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Content analysis, a widely-applied social sci- ence research method, is increasingly being supplemented by topic modeling. However, while the discourse on content analysis cen- ters heavily on reproducibility, computer sci- entists often focus more on scalability and less on coding reliability, leading to growing skep- ticism on the usefulness of …...

In this paper, we describe the TATO sys- tem which participated in the SemEval-2015 Task 2a: “Semantic Textual Similarity (STS) for English”. Our system is trained on pub- lished datasets from the previous competi- tions. Based on some machine learning tech- niques, it combines multiple similarity mea- sures of varying …...

ThisThis work is licensed under a Creative Commons Attribution 4.0 International Li- cence. Page numbers and proceedings footer are added by the organisers. Licence details: http://creativecommons.org/licenses/by/4.0/ paper describes the second version of the ASAP system1 and its participation in the SemEval-2015, task 2a on Semantic Textual Similarity (STS). Our approach …...

As part of the SemEval-2015 shared task on Broad-Coverage Semantic Dependency Pars- ing, we evaluate the performace of our last year’s system (TurboSemanticParser) on mul- tiple languages and out-of-domain data. Our system is characterized by a feature-rich lin- ear model, that includes scores for first and second-order dependencies (arcs, siblings, …...

English of Academic Writing (EAW) is essential to the research community for sharing knowledge. Research documents using EAW, especially the abstract and...

In this paper, we introduce the concept of a novel application, called Knowledge Learning from User Search, aiming at identifying timely new knowledge triples from user search log. In the literature, the need of knowledge enrichment has been recognized as the key to the success of knowledge-based search. However, previous …...

Word Complexity Measure (WCM, Stoel-Gammon, 2010) is a system of phonological assessment for children’s speech productions, a method that focuses on the complexity rather than accuracy. With its flexible parameter program, the assessment can be adjusted to the phonological properties of different languages. In the current study, the WCM was …...

In order to effectively utilize multiple datasets with heterogeneous annotations, this paper proposes a coupled sequence labeling model that can directly learn and infer two heterogeneous annotations simultaneously, and to facilitate discussion we use Chinese part-of- speech (POS) tagging as our case study. The key idea is to bundle two …...

We present AutoExtend, a system to learn embeddings for synsets and lexemes. It is flexible in that it can take any word embed- dings as input and does not need an addi- tional training corpus. The synset/lexeme embeddings obtained live in the same vec- tor space as the word embeddings. …...

Tracking topics on social media streams is non-trivial as the number of topics men- tioned grows without bound. This com- plexity is compounded when we want to track such topics against other fast mov- ing streams. We go beyond traditional small scale topic tracking and consider a stream of topics …...

Social media content can be used as a complementary source to the traditional methods for extracting and studying col- lective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their …...

This paper proposes an embedding match- ing approach to Chinese word segmenta- tion, which generalizes the traditional se- quence labeling framework and takes ad- vantage of distributed representations. The training and prediction algorithms have linear-time complexity. Based on the pro- posed model, a greedy segmenter is de- veloped and evaluated …...

Recently, neural network models for natu- ral language processing tasks have been in- creasingly focused on for their ability of al- leviating the burden of manual feature en- gineering. However, the previous neural models cannot extract the complicated fea- ture compositions as the traditional meth- ods with discrete features. In …...

In this paper, we investigate the problem of learning a machine translation model that can simultaneously translate sentences from one source language to multiple target languages. Our solution is inspired by the recently proposed neural machine translation model which generalizes machine translation as a sequence learning problem. We extend the …...

We present a novel, count-based approach to obtaining inter-lingual word represen- tations based on inverted indexing of Wikipedia. We present experiments ap- plying these representations to 17 datasets in document classification, POS tagging, dependency parsing, and word alignment. Our approach has the advantage that it is simple, computationally efficient and …...

In this paper, we consider the task of text categorization as a graph classifica- tion problem. By representing textual doc- uments as graph-of-words instead of his- torical n-gram bag-of-words, we extract more discriminative features that corre- spond to long-distance n-grams through frequent subgraph mining. Moreover, by capitalizing on the concept …...

Many existing deep learning models for natural language processing tasks focus on learning the compositionality of their in- puts, which requires many expensive com- putations. We present a simple deep neural network that competes with and, in some cases, outperforms such models on sen- timent analysis and factoid question an- …...

Traditional learning to rank methods learn ranking models from training data in a batch and offline learning mode, which suffers from some critical limitations, e.g., poor scalability as the model has to be re- trained from scratch whenever new train- ing data arrives. This is clearly non- scalable for many …...

In this paper, we model conversational roles in terms of distributions of turn level behaviors, including conversation acts and stylistic markers, as they occur over the whole interaction. This work presents a lightly supervised approach to inducing role definitions over sets of contributions within an extended interaction, where the supervision …...

The development and proliferation of so- cial media services has led to the emer- gence of new approaches for surveying the population and addressing social issues. One popular application of social media data is health surveillance, e.g., predicting the outbreak of an epidemic by recogniz- ing diseases and symptoms from …...