Articles

AbstractThis paper proposes OCR++, an open-source framework designed for a variety of information extraction tasks from scholarly articles including metadata (title, author names, affiliation and e-mail), structure (section headings and body text, table and figure headings, URLs and footnotes) and bibliography (citation instances and references). We analyze a diverse set …...

AbstractNamed-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by …...

AbstractAccurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a …...

AbstractSituated dialogue systems that interact with humans as part of a team (e.g., robot teammates) need to be able to use information from communication channels to gauge the coordination level and effectiveness of the team. Currently, the feasibility of this end goal is limited by several gaps in both the …...

We introduce a globally normalized transition-based neural network model that achieves state-of-the-art part-of- speech tagging, dependency parsing and sentence compression results. Our model is a simple feed-forward neural network that operates on a task-specific transition system, yet achieves comparable or better accuracies than recurrent models. We dis- cuss the importance …...

Linguistic drift is a process that produces slow irreversible changes in the grammar and function of a language’s construc- tions. Importantly, changes in a part of a language can have trickle down effects, triggering changes elsewhere in that lan- guage. Although such causally triggered chains of changes have long been …...

The ability to compute an accurate re- ward function is essential for optimising a dialogue policy via reinforcement learn- ing. In real-world applications, using ex- plicit user feedback as the reward sig- nal is often unreliable and costly to col- lect. This problem can be mitigated if the user’s intent …...

Combining deep neural networks with structured logic rules is desirable to harness flexibility and reduce uninterpretability of the neural models. We propose a general framework capable of enhancing various types of neural networks (e.g., CNNs and RNNs) with declarative first-order logic rules. Specifically, we develop an iterative distillation method that …...

Detecting hypernymy relations is a key task in NLP, which is addressed in the literature using two complemen- tary approaches. Distributional methods, whose supervised variants are the cur- rent best performers, and path-based meth- ods, which received less research atten- tion. We suggest an improved path-based algorithm, in which the …...

Arbitrariness of the sign—the notion that the forms of words are unrelated to their meanings—is an underlying assumption of many linguistic theories. Two lines of research have recently challenged this as- sumption, but they produce differing char- acterizations of non-arbitrariness in lan- guage. Behavioral and corpus studies have confirmed the …...

We introduce a new language learning setting relevant to building adaptive nat- ural language interfaces. It is inspired by Wittgenstein’s language games: a hu- man wishes to accomplish some task (e.g., achieving a certain configuration of blocks), but can only communicate with a computer, who performs the actual actions (e.g., …...

We present an approach to improve sta- tistical machine translation of image de- scriptions by multimodal pivots defined in visual space. The key idea is to perform image retrieval over a database of images that are captioned in the target language, and use the captions of the most similar images …...

Part of the unique cultural heritage of Chi- na is the Chinese couplet. Given a sen- tence (namely an antecedent clause), peo- ple reply with another sentence (namely a subsequent clause) equal in length. More- over, a special phenomenon is that corre- sponding characters from the same posi- tion in …...

Enabling a computer to understand a docu- ment so that it can answer comprehension questions is a central, yet unsolved goal of NLP. A key factor impeding its solu- tion by machine learned systems is the lim- ited availability of human-annotated data. Hermann et al. (2015) seek to solve this …...

Semantic deficit is a symptom of language impairment in Alzheimer’s disease (AD). We present a generalizable method for au- tomatic generation of information content units (ICUs) for a picture used in a stan- dard clinical task, achieving high recall, 96.8%, of human-supplied ICUs. We use the automatically generated topic model …...

Existing knowledge-based question an- swering systems often rely on small an- notated training data. While shallow meth- ods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at an- swering questions involving multiple con- straints. Here …...

Recently, knowledge graph embedding, which projects symbolic entities and rela- tions into continuous vector space, has be- come a new, hot topic in artificial intelli- gence. This paper proposes a novel gen- erative model (TransG) to address the is- sue of multiple relation semantics that a relation may have multiple …...

Labeling topics learned by topic models is a challenging problem. Previous studies have used words, phrases and images to label topics. In this paper, we propose to use text summaries for topic labeling. Several sentences are extracted from the most related documents to form the sum- mary for each topic. …...

In this paper, we propose a neural network model for graph-based dependency pars- ing which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual in- formation instead of using high-order fac- torization, and enable our model to use much fewer features than previous work. In addition, we propose an effective way …...

Anaphor resolution is an important task in NLP with many applications. De- spite much research effort, it remains an open problem. The difficulty of the prob- lem varies substantially across different sub-problems. One sub-problem, in par- ticular, has been largely untouched by prior work despite occurring frequently throughout corpora: the …...