[PDF] Top 20 Topic Models for Meaning Similarity in Context
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Topic Models for Meaning Similarity in Context
... Discovery of Inference Rules from Text (DIRT) A popular distributional method for meaning re- latedness is the DIRT algorithm for extracting in- ference rules (Lin and Pantel, 2001a). In this al- gorithm a pattern ... See full document
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Measuring Distributional Similarity in Context
... of meaning similarity as operationalized by vector-based models has found widespread use in many tasks ranging from the acquisition of synonyms and para- phrases to word sense disambiguation and tex- ... See full document
11
Polylingual Topic Models
... Statistical topic models have emerged as an in- creasingly useful analysis tool for large text col- ...lections. Topic models have been used for analyz- ing topic trends in research ... See full document
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Is this a Child, a Girl or a Car? Exploring the Contribution of Distributional Similarity to Learning Referential Word Meanings
... the meaning of the word child will most probably know a) how to distin- guish children from other entities in the real world and b) that child is related to other words, such as girl, boy, mother, ...lexical ... See full document
6
Exemplar Based Models for Word Meaning in Context
... some similarity measure such as Cosine or Jaccard, and θ(E, s) is a ...their similarity to s exceeds θ(E, ...the similarity of the k-th most similar ... See full document
6
An Entity Topic Model for Entity Linking
... the context compatibility and the topic coherence are first separately modeled, then their EL evidence are combined through an additional ...first models the context compatibility as a ... See full document
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Probabilistic models of similarity in syntactic context
... Distributional models of lexical semantics, which assume that aspects of a word’s meaning can be re- lated to the contexts in which that word is typically used, have a long history in Natural Language Pro- ... See full document
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Contextually Mediated Semantic Similarity Graphs for Topic Segmentation
... We have presented an approach to text segmen- tation that relies on a novel graph based repre- sentation of document structure and semantics. It successfully models topical coherence using long-range influence of ... See full document
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A comparison of models of word meaning in context
... posed models for computing context sensitive word ...these models, simplify their formula- tion and evaluate them in a unified ...the models are essentially equivalent if syntactic information ... See full document
5
Analogies in Complex Verb Meaning Shifts: the Effect of Affect in Semantic Similarity Models
... Distributional Similarity Model We created a basic DSM to represent all BVs and all PVs by using a corpus-derived 300- dimensional vector ...all context words within a symmetrical window of size ... See full document
7
Measuring Word Meaning in Context
... The Usim data, which directly describes the similarity of pairs of usages, can be used to evaluate distributional models of word meaning in context. So far, only one type of model has been ... See full document
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Using Topic Modeling and Similarity Thresholds to Detect Events
... The data sets need to be sufficiently large in size and time horizon in order for our ETD algorithm to be useful. The Reuters News Corpus spanned 525 days, and an even longer spanning corpus could yield bet- ter results. ... See full document
9
A Topic Similarity Model for Hierarchical Phrase based Translation
... bilingual topic model from bilingual corpus (Mimno et ...lingual topic distribution using the bilingual topic ...bilingual topic model avoid this problem by some monolingual ...the ... See full document
9
Blackbox Meets Blackbox: Representational Similarity & Stability Analysis of Neural Language Models and Brains
... as context length is ...different models, we find that both architectural differences and differ- ent training objectives have a noticeable impact on the representations learned by the models and the ... See full document
13
The Meaning of UML Models
... The interaction of modalities and quantification is notoriously complicated [Fit99]. Consider the apparently simple sentence “everything is always good”. When we say “everything”, do we mean everything that exists now, ... See full document
205
Similarity Based Reconstruction Loss for Meaning Representation
... We find that almost all of the proposed loss func- tions outperform the vanilla autoencoder trained with cross-entropy on all three tasks (see Table 1). The only exception is the weighted similarity loss function. ... See full document
6
Towards Metacognition, Autonomy, and Learners’ Knowledge/Meaning Construction in EFL Context
... on meaning than form along with students’ conceptualizations of the uses/functions of the tenses/aspects under ...study. Meaning-oriented instruction also occurred in moments of displaying and contrasting ... See full document
5
Evaluating a Topic Modelling Approach to Measuring Corpus Similarity
... In constructing traditional corpora, such as the British Na- tional Corpus (Burnard, 2000, BNC), documents are cho- sen based on particular selection criteria such as domain, genre, and time period. The composition of ... See full document
7
Vector space models for PPDB paraphrase ranking in context
... the meaning of a spe- cific occurrence is determined by choosing the vec- tor that minimizes the distance to the vector repre- senting the current ...Their models allow the computation of vector repre- ... See full document
7
Authorship Attribution with Topic Models
... generic topic models that incorporate metadata labels (Blei ...These models can be divided into two types: upstream models, which use the labels to constrain the topics, and downstream ... See full document
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