Articles

Combinatory Categorial Grammar (CCG) is generally construed as a fully lexicalized formalism, where all grammars use one and the same universal set of rules, and cross- linguistic variation is isolated in the lexicon. In this paper, we show that the weak gener- ative capacity of this ‘pure’ form of CCG …...

Linear Context-Free Rewriting Systems (LCFRSs) are a grammar formalism ca- pable of modeling discontinuous phrases. Many parsing applications use LCFRSs where the fan-out (a measure of the dis- continuity of phrases) does not exceed 2. We present an efficient algorithm for opti- mal reduction of the length of production right-hand …...

Dominance links were introduced grammars to model long distance scram-in bling phenomena, motivating the defi- nition of multiset-valued linear indexed grammars (MLIGs) by Rambow (1994b), and inspiring quite a few recent for- malisms. It turns out that MLIGs have since been rediscovered and reused in a variety of contexts, and …...

Conditional Random Fields (CRFs) are a widely-used approach for supervised sequence labelling, notably due to their ability to handle large description spaces and to integrate structural dependency be- tween labels. Even for the simple linear- chain model, taking structure into account implies a number of parameters and a computational effort …...

We combine two complementary ideas for learning supertaggers from highly am- biguous lexicons: grammar-informed tag transitions and models minimized via in- teger programming. Each strategy on its own greatly improves performance over basic expectation-maximization training with a bitag Hidden Markov Model, which we show on the CCGbank and CCG-TUT corpora. …...

The Viterbi algorithm is the conventional decoding algorithm most widely adopted for sequence labeling. Viterbi decoding is, however, prohibitively slow when the label set is large, because its time com- plexity is quadratic in the number of la- bels. This paper proposes an exact decod- ing algorithm that overcomes this …...

We present a novel approach to integrate transliteration into Hindi-to-Urdu statisti- cal machine translation. We propose two probabilistic models, based on conditional and joint probability formulations, that are novel solutions to the problem. Our mod- els consider both transliteration and trans- lation when translating a particular Hindi word given the …...

We present a novel scheme to apply fac- tored phrase-based SMT to a language pair with very disparate morphological struc- tures. Our approach relies on syntac- tic analysis on the source side (English) and then encodes a wide variety of local and non-local syntactic structures as com- plex structural tags …...

This paper improves the use of pseudo- words as an evaluation framework for selectional preferences. While pseudo- words originally evaluated word sense disambiguation, they are now commonly used to evaluate selectional preferences. A selectional preference model ranks a set of possible arguments for a verb by their se- mantic fit …...

This paper describes the application of so-called topic models to selectional pref- erence induction. Three models related to Latent Dirichlet Allocation, a proven method for modelling document-word co- occurrences, are presented and evaluated on datasets of human plausibility judge- ments. Compared to previously proposed techniques, these models perform very competitively, …...

In this paper, we adopt two views, personal and impersonal views, and systematically employ them in both supervised and semi-supervised sentiment classification. Here, personal views consist of those sentences which directly express speaker’s feeling and preference towards a target object while impersonal views focus on statements towards a target object …...

Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierar- chical relationships of products attributes is not fully utilized. (2) Reviews or sen- tences mentioning several attributes asso- ciated with complicated sentiments are not dealt with very well. In this paper, we …...

Automatically identifying the polarity of words is a very important task in Natural Language Processing. It has applications in text classification, text filtering, analysis of product review, analysis of responses to surveys, and mining online discussions. We propose a method for identifying the polarity of words. We apply a Markov …...

Several attempts have been made to learn phrase translation probabilities for phrase- based statistical machine translation that go beyond pure counting of phrases in word-aligned training data. Most approaches report problems with over- fitting. We describe a novel leaving- one-out approach to prevent over-fitting that allows us to train phrase …...

If we take an existing supervised NLP sys- tem, a simple and general way to improve accuracy is to use unsupervised word representations as extra word features. We evaluate Brown clusters, Collobert and Weston (2008) embeddings, and HLBL (Mnih & Hinton, 2009) embeddings of words on both NER and chunking. …...

The computation of selectional prefer- ences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present LDA-SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent top- ics and topic distributions over relations, LDA-SP combines the benefits …...

This paper demonstrates that the use of ensemble methods and carefully calibrat- ing the decision threshold can signifi- cantly improve the performance of ma- chine learning methods for morphologi- cal word decomposition. We employ two algorithms which come from a family of generative probabilistic models. The mod- els consider segment …...

Finding allowable places in words to insert hyphens is an important practical prob- lem. The algorithm that is used most of- ten nowadays has remained essentially un- changed for 25 years. This method is the TEX hyphenation algorithm of Knuth and Liang. We present here a hyphenation method that is …...

We investigate active learning methods for Japanese dependency parsing. We propose active learning methods of using partial dependency relations in a given sentence for parsing and evaluate their effective- ness empirically. Furthermore, we utilize syntactic constraints of Japanese to ob- tain more labeled examples from precious labeled ones that annotators …...

We propose a novel self-training method for a parser which uses a lexicalised gram- mar and supertagger, focusing on increas- ing the speed of the parser rather than its accuracy. The idea is to train the su- pertagger on large amounts of parser out- put, so that the supertagger can …...