Articles

For topic models, such as LDA, that use a bag-of-words assumption, it becomes es- pecially important to break the corpus into appropriately-sized “documents”. Since the models are estimated solely from the term cooccurrences, extensive documents such as books or long journal articles lead to diffuse statistics, and short documents such …...

We present a neural network based shift- reduce CCG parser, the first neural-network based parser for CCG. We also study the im- pact of neural network based tagging mod- els, and greedy versus beam-search parsing, by using a structured neural network model. Our greedy parser obtains a labeled F-score of …...

This paper proposes a variability normaliza- tion algorithm to reduce the variability be- tween intra-topic documents for topic classi- fication. Firstly, an optimization problem is constructed based on linear variability remov- able assumption. Secondly, a new feature s- pace for document representation is found by solving the optimization problem with …...

It has been shown that transition-based meth- ods can be used for syntactic word order- ing and tree linearization, achieving signifi- cantly faster speed compared with traditional best-first methods. State-of-the-art transition- based models give competitive results on ab- stract word ordering and unlabeled tree lin- earization, but significantly worse results …...

Automatic event schema induction (AESI) means to extract meta-event from raw text, in other words, to find out what types (tem- plates) of event may exist in the raw text and what roles (slots) may exist in each event type. In this paper, we propose a joint entity-driven model to …...

This study aims to measure the variation be- tween writers in their choices of referential form by collecting and analysing a new and publicly available corpus of referring expres- sions. The corpus is composed of referring expressions produced by different participants in identical situations. Results, measured in terms of normalized …...

Neural machine translation (NMT) with re- current neural networks, has proven to be an effective technique for end-to-end machine translation. However, in spite of its promis- ing advances over traditional translation meth- ods, it typically suffers from an issue of unbal- anced outputs, that arise from both the nature of …...

We tackle the task of extracting tweets that mention a specific event from all tweets that contain relevant keywords, for which the main challenges include unbalanced positive and negative cases, and the unavailability of man- ually labeled training data. Existing meth- ods leverage a few manually given seed events and …...

We propose recurrent support vector machine (RSVM) for slot tagging. This model is a combi- nation of the recurrent neural network (RNN) and the structured support vector machine. RNN extracts features from the input sequence. The structured support vector machine uses a sequence-level dis- criminative objective function. The proposed model …...

We introduce a bootstrapping algorithm for re- gression that exploits word embedding mod- els. We use it to infer four psycholinguis- tic properties of words: Familiarity, Age of Acquisition, Concreteness and Imagery and further populate the MRC Psycholinguistic Database with these properties. The ap- proach achieves 0.88 correlation with human- …...

We tackle the problem of identifying decep- tive agents in highly-motivated high-conflict dialogues. We consider the case where we only have textual information. We show the usefulness of psycho-linguistic deception and persuasion features on a small dataset for the game of Werewolf. We analyse the role of syntax and we …...

Children learn the meaning of words by being exposed to perceptually rich situations (lin- guistic discourse, visual scenes, etc). Current computational learning models typically sim- ulate these rich situations through impover- ished symbolic approximations. In this work, we present a distributed word learning model that operates on child-directed speech paired …...

AbstractStructured, complete inflectional paradigm data exists for very few of the world’s languages, but is crucial to training morphological analysis tools. We present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language. Informants are tasked with completing language-specific …...

AbstractBilingual lexicon extraction from comparable corpora is usually based on distributional methods when dealing with single word terms (SWT). These methods often treat SWT as single tokens without considering their compositional property. However, many SWT are compositional (composed of roots and affixes) and this information, if taken into account can …...

AbstractUnsupervised learning of morphological segmentation of words in a language, based only on a large corpus of words, is a challenging task. Evaluation of the learned segmentations is a challenge in itself, due to the inherent ambiguity of the segmentation task. There is no way to posit unique “correct” segmentation …...

AbstractIn this paper we report a comparison of various techniques for single-document extractive summarization under strict length budgets, which is a common commercial use case (e.g. summarization of news articles by news aggregators). We show that, evaluated using ROUGE, numerous algorithms from the literature fail to beat a simple lead-based …...

AbstractAutomatic summarization of reader comments in on-line news is an extremely challenging task and a capability for which there is a clear need. Work to date has focussed on producing extractive summaries using well-known techniques imported from other areas of language processing. But are extractive summaries of comments what users …...

AbstractScientific literature records the research process with a standardized structure and provides the clues to track the progress in a scientific field. Understanding its internal structure and content is of paramount importance for natural language processing (NLP) technologies. To meet this requirement, we have developed a multi-layered annotated corpus of …...

AbstractLexical Simplification is the task of replacing complex words in a text with simpler alternatives. A variety of strategies have been devised for this challenge, yet there has been little effort in comparing their performance. In this contribution, we present a benchmarking of several Lexical Simplification systems. By combining resources …...

AbstractIn American Sign Language (ASL) as well as other signed languages, different classes of signs (e.g., lexical signs, fingerspelled signs, and classifier constructions) have different internal structural properties. Continuous sign recognition accuracy can be improved through use of distinct recognition strategies, as well as different training datasets, for each class …...