Articles

This paper establishes a connection be- tween two apparently very different kinds of probabilistic models. Latent Dirich- let Allocation (LDA) models are used as “topic models” to produce a low- dimensional representation of documents, while Probabilistic Context-Free Gram- mars (PCFGs) define distributions over trees. The paper begins by showing that …...

This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a topic model to decom- pose this conditional probability into two conditional probabilities with latent vari- ables. We propose three different instanti- ations …...

Probabilistic latent topic models have re- cently enjoyed much success in extracting and analyzing latent topics in text in an un- supervised way. One common deficiency of existing topic models, though, is that they would not work well for extracting cross-lingual latent topics simply because words in different languages generally …...

We present a new approach to cross- language text classification that builds on structural correspondence learning, a re- cently proposed theory for domain adap- tation. The approach uses unlabeled doc- uments, along with a simple word trans- lation oracle, in order to induce task- specific, cross-lingual word correspon- dences. We …...

This paper explores joint syntactic and seman- tic parsing of Chinese to further improve the performance of both syntactic and semantic parsing, in particular the performance of se- mantic parsing (in this paper, semantic role labeling). This is done from two levels. Firstly, an integrated parsing approach is proposed to …...

For languages with (semi-) free word or- der (such as German), labelling gramma- tical functions on top of phrase-structural constituent analyses is crucial for making them interpretable. Unfortunately, most statistical classifiers consider only local information for function labelling and fail to capture important restrictions on the distribution of core argument …...

We present a simple but accurate parser which exploits both large tree fragments and symbol refinement. We parse with all fragments of the training set, in con- trast to much recent work on tree se- lection in data-oriented parsing and tree- substitution grammar learning. We re- quire only simple, deterministic …...

Incremental parsing techniques such as shift-reduce have gained popularity thanks to their efficiency, but there remains a major problem: the search is greedy and only explores a tiny fraction of the whole space (even with beam search) as op- posed to dynamic programming. We show that, surprisingly, dynamic programming is …...

A characterization of the expressive power of synchronous tree-adjoining grammars (STAGs) in terms of tree transducers (or equivalently, synchronous tree substitution grammars) is developed. Essentially, a STAG corresponds to an extended tree transducer that uses explicit substitution in both the input and output. This characteri- zation allows the easy integration …...

In this paper we propose a method for the automatic decipherment of lost languages. Given a non-parallel corpus in a known re- lated language, our model produces both alphabetic mappings and translations of words into their corresponding cognates. We employ a non-parametric Bayesian framework to simultaneously capture both low-level character …...

We present a novel approach to Informa- tion Presentation (IP) in Spoken Dialogue Systems (SDS) using a data-driven statis- tical optimisation framework for content planning and attribute selection. First we collect data in a Wizard-of-Oz (WoZ) ex- periment and use it to build a supervised model of human behaviour. This …...

A fundamental step in sentence compre- hension involves assigning semantic roles to sentence constituents. To accomplish this, the listener must parse the sentence, find constituents that are candidate argu- ments, and assign semantic roles to those constituents. Each step depends on prior lexical and syntactic knowledge. Where do children learning …...

Substantial research effort has been in- vested in recent decades into the com- putational study and automatic process- ing of multi-party conversation. While most aspects of conversational speech have benefited from a wide availabil- ity of analytic, computationally tractable techniques, only qualitative assessments are available for characterizing multi-party turn-taking. The …...

We describe a novel approach to unsuper- vised learning of the events that make up a script, along with constraints on their temporal ordering. We collect natural- language descriptions of script-specific event sequences from volunteers over the Internet. Then we compute a graph rep- resentation of the script’s temporal struc- …...

Most supervised language processing sys- tems show a significant drop-off in per- formance when they are tested on text that comes from a domain significantly different from the domain of the training data. Semantic role labeling techniques are typically trained on newswire text, and in tests their performance on fiction …...

We argue that groups of unannotated texts with overlapping and non-contradictory semantics represent a valuable source of information for learning semantic repre- sentations. A simple and efficient infer- ence method recursively induces joint se- mantic representations for each group and discovers correspondence between lexical entries and latent semantic concepts. We …...

We present a syntactically enriched vec- tor model that supports the computation of contextualized semantic representations in a quasi compositional fashion. It em- ploys a systematic combination of first- and second-order context vectors. We apply our model to two different tasks and show that (i) it substantially outperforms previ- ous …...

This paper introduces algorithms for non- projective parsing based on dual decomposi- tion. We focus on parsing algorithms for non- projective head automata, a generalization of head-automata models to non-projective struc- tures. The dual decomposition algorithms are simple and efficient, relying on standard dy- namic programming and minimum spanning tree …...

The rapid growth of geotagged social media raises new computational possibilities for in- vestigating geographic linguistic variation. In this paper, we present a multi-level generative model that reasons jointly about latent topics and geographical regions. High-level topics such as “sports” or “entertainment” are ren- dered differently in each geographic region, …...

Determining whether a textual phrase denotes a functional relation (i.e., a relation that maps each domain element to a unique range el- ement) is useful for numerous NLP tasks such as synonym resolution and contradic- tion detection. Previous work on this prob- lem has relied on either counting methods or …...