Articles

Much of the writing styles recognized in rhetorical and composition theories involve deep syntactic elements. However, most previous research for computational sty- lometric analysis has relied on shallow lexico-syntactic patterns. Some very re- cent work has shown that PCFG models can detect distributional difference in syn- tactic styles, but without …...

Discriminative training in query spelling cor- rection is difficult due to the complex inter- nal structures of the data. Recent work on query spelling correction suggests a two stage approach a noisy channel model that is used to retrieve a number of candidate corrections, followed by discriminatively trained ranker applied …...

The geographical properties of words have re- cently begun to be exploited for geolocating documents based solely on their text, often in the context of social media and online content. One common approach for geolocating texts is rooted in information retrieval. Given training documents labeled with latitude/longitude co- ordinates, a …...

The question “how predictable is English?” has long fascinated researchers. While prior work has focused on formal English typically used in news articles, we turn to texts gener- ated by users in online settings that are more informal in nature. We are motivated by a novel application scenario: given the …...

We consider the task of predicting the gender of the YouTube1 users and contrast two infor- mation sources: the comments they leave and the social environment induced from the af- filiation graph of users and videos. We prop- agate gender information through the videos and show that a user’s gender …...

Activities on social media increase at a dra- matic rate. When an external event happens, there is a surge in the degree of activities re- lated to the event. These activities may be temporally correlated with one another, but they may also capture different aspects of an event and therefore …...

Most current dependency parsers presuppose that input words have been morphologically disambiguated using a part-of-speech tagger before parsing begins. We present a transition- based system for joint part-of-speech tagging and labeled dependency parsing with non- projective trees. Experimental evaluation on Chinese, Czech, English and German shows consistent improvements in both …...

We present an automatic method for mapping language-specific part-of-speech tags to a set of universal tags. This unified representation plays a crucial role in cross-lingual syntactic transfer of multilingual dependency parsers. Until now, however, such conversion schemes have been created manually. Our central hy- pothesis is that a valid mapping …...

Most previous approaches to syntactic pars- ing of Chinese rely on a preprocessing step of word segmentation, thereby assuming there was a clearly defined boundary between mor- phology and syntax in Chinese. We show how this assumption can fail badly, leading to many out-of-vocabulary words and incom- patible annotations. Hence …...

State-of-the-art statistical parsers and POS taggers perform very well when trained with large amounts of in-domain data. When train- ing data is out-of-domain or limited, accuracy degrades. In this paper, we aim to compen- sate for the lack of available training data by exploiting similarities between test set sen- tences. …...

A popular tradition of studying semantic rep- resentation has been driven by the assump- tion that word meaning can be learned from the linguistic environment, despite ample ev- idence suggesting that language is grounded in perception and action. In this paper we present a comparative study of models that represent …...

We present a new minimally-supervised framework for performing domain-driven Word Sense Disambiguation (WSD). Glos- saries for several domains are iteratively ac- quired from the Web by means of a boot- strapping technique. The acquired glosses are then used as the sense inventory for fully- unsupervised domain WSD. Our experiments, on …...

We present a multilingual joint approach to Word Sense Disambiguation (WSD). Our method exploits BabelNet, a very large mul- tilingual knowledge base, to perform graph- based WSD across different languages, and brings together empirical evidence from these languages using ensemble methods. The re- sults show that, thanks to complementing wide-coverage …...

Despite significant recent work, purely unsu- pervised techniques for part-of-speech (POS) tagging have not achieved useful accuracies required by many language processing tasks. Use of parallel text between resource-rich and resource-poor languages is one source of weak supervision that significantly improves accu- racy. However, parallel text is not always available …...

In modern Chinese articles or conversations, it is very popular to involve a few English words, especially in emails and Internet liter- ature. Therefore, it becomes an important and challenging topic to analyze Chinese-English mixed texts. The underlying problem is how to tag part-of-speech (POS) for the English words involved. …...

We investigate the use of language in food writing, specifically on restaurant menus and in customer reviews. Our approach is to build predictive models of concrete external vari- ables, such as restaurant menu prices. We make use of a dataset of menus and customer reviews for thousands of restaurants in …...

We introduce a novel approach named unam- biguity regularization for unsupervised learn- ing of probabilistic natural language gram- mars. The approach is based on the observa- tion that natural language is remarkably unam- biguous in the sense that only a tiny portion of the large number of possible parses of …...

This paper proposes a novel approach to extract opinion targets based on word- based translation model (WTM). At first, we apply WTM in a monolingual scenario to mine the associations between opinion targets and opinion words. Then, a graph- based algorithm is exploited to extract opinion targets, where candidate opinion …...

Extracting opinion expressions from text is usually formulated as a token-level sequence labeling task tackled using Conditional Ran- dom Fields (CRFs). CRFs, however, do not readily model potentially useful segment-level information like syntactic constituent struc- ture. Thus, we propose a semi-CRF-based ap- proach to the task that can perform sequence …...

Representation learning is a promising tech- nique for discovering features that allow su- pervised classifiers to generalize from a source domain dataset to arbitrary new domains. We present a novel, formal statement of the rep- resentation learning task. We argue that be- cause the task is computationally intractable in general, …...