Articles

Speculation and negation are important infor- mation to identify text factuality. In this paper, we propose a Convolutional Neural Network (CNN)-based model with probabilistic weighted average pooling to address specula- tion and negation scope detection. In particular, our CNN-based model extracts those meaning- ful features from various syntactic paths be- …...

Sentence modelling is a fundamental top- ic in computational linguistics. Recently, deep learning-based sequential models of sen- tence, such as recurrent neural network, have proved to be effective in dealing with the non-sequential properties of human language. However, little is known about how a recurrent neural network captures linguistic knowledge. …...

In this work, we investigate the possibility of cross-website transfer learning for tackling the cold-start problem. To address the cold- start issues commonly present in a collabora- tive �ltering (CF) system, most existing cross- domain CF models require auxiliary rating data from another domain; nevertheless, un- der the cross-website scenario, …...

Review spam detection is a key task in opin- ion mining. To accomplish this type of de- tection, previous work has focused mainly on effectively representing fake and non-fake re- views with discriminative features, which are discovered or elaborately designed by expert- s or developers. This paper proposes a nov- …...

We present a new Convolutional Neural Net- work (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we con- sider scenarios in which annotators explic- itly mark sentences (or snippets) that sup- port their overall document categorization, i.e., they provide rationales. Our model …...

This paper studies summarizing key informa- tion from news streams. We propose sim- ple yet effective models to solve the problem based on a novel and promising representation of text streams – Burst Information Networks (BINets). A BINet can be aware of redundant information, allows global analysis of a text …...

This paper introduces STEM and LAMB, em- beddings trained for stems and lemmata in- stead of for surface forms. For morpholog- ically rich languages, they perform signif- icantly better than standard embeddings on word similarity and polarity evaluations. On a new WordNet-based evaluation, STEM and LAMB are up to 50% …...

Multiple treebanks annotated under heteroge- neous standards give rise to the research ques- tion of best utilizing multiple resources for im- proving statistical models. Prior research has focused on discrete models, leveraging stack- ing and multi-view learning to address the problem. In this paper, we empirically inves- tigate heterogeneous annotations …...

Unsupervised dependency parsing aims to learn a dependency grammar from text anno- tated with only POS tags. Various features and inductive biases are often used to incorpo- rate prior knowledge into learning. One use- ful type of prior information is that there exist correlations between the parameters of gram- mar …...

Exact inference in high-order graph-based non-projective dependency parsing is in- tractable. Hence, sophisticated approximation techniques based on algorithms such as be- lief propagation and dual decomposition have been employed. In contrast, we propose a sim- ple greedy search approximation for this prob- lem which is very intuitive and easy to …...

Search personalization that considers the social dimension of the web has attracted a significant volume of research in recent years. A user pro- file is usually needed to represent a user’s inter- ests in order to tailor future searches. Previous research has typically constructed a profile sole- ly from a …...

Previous work on automatic summarization does not thoroughly consider coherence while generating the summary. We introduce a graph-based approach to summarize scientific articles. We employ coherence patterns to en- sure that the generated summaries are coher- ent. The novelty of our model is twofold: we mine coherence patterns in a …...

The recently proposed coupled sequence label- ing is shown to be able to effectively exploit multiple labeled data with heterogeneous an- notations but suffer from severe inefficiency problem due to the large bundled tag space (Li et al., 2015). In their case study of part-of- speech (POS) tagging, Li et …...

In this paper, we utilize distributed word rep- resentations (i.e., word embeddings) to anal- yse the representation of semantics in brain activity. The brain activity data were recorded using functional magnetic resonance imaging (fMRI) when subjects were viewing words. First, we analysed the functional selectivity of different cortex areas by …...

Chinese named entities occur frequently in formal and informal environments. Various approaches have been formalized the problem as a sequence labelling task and utilize a character-based methodology, in which char- acter is treated as the basic classification unit. One of the main drawbacks of these methods is that some of …...

We explore linguistic and behavioral features of dogmatism in social media and construct statistical models that can identify dogmatic comments. Our model is based on a corpus of Reddit posts, collected across a diverse set of conversational topics and annotated via paid crowdsourcing. We operationalize key aspects of dogmatism described …...

To alleviate the error propagation in the tra- ditional pipelined models for Abstract Mean- ing Representation (AMR) parsing, we for- mulate AMR parsing as a joint task that per- forms the two subtasks: concept identification and relation identification simultaneously. To this end, we first develop a novel component- wise beam …...

Evaluation of NLP methods requires test- ing against a previously vetted gold-standard test set and reporting standard metrics (ac- curacy/precision/recall/F1). The current as- sumption is that all items in a given test set are equal with regards to difficulty and discrim- inating power. We propose Item Response Theory (IRT) from …...

Embedding words in a vector space has gained a lot of attention in recent years. While state- of-the-art methods provide efficient computa- tion of word similarities via a low-dimensional matrix embedding, their motivation is often left unclear. In this paper, we argue that word embedding can be naturally viewed as …...

In aspect-based sentiment analysis, extract- ing aspect terms along with the opinions be- ing expressed from user-generated content is one of the most important subtasks. Previ- ous studies have shown that exploiting con- nections between aspect and opinion terms is promising for this task. In this paper, we pro- pose …...