Articles

Sentences form coherent relations in a discourse without discourse connectives more frequently than with connectives. Senses of these implicit discourse rela- tions that hold between a sentence pair, however, are challenging to infer. Here, we employ Brown cluster pairs to rep- resent discourse relation and incorporate coreference patterns to identify …...

Length constraints impose implicit re- quirements on the type of content that can be included in a text. Here we pro- pose the first model to computationally as- sess if a text deviates from these require- ments. Specifically, our model predicts the appropriate length for texts based on content types …...

We propose the use of a generative model to simulate user behaviour in a novel task- oriented dialog domain, where user goals are spatial routes across artificial land- scapes. We show how to derive an effi- cient feature-based representation of spa- tial goals, admitting exact inference and generalising to new …...

We propose a novel technique for adapting text-based statistical machine translation to deal with input from automatic speech recognition in spoken language translation tasks. We simulate likely misrecognition errors using only a source language pro- nunciation dictionary and language model (i.e., without an acoustic model), and use these to augment …...

Although the performance of SMT sys- tems has improved over a range of differ- ent linguistic phenomena, negation has not yet received adequate treatment. Previous works have considered the prob- lem of translating negative data as one of data sparsity (Wetzel and Bond (2012)) or of structural differences between source …...

We report an empirical investigation on type-supervised domain adaptation for joint Chinese word segmentation and POS-tagging, making use of domain- specific tag dictionaries and only un- labeled target domain data to improve target-domain accuracies, given a set of annotated source domain sentences. Pre- vious work on POS-tagging of other lan- …...

Compounding in morphologically rich languages is a highly productive process which often causes SMT approaches to fail because of unseen words. We present an approach for translation into a com- pounding language that splits compounds into simple words for training and, due to an underspecified representation, allows for free merging …...

We present a semi-supervised approach to the problem of paradigm induction from inflection tables. Our system ex- tracts generalizations from inflection ta- bles, representing the resulting paradigms in an abstract form. The process is in- tended to be language-independent, and to provide human-readable generalizations of paradigms. The tools we provide …...

Analogies are considered to be one of the core concepts of human cognition and communica- tion, and are very efficient at encoding com- plex information in a natural fashion. How- ever, computational approaches towards large- scale analysis of the semantics of analogies are hampered by the lack of suitable corpora …...

The question of data reliability is of first im- portance to assess the quality of manually an- notated corpora. Although Cohen’s κ is the prevailing reliability measure used in NLP, al- ternative statistics have been proposed. This paper presents an experimental study with four measures (Cohen’s κ, Scott’s π, binary …...

We present the first large-scale English “all- words lexical substitution” corpus. The size of the corpus provides a rich resource for investigations into word meaning. We investigate the nature of lexical substitute sets, comparing them to WordNet synsets. We find them to be consistent with, but more fine-grained than, synsets. …...

In this paper, we explore bilingual sentiment knowledge for statistical machine translation (SMT). We propose to explicitly model the consistency of sentiment between the source and target side with a lexicon-based approach. The experiments show that the proposed mod- el significantly improves Chinese-to-English NIST translation over a competitive baseline. 1...

Topic models based on latent Dirichlet al- location and related methods are used in a range of user-focused tasks including doc- ument navigation and trend analysis, but evaluation of the intrinsic quality of the topic model and topics remains an open research area. In this work, we explore the two …...

We investigate the order of mention for objects in relational descriptions in visual scenes. Existing work in the visual do- main focuses on content selection for text generation and relies primarily on tem- plates to generate surface realizations from underlying content choices. In contrast, we seek to clarify the influence …...

Automatic detection of lexical entailment, or hypernym detection, is an important NLP task. Recent hypernym detection measures have been based on the Distri- butional Inclusion Hypothesis (DIH). This paper assumes that the DIH sometimes fails, and investigates other ways of quan- tifying the relationship between the co- occurrence contexts of …...

We model scientific expertise as a mixture of topics and authority. Authority is calcu- lated based on the network properties of each topic network. ThemedPageRank, our combi- nation of LDA-derived topics with PageRank differs from previous models in that topics in- fluence both the bias and transition probabili- ties of …...

This paper takes a discourse-oriented per- spective for disambiguating common and proper noun mentions with respect to Wikipedia. Our novel approach mod- els the relationship between disambigua- tion and aspects of cohesion using Markov Logic Networks with latent variables. Considering cohesive aspects consistently improves the disambiguation results on various commonly …...

Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically sim- plify the word embeddings computation through a Hellinger PCA of the word …...

The distributional hypothesis of Harris (1954), according to which the meaning of words is evidenced by the contexts they occur in, has motivated several effec- tive techniques for obtaining vector space semantic representations of words using unannotated text corpora. This paper ar- gues that lexico-semantic content should additionally be invariant …...

In this paper, we show that the lexical function model for composition of dis- tributional semantic vectors can be im- proved by adopting a more advanced re- gression technique. We use the pathwise coordinate-descent optimized elastic-net regression method to estimate the compo- sition parameters, and compare the result- ing model …...