Articles

Due to their origin in computer graph- ics, graphics processing units (GPUs) are highly optimized for dense problems, where the exact same operation is applied repeatedly to all data points. Natural lan- guage processing algorithms, on the other hand, are traditionally constructed in ways that exploit structural sparsity. Recently, Canny …...

Much of the recent work on depen- dency parsing has been focused on solv- ing inherent combinatorial problems as- sociated with rich scoring functions. In contrast, we demonstrate that highly ex- pressive scoring functions can be used with substantially simpler inference pro- cedures. Specifically, we introduce a sampling-based parser that …...

Existing models for social media per- sonal analytics assume access to thou- sands of messages per user, even though most users author content only sporadi- cally over time. Given this sparsity, we: (i) leverage content from the local neigh- borhood of a user; (ii) evaluate batch mod- els as a …...

Consider a person trying to spread an important message on a social network. He/she can spend hours trying to craft the message. Does it actually matter? While there has been extensive prior work look- ing into predicting popularity of social- media content, the effect of wording per se has rarely …...

While user attribute extraction on social media has received considerable attention, existing approaches, mostly supervised, encounter great difficulty in obtaining gold standard data and are therefore limited to predicting unary predicates (e.g., gen- der). In this paper, we present a weakly- supervised approach to user profile extrac- tion from Twitter. …...

Discussion forums have evolved into a de- pendable source of knowledge to solve common problems. However, only a mi- nority of the posts in discussion forums are solution posts. Identifying solution posts from discussion forums, hence, is an important research problem. In this pa- per, we present a technique for …...

Statistical Machine Translation (SMT) usually utilizes contextual information to disambiguate translation candidates. However, it is often limited to contexts within sentence boundaries, hence broader topical information cannot be leveraged. In this paper, we propose a novel approach to learning topic representation for paral- lel data using a neural network architec- …...

We propose Bilingually-constrained Re- cursive Auto-encoders (BRAE) to learn semantic phrase embeddings (compact vector representations for phrases), which can distinguish the phrases with differ- ent semantic meanings. The BRAE is trained in a way that minimizes the seman- tic distance of translation equivalents and maximizes the semantic distance of non- …...

Dependency-based Compositional Se- mantics (DCS) is a framework of natural language semantics with easy-to-process structures as well as strict semantics. In this paper, we equip the DCS framework with logical inference, by defining ab- stract denotations as an abstraction of the computing process of denotations in original DCS. An inference …...

In this work, we revisit Shared Task 1 from the 2012 *SEM Conference: the au- tomated analysis of negation. Unlike the vast majority of participating systems in 2012, our approach works over explicit and formal representations of proposi- tional semantics, i.e. derives the notion of negation scope assumed in this …...

In this paper, we address the problem of web-domain POS tagging using a two- phase approach. The first phase learns rep- resentations that capture regularities un- derlying web text. The representation is integrated as features into a neural network that serves as a scorer for an easy-first POS tagger. Parameters …...

We present a novel technique for learn- ing semantic representations, which ex- tends the distributional hypothesis to mul- tilingual data and joint-space embeddings. Our models leverage parallel data and learn to strongly align the embeddings of semantically equivalent sentences, while maintaining sufficient distance between those of dissimilar sentences. The mod- …...

We investigate different ways of learning structured perceptron models for coref- erence resolution when using non-local features and beam search. Our experi- mental results indicate that standard tech- niques such as early updates or Learning as Search Optimization (LaSO) perform worse than a greedy baseline that only uses local features. …...

A key challenge for computational conver- sation models is to discover latent struc- ture in task-oriented dialogue, since it pro- vides a basis for analysing, evaluating, and building conversational systems. We pro- pose three new unsupervised models to discover latent structures in task-oriented dialogues. Our methods synthesize hidden Markov models …...

Previous researches on Text-level discourse parsing mainly made use of constituency structure to parse the whole document into one discourse tree. In this paper, we present the limitations of constituency based dis- course parsing and first propose to use de- pendency structure to directly represent the relations between elementary discourse …...

Morphological segmentation is an effec- tive sparsity reduction strategy for statis- tical machine translation (SMT) involv- ing morphologically complex languages. When translating into a segmented lan- guage, an extra step is required to deseg- ment the output; previous studies have de- segmented the 1-best output from the de- coder. In …...

Text-level discourse parsing is notoriously difficult, as distinctions between discourse relations require subtle semantic judg- ments that are not easily captured using standard features. In this paper, we present a representation learning approach, in which we transform surface features into a latent space that facilitates RST dis- course parsing. By …...

We present a series of algorithms with the- oretical guarantees for learning accurate ensembles of several structured prediction rules for which no prior knowledge is as- sumed. This includes a number of ran- domized and deterministic algorithms de- vised by converting on-line learning al- gorithms to batch ones, and a …...

In this paper, instead of designing new fea- tures based on intuition, linguistic knowl- edge and domain, we learn some new and effective features using the deep auto- encoder (DAE) paradigm for phrase-based translation model. Using the unsupervised pre-trained deep belief net (DBN) to ini- tialize DAE’s parameters and using …...

Most traditional distributional similarity models fail to capture syntagmatic patterns that group together multiple word features within the same joint context. In this work we introduce a novel generic distributional similarity scheme under which the power of probabilistic models can be leveraged to effectively model joint contexts. Based on this …...