Cultural fit is widely believed to affect the success of individuals and the groups to which they belong.Yet it remains an elusive, poorly measured construct.Recent research draws on computational linguistics to measure cultural fit but overlooks asymmetries in cultural adaptation.By contrast, we develop a directed, dynamic measure of cultural fit based on linguistic alignment, which estimates the influence of one person's word use on another's and distinguishes between two enculturation mechanisms: internalization and selfregulation.We use this measure to trace employees' enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals downstream outcomes, especially involuntary exit.Further predictive analyses suggest referential alignment plays an overlooked role in linguistic alignment.

In conversation, speakers tend to "accommodate" or "align" to their partners, changing the style and substance of their communications to be more similar to their partners' utterances.We focus here on "linguistic alignment," changes in word choice based on others' choices.Although linguistic alignment is observed across many different contexts and its degree correlates with important social factors such as power and likability, its sources are still uncertain.We build on a recent probabilistic model of alignment, using it to separate out alignment attributable to words versus word categories.We model alignment in two contexts: telephone conversations and microblog replies.Our results show evidence of alignment, but it is primarily lexical rather than categorical.Furthermore, we find that discourse acts modulate alignment substantially.This evidence supports the view that alignment is shaped by strategic communicative processes related to the ongoing discourse.

We propose a non-parametric Bayesian model for learning and weighting symbolically-defined constraints to populate a log-linear model. The model jointly infers a vector of binary constraint values for each candidate output and likely definitions for these constraints, combining observations of the output classes with a (potentially infinite) grammar over potential constraint definitions. We present results on a small morphophonological system, English regular plurals, as a test case. The inferred constraints, based on a grammar of articulatory features, perform as well as theoretically-defined constraints on both observed and novel forms of English regular plurals. The learned constraint values and definitions also closely resemble standard constraints defined within phonological theory.

When people talk, they tend to adopt the behaviors, gestures, and language of their conversational partners. This "accommodation" to one's partners is largely automatic, but the degree to which it occurs is influenced by social factors, such as gender, relative power, and attraction. In settings where such social information is not known, this accommodation can be a useful cue for the missing information. This is especially important in web-based communication, where social dynamics are often fluid and rarely stated explicitly. But connecting accommodation and social dynamics on the web requires accurate quantification of the different amounts of accommodation being made.

If speakers use language rationally, they should structure their messages to achieve approximately uniform information density (UID), in order to optimize transmission via a noisy channel.Previous work identified a consistent increase in linguistic information across sentences in text as a signature of the UID hypothesis.This increase was derived from a predicted increase in context, but the context itself was not quantified.We use microblog texts from Twitter, tied to a single shared event (the baseball World Series), to quantify both linguistic and non-linguistic context.By tracking changes in contextual information, we predict and identify gradual and rapid changes in information content in response to in-game events.These findings lend further support to the UID hypothesis and highlights the importance of nonlinguistic common ground for language production and processing.

We present a method to jointly learn fea-tures and weights directly from distri-butional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonologi-cal markedness constraints directly from the distribution of input-output mappings in an Optimality Theory (OT) setting. The model uses an Indian Buffet Process prior to learn the feature values used in the log-linear method, and is the first algorithm for learning phonological constraints with-out presupposing constraint structure. The model learns a system of constraints that explains observed data as well as the phonologically-grounded constraints of a standard analysis, with a violation struc-ture corresponding to the standard con-straints. These results suggest an alterna-tive data-driven source for constraints in-stead of a fully innate constraint set. 1

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic feature spaces for different content modalities. Two hypotheses are then investigated regarding the fundamental attributes of these spaces. The first is that low-level cross-modal correlations should be accounted for. The second is that the space should enable semantic abstraction. Three new solutions to the cross-modal retrieval problem are then derived from these hypotheses: correlation matching (CM), an unsupervised method which models cross-modal correlations, semantic matching (SM), a supervised technique that relies on semantic representation, and semantic correlation matching (SCM), which combines both. An extensive evaluation of retrieval performance is conducted to test the validity of the hypotheses. All approaches are shown successful for text retrieval in response to image queries and vice versa. It is concluded that both hypotheses hold, in a complementary form, although evidence in favor of the abstraction hypothesis is stronger than that for correlation.

The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly modeling correlations between the two components, and 2) this modeling is more effective in feature spaces with higher levels of abstraction. Correlations between the two components are learned with canonical correlation analysis. Abstraction is achieved by representing text and images at a more general, semantic level. The two hypotheses are studied in the context of the task of cross-modal document retrieval. This includes retrieving the text that most closely matches a query image, or retrieving the images that most closely match a query text. It is shown that accounting for cross-modal correlations and semantic abstraction both improve retrieval accuracy. The cross-modal model is also shown to outperform state-of-the-art image retrieval systems on a unimodal retrieval task.

Many different topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suffer from the important flaw that they do not capture the tendency of words to appear in bursts; it is a fundamental property of language that if a word is used once in a document, it is more likely to be used again. We introduce a topic model that uses Dirichlet compound multinomial (DCM) distributions to model this burstiness phenomenon. On both text and non-text datasets, the new model achieves better held-out likelihood than standard latent Dirichlet allocation (LDA). It is straightforward to incorporate the DCM extension into topic models that are more complex than LDA.