Articles

As documents tend to contain temporal information, extracting such information is attracting much research interests re- cently. In this paper, we propose a hybrid method that combines machine-learning models and hand-crafted rules for the task of extracting temporal information from unstructured Korean texts. We address Korean-specific research issues and pro- …...

We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific fea- tures on a large unlabeled corpus. This al- lows us to explicitly incorporate relation- specific information into the word embed- dings. The …...

We present a novel word level vector rep- resentation based on symmetric patterns (SPs). For this aim we automatically ac- quire SPs (e.g., “X and Y”) from a large corpus of plain text, and generate vectors where each coordinate represents the co- occurrence in SPs of the represented word with …...

Software system development is guided by the evolution of requirements. In this pa- per, we address the task of requirements traceability, which is concerned with pro- viding bi-directional traceability between various requirements, enabling users to find the origin of each requirement and track every change made to it. We pro- …...

Neural language models (NLMs) have been able to improve machine translation (MT) thanks to their ability to generalize well to long contexts. Despite recent suc- cesses of deep neural networks in speech and vision, the general practice in MT is to incorporate NLMs with only one or two hidden layers …...

Typically, visually-grounded language learning systems only accept feature data about objects in the environment that are explicitly mentioned, whether through annotation labels or direct ref- erence through natural language. We show that when objects are described ambiguously using natural language, a system can use a combination of the pragmatic principles …...

We present an approach for opinion role induction for verbal predicates. Our model rests on the assumption that opinion verbs can be divided into three different types where each type is associated with a char- acteristic mapping between semantic roles and opinion holders and targets. In sev- eral experiments, we …...

We propose MVCNN, a convolution neu- ral network (CNN) architecture for sen- tence classification. It (i) combines di- verse versions of pretrained word embed- dings and (ii) extracts features of multi- granular phrases with variable-size convo- lution filters. We also show that pretrain- ing MVCNN is critical for good perfor- …...

This paper describes a set of methods to link entities across images and text. As a corpus, we used a data set of images, where each image is commented by a short caption and where the regions in the im- ages are manually segmented and labeled with a category. We …...

One of the most common features across all known languages is their variability in word order. We show that differences in the prenominal and postnominal place- ment of adjectives in the noun phrase across five main Romance languages is not only subject to heaviness effects, as previously reported, but also …...

Massive knowledge resources, such as Wikidata, can provide valuable informa- tion for lexical inference, especially for proper-names. Prior resource-based ap- proaches typically select the subset of each resource’s relations which are relevant for a particular given task. The selection process is done manually, limiting these approaches to smaller resources such …...

We present labeled morphological segmentation—an alternative view of morphological processing that unifies sev- eral tasks. We introduce a new hierarchy of morphotactic tagsets and CHIPMUNK, a discriminative morphological segmen- tation system that, contrary to previous work, explicitly models morphotactics. We show improved performance on three tasks for all six languages: …...

We propose a discriminatively trained re- current neural network (RNN) that pre- dicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model struc- ture to compute hidden vectors that en- code the preceding partial parse, and uses them to estimate probabilities of parser …...

Scarcity of annotated corpora for many languages is a bottleneck for training fine- grained sentiment analysis models that can tag aspects and subjective phrases. We pro- pose to exploit statistical machine transla- tion to alleviate the need for training data by projecting annotated data in a source language to a …...

Although entity linking is a widely re- searched topic, the same cannot be said for entity linking geared for languages other than English. Several limitations includ- ing syntactic features and the relative lack of resources prevent typical approaches to entity linking to be used as effectively for other languages in …...

Two questions asking the same thing could be too different in terms of vocabulary and syntactic structure, which makes identify- ing their semantic equivalence challeng- ing. This study aims to detect semanti- cally equivalent questions in online user forums. We perform an extensive number of experiments using data from two …...

Cross-lingual transfer has been shown to produce good results for dependency pars- ing of resource-poor languages. Although this avoids the need for a target language treebank, most approaches have still used large parallel corpora. However, parallel data is scarce for low-resource languages, and we report a new method that does …...

Most computational sociolinguistics stud- ies have focused on phonological and lexical variation. We present the first large-scale study of syntactic variation among demographic groups (age and gen- der) across several languages. We har- vest data from online user-review sites and parse it with universal dependencies. We show that several age …...

Word embeddings — distributed word representations that can be learned from unlabelled data — have been shown to have high utility in many natural language processing applications. In this paper, we perform an extrinsic evaluation of four popular word embedding methods in the context of four sequence labelling tasks: part-of-speech …...

We study the impact of source length and verbosity of the tuning dataset on the per- formance of parameter optimizers such as MERT and PRO for statistical machine translation. In particular, we test whether the verbosity of the resulting translations can be modified by varying the length or the verbosity …...