Articles

Recent work has shown how a parallel corpus can be leveraged to build syntac- tic parser for a target language by project- ing automatic source parse onto the target sentence using word alignments. The pro- jected target dependency parses are not al- ways fully connected to be useful for train- …...

We explore the contribution of morphologi- cal features – both lexical and inflectional – to dependency parsing of Arabic, a morpho- logically rich language. Using controlled ex- periments, we find that definiteness, person, number, gender, and the undiacritzed lemma are most helpful for parsing on automatically tagged input. We further …...

In this paper, we present a novel approach which incorporates the web-derived selec- tional preferences to improve statistical de- pendency parsing. Conventional selectional preference learning methods have usually fo- cused on word-to-class relations, e.g., a verb selects as its subject a given nominal class. This paper extends previous work to …...

It is well known that parsing accuracy suf- fers when a model is applied to out-of-domain data. It is also known that the most benefi- cial data to parse a given domain is data that matches the domain (Sekine, 1997; Gildea, 2001). Hence, an important task is to select appropriate …...

Cross-language document summarization is de- fined as the task of producing a summary in a target language (e.g. Chinese) for a set of documents in a source language (e.g. English). Existing methods for addressing this task make use of either the information from the original documents in the source language …...

We propose a method for automatically la- belling topics learned via LDA topic models. We generate our label candidate set from the top-ranking topic terms, titles of Wikipedia ar- ticles containing the top-ranking topic terms, and sub-phrases extracted from the Wikipedia article titles. We rank the label candidates us- ing …...

Topic models have been successfully applied to many document analysis tasks to discover topics embedded in text. However, existing topic models generally cannot capture the la- tent topical structures in documents. Since languages are intrinsically cohesive and coher- ent, modeling and discovering latent topical transition structures within documents would be …...

Building on earlier work that integrates dif- ferent factors in language modeling, we view (i) backing off to a shorter history and (ii) class-based generalization as two complemen- tary mechanisms of using a larger equivalence class for prediction when the default equiv- alence class is too small for reliable estima- …...

This paper explores approaches to sentiment classification of U.S. Congressional floor- debate transcripts. Collective classification techniques are used to take advantage of the informal citation structure present in the de- bates. We use a range of methods based on local and global formulations and introduce novel approaches for incorporating the …...

We present a systematic comparison and com- bination of two orthogonal techniques for efficient parsing of Combinatory Categorial Grammar (CCG). First we consider adap- tive supertagging, a widely used approximate search technique that prunes most lexical cat- egories from the parser’s search space using a separate sequence model. Next we …...

In this paper, we present a unified model for the automatic induction of word senses from text, and the subsequent disambiguation of particular word instances using the automati- cally extracted sense inventory. The induction step and the disambiguation step are based on the same principle: words and contexts are mapped …...

In this paper, we dedicate to the topic of aspect ranking, which aims to automatically identify important product aspects from online con- sumer reviews. The important aspects are identified according to two observations: (a) the important aspects of a product are usually commented by a large number of consumers; and …...

Current approaches for semantic parsing take a supervised approach requiring a consider- able amount of training data which is expen- sive and difficult to obtain. This supervision bottleneck is one of the major difficulties in scaling up semantic parsing. We argue that a semantic parser can be trained effectively without …...

This paper presents an unsupervised method for deriving inference axioms by composing semantic relations. The method is indepen- dent of any particular relation inventory. It relies on describing semantic relations using primitives and manipulating these primitives according to an algebra. The method was tested using a set of eight semantic …...

Learning by Reading (LbR) aims at enabling machines to acquire knowledge from and rea- son about textual input. This requires knowl- edge about the domain structure (such as en- tities, classes, and actions) in order to do in- ference. We present a method to infer this im- plicit knowledge from …...

We propose a non-parametric Bayesian model for unsupervised semantic parsing. Follow- ing Poon and Domingos (2009), we consider a semantic parsing setting where the goal is to (1) decompose the syntactic dependency tree of a sentence into fragments, (2) assign each of these fragments to a cluster of semanti- cally …...

We describe a new approach to disambiguat- ing semantic frames evoked by lexical predi- cates previously unseen in a lexicon or anno- tated data. Our approach makes use of large amounts of unlabeled data in a graph-based semi-supervised learning framework. We con- struct a large graph where vertices correspond to …...

Translating compounds is an important prob- lem in machine translation. Since many com- pounds have not been observed during train- ing, they pose a challenge for translation sys- tems. Previous decompounding methods have often been restricted to a small set of lan- guages as they cannot deal with more complex …...

There are several tasks where is preferable not responding than responding incorrectly. This idea is not new, but despite several previous at- tempts there isn’t a commonly accepted mea- sure to assess non-response. We study here an extension of accuracy measure with this fea- ture and a very easy to …...

The large combined search space of joint word segmentation and Part-of-Speech (POS) tag- ging makes efficient decoding very hard. As a result, effective high order features represent- ing rich contexts are inconvenient to use. In this work, we propose a novel stacked sub- word model for this task, concerning both …...