Articles

Determining the semantic intent of web queries not only involves identifying their semantic class, which is a primary focus of previous works, but also understanding their semantic structure. In this work, we formally define the semantic structure of noun phrase queries as comprised of intent heads and intent modifiers. We …...

An important relation in information ex- traction is the part-whole relation. On- tological studies mention several types of this relation. In this paper, we show that the traditional practice of initializ- ing minimally-supervised algorithms with a single set that mixes seeds of different types fails to capture the wide variety …...

Definition extraction is the task of au- tomatically identifying definitional sen- tences within texts. The task has proven useful in many research areas including ontology learning, relation extraction and question answering. However, current ap- proaches – mostly focused on lexico- syntactic patterns – suffer from both low recall and precision, …...

We present a novel framework for auto- mated extraction and approximation of nu- merical object attributes such as height and weight from the Web. Given an object-attribute pair, we discover and ana- lyze attribute information for a set of com- parable objects in order to infer the desired value. This …...

We present a novel fully unsupervised al- gorithm for POS induction from plain text, motivated by the cognitive notion of proto- types. The algorithm first identifies land- mark clusters of words, serving as the cores of the induced POS categories. The rest of the words are subsequently mapped to these …...

We present an approach to multilin- gual grammar induction that exploits a phylogeny-structured model of parameter drift. Our method does not require any translated texts or token-level alignments. Instead, the phylogenetic prior couples languages at a parameter level. Joint in- duction in the multilingual model substan- tially outperforms independent learning, …...

We show how web mark-up can be used to improve unsupervised dependency pars- ing. Starting from raw bracketings of four common HTML tags (anchors, bold, ital- ics and underlines), we refine approximate partial phrase boundaries to yield accurate parsing constraints. Conversion proce- dures fall out of our linguistic analysis of …...

In this paper, we address the task of mapping high-level instructions to se- quences of commands in an external en- vironment. Processing these instructions is challenging—they posit goals to be achieved without specifying the steps re- quired to complete them. We describe a method that fills in missing informa- tion …...

This paper presents a novel approach to automatic captioning of geo-tagged images by summarizing multiple web- documents that contain information re- lated to an image’s location. The summa- rizer is biased by dependency pattern mod- els towards sentences which contain fea- tures typically provided for different scene types such as …...

Music Recommendation Systems often recommend individual songs, as opposed to entire albums. The challenge is to gen- erate reviews for each song, since only full album reviews are available on-line. We developed a summarizer that combines in- formation extraction and generation tech- niques to produce summaries of reviews of individual …...

In this paper we tackle the problem of au- tomatic caption generation for news im- ages. Our approach leverages the vast re- source of pictures available on the web and the fact that many of them are cap- tioned. Inspired by recent work in sum- marization, we propose extractive and …...

Quantifying the semantic relevance be- tween questions and their candidate an- swers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed to model the semantic relevance for question-answer pairs. Observing the textual similarity between the community-driven question- answering (cQA) dataset and …...

We propose a global algorithm for learn- ing entailment relations between predi- cates. We define a graph structure over predicates that represents entailment rela- tions as directed edges, and use a global transitivity constraint on the graph to learn the optimal set of edges, by formulating the optimization problem as …...

This paper proposes an approach to ref- erence resolution in situated dialogues by exploiting extra-linguistic information. Recently, investigations of referential be- haviours involved in situations in the real world have received increasing attention by researchers (Di Eugenio et al., 2000; Byron, 2005; van Deemter, 2007; Spanger et al., 2009). In …...

Discourse references, notably coreference and bridging, play an important role in many text understanding applications, but their impact on textual entailment is yet to be systematically understood. On the ba- sis of an in-depth analysis of entailment instances, we argue that discourse refer- ences have the potential of substantially improving …...

We challenge the NLP community to par- ticipate in a large-scale, distributed effort to design and build resources for devel- oping and evaluating solutions to new and existing NLP tasks in the context of Rec- ognizing Textual Entailment. We argue that the single global label with which RTE examples are …...

Hierarchical HMM (HHMM) parsers make promising cognitive models: while they use a bounded model of working memory and pursue incremental hypothe- ses in parallel, they still achieve parsing accuracies competitive with chart-based techniques. This paper aims to validate that a right-corner HHMM parser is also able to produce complexity metrics, …...

Probabilistic models of sentence com- prehension are increasingly relevant to questions concerning human language processing. However, such models are of- ten limited to syntactic factors. This paper introduces a novel sentence processing model that consists of a parser augmented with a probabilistic logic-based model of coreference resolution, which allows us …...

A number of results in the study of real- time sentence comprehension have been explained by computational models as re- sulting from the rational use of probabilis- tic linguistic information. Many times, these hypotheses have been tested in read- ing by linking predictions about relative word difficulty to word-aggregated eye …...

We report on an experiment to track com- plex decision points in linguistic meta- data annotation where the decision behav- ior of annotators is observed with an eye- tracking device. As experimental con- ditions we investigate different forms of textual context and linguistic complexity classes relative to syntax and semantics. …...