Articles

We introduce several probabilistic models for learning the lexicon of a semantic parser. Lex- icon learning is the first step of training a se- mantic parser for a new application domain and the quality of the learned lexicon signifi- cantly affects both the accuracy and efficiency of the final semantic …...

Search tasks, comprising a series of search queries serving a common informational need, have steadily emerged as accurate units for developing the next generation of task-aware web search systems. Most prior research in this area has focused on segmenting chrono- logically ordered search queries into higher level tasks. A more …...

Cross-lingual Wikification is the task of grounding mentions written in non-English documents to entries in the English Wikipedia. This task involves the problem of comparing textual clues across languages, which requires developing a notion of similarity between text snippets across languages. In this paper, we address this problem by jointly …...

Property norms have the potential to aid a wide range of semantic tasks, provided that they can be obtained for large numbers of concepts. Recent work has focused on text as the main source of information for auto- matic property extraction. In this paper we ex- amine property norm prediction …...

Group discussions are essential for organiz- ing every aspect of modern life, from faculty meetings to senate debates, from grant review panels to papal conclaves. While costly in terms of time and organization effort, group discussions are commonly seen as a way of reaching better decisions compared to solu- tions …...

We address the task of annotating images with semantic tuples. Solving this problem requires an algorithm able to deal with hundreds of classes for each argument of the tuple. In such contexts, data sparsity becomes a key challenge. We propose handling this spar- sity by incorporating feature representations of both …...

Multitask learning has been proven a useful technique in a number of Natural Language Processing applications where data is scarce and naturally diverse. Examples include learn- ing from data of different domains and learn- ing from labels provided by multiple annota- tors. Tasks in these scenarios would be the domains …...

An increasing amount of research has recently focused on representing affective states as continuous numerical values on multiple di- mensions, such as the valence-arousal (VA) space. Compared to the categorical approach that represents affective states as several clas- ses (e.g., positive and negative), the dimen- sional approach can provide more …...

Automatically learning script knowledge has proved difficult, with previous work not or just barely beating a most-frequent baseline. Script knowledge is a type of world knowl- edge which can however be useful for various task in NLP and psycholinguistic modelling. We here propose a model that includes partici- pant information …...

This paper investigates two different neural architectures for the task of relation classi- fication: convolutional neural networks and recurrent neural networks. For both mod- els, we demonstrate the effect of different ar- chitectural choices. We present a new con- text representation for convolutional neural networks for relation classification (extended middle …...

Bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) has been successfully applied in many tagging tasks. BLSTM-RNN relies on the distributed representation of words, which implies that the former can be futhermore improved through learning the latter better. In this work, we propose a novel approach to learn distributed …...

Recent approaches based on artificial neural networks (ANNs) have shown promising re- sults for short-text classification. However, many short texts occur in sequences (e.g., sen- tences in a document or utterances in a dia- log), and most existing ANN-based systems do not leverage the preceding short texts when classifying a …...

Existing research on multiclass text classifica- tion mostly makes the closed world assump- tion, which focuses on designing accurate classifiers under the assumption that all test classes are known at training time. A more re- alistic scenario is to expect unseen classes during testing (open world). In this case, the …...

This paper presents a novel approach using recurrent neural networks for estimating the quality of machine translation output. A se- quence of vectors made by the prediction method is used as the input of the final recur- rent neural network. The prediction method uses bi-directional recurrent neural network architecture both …...

When evaluating the quality of topics gener- ated by a topic model, the convention is to score topic coherence — either manually or automatically — using the top-N topic words. This hyper-parameter N, or the cardinality of the topic, is often overlooked and selected arbitrarily. In this paper, we investigate …...

Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understand- ing of EHRs. It has important applications in health informatics including pharmacovig- ilance and drug surveillance. The state of the art supervised machine learning …...

In a multi-label text classification task, in which multiple labels can be assigned to one text, label co-occurrence itself is informative. We propose a novel neural network initializa- tion method to treat some of the neurons in the final hidden layer as dedicated neurons for each pattern of label co-occurrence. …...

Historical documents frequently exhibit ex- tensive orthographic variation, including ar- chaic spellings and obsolete shorthand. OCR tools typically seek to produce so-called diplo- matic transcriptions that preserve these vari- ants, but many end tasks require transcriptions with normalized orthography. In this paper, we present a novel joint transcription model that …...

Knowledge bases of real-world facts about entities and their relationships are useful re- sources for a variety of natural language pro- cessing tasks. However, because knowledge bases are typically incomplete, it is useful to be able to perform link prediction, i.e., pre- dict whether a relationship not in the knowl- …...

State-of-the-art word embeddings, which are often trained on bag-of-words (BOW) con- texts, provide a high quality representation of aspects of the semantics of nouns. However, their quality decreases substantially for the task of verb similarity prediction. In this paper we show that using symmetric pattern contexts (SPs, e.g., “X and …...