Articles

Multi-modal semantics has relied on fea- ture norms or raw image data for per- ceptual input. In this paper we examine grounding semantic representations in raw auditory data, using standard evaluations for multi-modal semantics, including mea- suring conceptual similarity and related- ness. We also evaluate cross-modal map- pings, through a …...

This paper introduces a novel way to nav- igate neighborhoods in distributional se- mantic models. The approach is based on relative neighborhood graphs, which uncover the topological structure of local neighborhoods in semantic space. This has the potential to overcome both the problem with selecting a proper k in k-NN …...

Distributed word representations capture relational similarities by means of vec- tor arithmetics, giving high accuracies on analogy detection. We empirically inves- tigate the use of syntactic dependencies on improving Chinese analogy detection based on distributed word representation- s, showing that a dependency-based em- beddings does not perform better than an …...

As a key representation model of knowl- edge, ontology has been widely used in a lot of NLP related tasks, such as se- mantic parsing, information extraction and text mining etc. In this paper, we study the task of ontology matching, which con- centrates on finding semantically related entities between …...

Efficient methods for storing and querying language models are critical for scaling to large corpora and high Markov orders. In this paper we propose methods for mod- eling extremely large corpora without im- posing a Markov condition. At its core, our approach uses a succinct index – a compressed suffix …...

We present a new unsupervised mecha- nism, which ranks word n-grams accord- ing to their multiwordness. It heavily re- lies on a new uniqueness measure that computes, based on a distributional the- saurus, how often an n-gram could be re- placed in context by a single-worded term. In addition with …...

This paper describes a novel target-side syntactic language model for phrase-based statistical machine translation, bilingual structured language model. Our approach represents a new way to adapt structured language models (Chelba and Jelinek, 2000) to statistical machine translation, and a first attempt to adapt them to phrase- based statistical machine translation. …...

Efficient computation of n-gram posterior probabilities from lattices has applications in lattice-based minimum Bayes-risk de- coding in statistical machine translation and the estimation of expected document frequencies from spoken corpora. In this paper, we present an algorithm for com- puting the posterior probabilities of all n- grams in a lattice …...

We compare the multinomial i-vector framework from the speech community with LDA, SAGE, and LSA as fea- ture learners for topic ID on multinomial speech and text data. We also compare the learned representations in their abil- ity to discover topics, quantified by dis- tributional similarity to gold-standard top- ics …...

Humor is an essential component in personal communication. How to create computational models to discover the structures behind humor, recognize humor and even extract humor anchors remains a challenge. In this work, we first identify several semantic structures behind humor and design sets of features for each structure, and next …...

In a typical social media content analysis task, the user is interested in analyzing posts of a particular topic. Identifying such posts is often formulated as a classi- fication problem. However, this problem is challenging. One key issue is covariate shift. That is, the training data is not fully representative …...

With the exponential growth of scholarly data during the past few years, effective methods for topic classification are greatly needed. Current approaches usually re- quire large amounts of expensive labeled data in order to make accurate predictions. In this paper, we posit that, in addition to a research article’s textual …...

Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been con- ducted on acted or artificially collected …...

Neural network based methods have ob- tained great progress on a variety of nat- ural language processing tasks. However, it is still a challenge task to model long texts, such as sentences and documents. In this paper, we propose a multi-timescale long short-term memory (MT-LSTM) neu- ral network to model …...

This paper proposes a tree-based con- volutional neural network (TBCNN) for discriminative sentence modeling. Our model leverages either constituency trees or dependency trees of sentences. The tree-based convolution process extracts sentences structural features, which are then aggregated by max pooling. Such ar- chitecture allows short propagation paths between the output …...

We propose a novel framework for im- proving a word segmenter using informa- tion acquired from symbol grounding. We generate a term dictionary in three steps: generating a pseudo-stochastically seg- mented corpus, building a symbol ground- ing model to enumerate word candidates, and filtering them according to the ground- ing …...

In this paper, we describe a method based on statistical machine translation (SMT) that is able to restore accents in Hungarian texts with high accuracy. Due to the ag- glutination in Hungarian, there are always plenty of word forms unknown to a sys- tem trained on a fixed vocabulary. In …...

We present a new morphological analy- sis model that considers semantic plausi- bility of word sequences by using a re- current neural network language model (RNNLM). In unsegmented languages, since language models are learned from automatically segmented texts and in- evitably contain errors, it is not apparent that conventional language …...

Arabic, Hebrew, and similar languages are typi- cally written without diacritics, leading to ambigu- ity and posing a major challenge for core language processing tasks like speech recognition. Previous approaches to automatic diacritization employed a variety of machine learning techniques. However, they typically rely on existing tools like morpho- logical …...

We describe a simple and efficient al- gorithm to disambiguate non-functional weighted finite state transducers (WFSTs), i.e. to generate a new WFST that con- tains a unique, best-scoring path for each hypothesis in the input labels along with the best output labels. The algorithm uses topological features combined with a …...