSG-based precision gram- mars of English and Japanese. Us- ing a pseudo-likelihood approximation we are able to scale our model to hun- dreds of supertags and …...

This paper describes an extremely lexi- calized probabilistic model for fast and accurate HPSG parsing. In this model, the probabilities of parse trees are de- fined with only the probabilities of select- ing lexical entries. The proposed model is very simple, and experiments revealed that the implemented parser runs around …...

Complex tasks like question answering need to be able to identify events in text and the relations among those events. We show that this event identification task and a related task, identifying the seman- tic class of these events, can both be for- mulated as classification problems in a word-chunking …...

Discriminative learning methods are widely used in natural language process- ing. These methods work best when their training and test data are drawn from the same distribution. For many NLP tasks, however, we are confronted with new domains in which labeled data is scarce or non-existent. In such cases, we …...

Integer Linear Programming has recently been used for decoding in a number of probabilistic models in order to enforce global constraints. However, in certain ap- plications, such as non-projective depen- dency parsing and machine translation, the complete formulation of the decod- ing problem as an integer linear program renders solving …...

This paper presents a comparative study of probabilistic treebank parsing of Ger- man, using the Negra and T¨uBa-D/Z tree- banks. Experiments with the Stanford parser, which uses a factored PCFG and dependency model, show that, contrary to previous claims for other parsers, lexical- ization of PCFG models boosts parsing performance …...

Citation function is defined as the author’s reason for citing a given paper (e.g. ac- knowledgement of the use of the cited method). The automatic recognition of the rhetorical function of citations in scientific text has many applications, from improve- ment of impact factor calculations to text summarisation and more …...

In this paper we address the issue of au- tomatically assigning information status to discourse entities. Using an annotated cor- pus of conversational English and exploit- ing morpho-syntactic and lexical features, we train a decision tree to classify entities introduced by noun phrases as old, medi- ated, or new. We …...

In this paper we study the utility of dis- course structure for spoken dialogue per- formance modeling. We experiment with various ways of exploiting the discourse structure: in isolation, as context infor- mation for other factors (correctness and certainty) and through trajectories in the discourse structure hierarchy. Our corre- lation …...

This paper demonstrates two methods to improve the performance of instance- based learning (IBL) algorithms for the problem of Semantic Role Labeling (SRL). Two IBL algorithms are utilized: k-Nearest Neighbor (kNN), and Priority Maximum Likelihood (PML) with a modified back-off combination method. The experimental data are the WSJ23 and Brown …...

This paper describes our attempt NomBank-based automatic Semantic Roleat Labeling (SRL). NomBank is a project at New York University to annotate the ar- gument structures for common nouns in the Penn Treebank II corpus. We treat the NomBank SRL task as a classifica- tion problem and explore the possibility of …...

Reordering is currently one of the most important problems in statistical machine translation systems. This paper presents a novel strategy for dealing with it: sta- tistical machine reordering (SMR). It con- sists in using the powerful techniques de- veloped for statistical machine translation (SMT) to translate the source language (S) …...

We investigate the impact of parse quality on a syntactically-informed statistical ma- chine translation system applied to techni- cal text. We vary parse quality by vary- ing the amount of data used to train the parser. As the amount of data increases, parse quality improves, leading to im- provements in …...

We discuss different strategies for smooth- ing the phrasetable in Statistical MT, and give results over a range of translation set- tings. We show that any type of smooth- ing is a better idea than the relative- frequency estimates that are often used. The best smoothing techniques yield con- sistent …...

We introduce SPMT, a new class of sta- tistical Translation Models that use Syn- tactified target language Phrases. The SPMT models outperform a state of the art phrase-based baseline model by 2.64 Bleu points on the NIST 2003 Chinese-English test corpus and 0.28 points on a human- based quality metric …...

In this paper, we present ParaEval, an automatic evaluation framework that uses paraphrases to improve the quality of machine translation evaluations. Previous work has focused on fixed n-gram evaluation metrics coupled with lexical identity matching. ParaEval addresses three important issues: support for para- phrase/synonym matching, recall meas- urement, and correlation …...

This paper demonstrates how unsupervised tech- niques can be used to learn models of deep linguis- tic structure. Determining the semantic roles of a verb’s dependents is an important step in natural language understanding. We present a method for learning models of verb argument patterns directly from unannotated text. The …...