Articles

Interpersonal relations are fickle, with close friendships often dissolving into en- mity. In this work, we explore linguis- tic cues that presage such transitions by studying dyadic interactions in an on- line strategy game where players form al- liances and break those alliances through betrayal. We characterize friendships that are …...

We propose a nonparametric Bayesian model for joint unsupervised word seg- mentation and part-of-speech tagging from raw strings. Extending a previous model for word segmentation, our model is called a Pitman-Yor Hidden Semi- Markov Model (PYHSMM) and consid- ered as a method to build a class n-gram language model directly …...

We propose a language production model that uses dynamic discourse information to account for speakers’ choices of refer- ring expressions. Our model extends pre- vious rational speech act models (Frank and Goodman, 2012) to more naturally dis- tributed linguistic data, instead of assuming a controlled experimental setting. Simula- tions show …...

Framing is a sophisticated form of dis- course in which the speaker tries to in- duce a cognitive bias through consis- tent linkage between a topic and a spe- cific context (frame). We build on po- litical science and communication theory and use probabilistic topic models com- bined with time …...

In this paper we present the task of un- supervised prediction of speakers’ accept- ability judgements. We use a test set generated from the British National Cor- pus (BNC) containing both grammatical sentences and sentences containing a va- riety of syntactic infelicities introduced by round trip machine translation. This set …...

During crises such as natural disasters or other human tragedies, information needs of both civilians and responders often re- quire urgent, specialized treatment. Moni- toring and summarizing a text stream dur- ing such an event remains a difficult prob- lem. We present a system for update sum- marization which predicts …...

Automatic timeline summarization (TLS) generates precise, dated overviews over (often prolonged) events, such as wars or economic crises. One subtask of TLS se- lects the most important dates for an event within a certain time frame. Date selec- tion has up to now been handled via su- pervised machine learning …...

ive Multi-Document Summarization via Phrase Selection and Merging∗ Lidong Bing§ Piji Li♮ Yi Liao♮ Wai Lam♮ Weiwei Guo† Rebecca J. Passonneau‡ §Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA USA ♮Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong †Yahoo Labs, Sunnyvale, CA, USA ‡Center for …...

We propose Neural Responding Ma- chine (NRM), a neural network-based re- sponse generator for Short-Text Conver- sation. NRM takes the general encoder- decoder framework: it formalizes the gen- eration of response as a decoding process based on the latent representation of the in- put text, while both encoding and decod- …...

Because of their superior ability to pre- serve sequence information over time, Long Short-Term Memory (LSTM) net- works, a type of recurrent neural net- work with a more complex computational unit, have obtained strong results on a va- riety of sequence modeling tasks. The only underlying LSTM structure that has …...

We propose a convolutional neural net- work, named genCNN, for word se- quence prediction. Different from previous work on neural network- based language modeling and genera- tion (e.g., RNN or LSTM), we choose not to greedily summarize the history of words as a fixed length vector. In- stead, we use …...

This paper is concerned with building CCG-grounded, semantics-oriented deep dependency structures with a data-driven, factorization model. Three types of fac- torization together with different higher- order features are designed to capture different syntacto-semantic properties of functor-argument dependencies. Integrat- ing heterogeneous factorizations results in intractability in decoding. We pro- pose a …...

We present a constituent shift-reduce parser with a structured perceptron that finds the optimal parse in a practical run- time. The key ideas are new feature tem- plates that facilitate state merging of dy- namic programming and A* search. Our system achieves 91.1 F1 on a standard English experiment, a …...

We reduce phrase-based parsing to depen- dency parsing. Our reduction is grounded on a new intermediate representation, “head-ordered dependency trees,” shown to be isomorphic to constituent trees. By encoding order information in the depen- dency labels, we show that any off-the- shelf, trainable dependency parser can be used to produce …...

In this paper, we propose a general frame- work to incorporate semantic knowledge into the popular data-driven learning pro- cess of word embeddings to improve the quality of them. Under this framework, we represent semantic knowledge as many ordinal ranking inequalities and formu- late the learning of semantic word embed- …...

Current distributed representations of words show little resemblance to theo- ries of lexical semantics. The former are dense and uninterpretable, the lat- ter largely based on familiar, discrete classes (e.g., supersenses) and relations (e.g., synonymy and hypernymy). We pro- pose methods that transform word vec- tors into sparse (and optionally …...

Two important aspects of semantic pars- ing for question answering are the breadth of the knowledge source and the depth of logical compositionality. While existing work trades off one aspect for another, this paper simultaneously makes progress on both fronts through a new task: answering complex questions on semi-structured ta- …...

Most existing topic models make the bag- of-words assumption that words are gener- ated independently, and so ignore poten- tially useful information about word or- der. Previous attempts to use collocations (short sequences of adjacent words) in topic models have either relied on a pipe- line approach, restricted attention to …...

A key problem in semantic parsing with graph-based semantic representations is graph parsing, i.e. computing all pos- sible analyses of a given graph accord- ing to a grammar. This problem arises in training synchronous string-to-graph grammars, and when generating strings from them. We present two algorithms for graph parsing (bottom-up …...

Many existing knowledge bases (KBs), in- cluding Freebase, Yago, and NELL, rely on a fixed ontology, given as an input to the system, which defines the data to be cataloged in the KB, i.e., a hierar- chy of categories and relations between them. The system then extracts facts that match …...