Index of papers in Proc. ACL 2014 that mention
  • content words
Johnson, Mark and Christophe, Anne and Dupoux, Emmanuel and Demuth, Katherine
Introduction
Their experiments suggest that function words play a special role in the acquisition process: children learn function words before they learn the vast bulk of the associated content words , and they use function words to help identify context words.
Introduction
Traditional descriptive linguistics distinguishes function words, such as determiners and prepositions, from content words , such as nouns and verbs, corresponding roughly to the distinction between functional categories and lexical categories of modern generative linguistics (Fromkin, 2001).
Introduction
Function words differ from content words in at
Word segmentation results
Thus, the present model, initially aimed at segmenting words from continuous speech, shows three interesting characteristics that are also exhibited by human infants: it distinguishes between function words and content words (Shi and Werker, 2001), it allows learners to acquire at least some of the function words of their language (e. g. (Shi et al., 2006)); and furthermore, it may also allow them to start grouping together function words according to their category (Cauvet et al., 2014; Shi and Melancon, 2010).
Word segmentation with Adaptor Grammars
This means that “function words” are memoised independently of the “content words” that Word expands to; i.e., the model learns distinct “function word” and “content word” vocabularies.
content words is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Cohen, Shay B. and Steedman, Mark
Experimental Setup
A Reverb argument is represented as the conjunction of its content words that appear more than 10 times in the corpus.
Experimental Setup
The first, LEFTMOST, selects the leftmost content word for each predicate.
Our Proposal: A Latent LC Approach
In our experiments we attempt to keep the approach maximally general, and define H p to be the set of all subsets of size 1 or 2 of content words in Wpl.
Our Proposal: A Latent LC Approach
1We use a POS tagger to identify content words .
Our Proposal: A Latent LC Approach
Prepositions are considered content words under this definition.
content words is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Pilehvar, Mohammad Taher and Navigli, Roberto
Experiments
For ontologizing WT and OW, the bag of content words W is given by the content words in sense definitions and, if available, additional related words obtained from lexicon relations (see Section 3).
Experiments
tively small in number, are already disambiguated and, therefore, the ontologization was just performed on the definition’s content words .
Lexical Resource Ontologization
We first create the empty undirected graph G L = (V, E) such that V is the set of concepts in L and E = (D. For each source concept c E V we create a bag of content words W = {2121, .
Lexical Resource Ontologization
,wn} which includes all the content words in its definition d and, if available, additional related words obtained from lexicon relations (e.g., synonyms in Wiktionary).
Lexical Resource Ontologization
The definition contains two content words : fruitn and conifern.
Resource Alignment
In this component the personalization vector vi is set by uniformly distributing the probability mass over the nodes corresponding to the senses of all the content words in the extended definition of di according to the sense inventory of a semantic network H. We use the same semantic graph H for computing the semantic signatures of both definitions.
content words is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Zou, Bowei and Zhou, Guodong and Zhu, Qiaoming
Baselines
Since such correlation is more from the semantic perspective than the grammatical perspective, only content words are considered in our graph model, ignoring functional words (e.g., the, t0,.
Baselines
Especially, the content words limited to those with part-of-
Baselines
While the above word-based graph model can well capture the relatedness between content words , it can only partially model the focus of a negation eXpression since negation focus is more directly related with topic than content.
content words is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Tian, Ran and Miyao, Yusuke and Matsuzaki, Takuya
The Idea
Our solution is to redefine DCS trees without the aid of any databases, by considering each node of a DCS tree as a content word in a sentence (but may no longer be a table in a specific database), while each edge represents semantic relations between two words.
The Idea
0 Content words: a content word (e.g.
The Idea
A DCS tree ’2' = (N, 5) is defined as a rooted tree, where each node 0 E N is labeled with a content word 212(0) and each edge (a, 0’) E 5 C N x N is labeled with a pair of semantic roles (r, r’)7.
content words is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Cui, Lei and Zhang, Dongdong and Liu, Shujie and Chen, Qiming and Li, Mu and Zhou, Ming and Yang, Muyun
Introduction
Since the information within the sentence is insufficient for topic modeling, we first enrich sentence contexts via Information Retrieval (IR) methods using content words in the sentence as queries, so that topic-related monolingual documents can be collected.
Topic Similarity Model with Neural Network
One problem with auto-encoder is that it treats all words in the same way, making no distinguish-ment between function words and content words .
Topic Similarity Model with Neural Network
For each positive instance ( f, e), we select 6’ which contains at least 30% different content words from 6.
content words is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Frank, Stella and Feldman, Naomi H. and Goldwater, Sharon
Experiments
We restrict the corpus to content words by retaining only words tagged as adj, n, part and v (adjectives, nouns, particles, and verbs).
Experiments
As well as function words, we also remove the five most frequent content words (be, go, get, want, come).
Experiments
On average, situations are only 59 words long, reflecting the relative lack of content words in CD8 utterances.
content words is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: