Articles

We present a novel dependency pars- ing method which enforces two structural properties on dependency trees: bounded block degree and well-nestedness. These properties are useful to better represent the set of admissible dependency structures in treebanks and connect dependency pars- ing to context-sensitive grammatical for- malisms. We cast this problem …...

Domain adaptation is an important re- search topic in sentiment analysis area. Existing domain adaptation methods usu- ally transfer sentiment knowledge from only one source domain to target do- main. In this paper, we propose a new domain adaptation approach which can exploit sentiment knowledge from mul- tiple source domains. …...

Natural language understanding often re- quires deep semantic knowledge. Ex- panding on previous proposals, we suggest that some important aspects of semantic knowledge can be modeled as a language model if done at an appropriate level of ab- straction. We develop two distinct mod- els that capture semantic frame chains …...

Event extraction from texts aims to de- tect structured information such as what has happened, to whom, where and when. Event extraction and visualization are typ- ically considered as two different tasks. In this paper, we propose a novel approach based on probabilistic modelling to joint- ly extract and visualize …...

We propose a brand new “Liberal” Event Extraction paradigm to extract events and discover event schemas from any input corpus simultaneously. We incorporate symbolic (e.g., Abstract Meaning Repre- sentation) and distributional semantics to detect and represent event structures and adopt a joint typing framework to simulta- neously extract event types …...

A shared bilingual word embedding space (SBWES) is an indispensable resource in a variety of cross-language NLP and IR tasks. A common approach to the SB- WES induction is to learn a mapping func- tion between monolingual semantic spaces, where the mapping critically relies on a seed word lexicon used …...

We introduce a new methodology for in- trinsic evaluation of word representations. Specifically, we identify four fundamen- tal criteria based on the characteristics of natural language that pose difficulties to NLP systems; and develop tests that di- rectly show whether or not representations contain the subspaces necessary to satisfy these …...

Neural networks are among the state-of- the-art techniques for language modeling. Existing neural language models typically map discrete words to distributed, dense vector representations. After information processing of the preceding context words by hidden layers, an output layer estimates the probability of the next word. Such ap- proaches are time- …...

Metaphor is a common linguistic tool in communication, making its detection in discourse a crucial task for natural lan- guage understanding. One popular ap- proach to this challenge is to capture se- mantic incohesion between a metaphor and the dominant topic of the surrounding text. While these methods are effective, …...

There is a small but growing body of research on statistical scripts, models of event sequences that allow probabilistic inference of implicit events from docu- ments. These systems operate on struc- tured verb-argument events produced by an NLP pipeline. We compare these sys- tems with recent Recurrent Neural Net models …...

We present a novel method for jointly learning compositional and non- compositional phrase embeddings by adaptively weighting both types of em- beddings using a compositionality scoring function. The scoring function is used to quantify the level of compositionality of each phrase, and the parameters of the function are jointly optimized …...

Idiom token classification is the task of deciding for a set of potentially idiomatic phrases whether each occurrence of a phrase is a literal or idiomatic usage of the phrase. In this work we explore the use of Skip-Thought Vectors to create dis- tributed representations that encode fea- tures that …...

Metaphorical expressions are pervasive in natural language and pose a substan- tial challenge for computational seman- tics. The inherent compositionality of metaphor makes it an important test case for compositional distributional semantic models (CDSMs). This paper is the first to investigate whether metaphorical compo- sition warrants a distinct treatment in …...

Dynamic oracle training has shown sub- stantial improvements for dependency parsing in various settings, but has not been explored for constituent parsing. The present article introduces a dynamic ora- cle for transition-based constituent pars- ing. Experiments on the 9 languages of the SPMRL dataset show that a neu- ral greedy …...

We present a transition-based system that jointly predicts the syntactic structure and lexical units of a sentence by building two structures over the input words: syntactic dependency tree and a forest ofa lexical units including multiword expres- sions (MWEs). This combined represen- tation allows us to capture both the syn- …...

In this paper, we present a generalized transition-based parsing framework where parsers are instantiated in terms of a set of control parameters that constrain tran- sitions between parser states. This gener- alization provides a unified framework to describe and compare various transition- based parsing approaches from both a the- oretical …...

The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP sys- tems, including traditional count-based and deep learning models. We propose a novel way to deal with the rare and unseen words for the neural network models us- ing attention. …...

We use Bayesian optimization to learn curricula for word representation learning, optimizing performance on downstream tasks that depend on the learned represen- tations as features. The curricula are mod- eled by a linear ranking function which is the scalar product of a learned weight vec- tor and an engineered feature …...

We propose a framework for lexical sub- stitution that is able to perform transfer learning across languages. Datasets for this task are available in at least three languages (English, Italian, and German). Previous work has addressed each of these tasks in isolation. In contrast, we regard the union of three …...

As a new generation of cognitive robots start to enter our lives, it is important to enable robots to follow human commands and to learn new actions from human lan- guage instructions. To address this issue, this paper presents an approach that ex- plicitly represents verb semantics through hypothesis spaces …...