Word Neighborhood Research Articles

Measuring Phonological Complexity Using the Perfect Rhymes Dictionary (PeRDict)

This paper examines links between perfect rhymes and text readability and decoding using a measure of English rhymes called the Perfect Rhymes Dictionary (PeRDict). PeRDict is based on the Carnegie Mellon University Pronouncing Dictionary (the CMUdict) and provides rhyme counts for ∼48,000 words in English and for the most frequent 1,000, 2,500, 5,000, and 10,000 rhymes within the dictionary as measured by the Corpus of American English (COCA). Two assessments of PeRDICT are presented. The first examines the strength of rhyme features to predict text readability in conjunction with word neighborhood density effects reported by the English Lexicon Project (ELP) and a word frequency measure. The second examines the strength of rhyme features to predict word decoding in conjunction with word neighborhood effects and a word frequency measure. In both assessments, the number of rhymes per word was predictive of text or word processing beyond features related to word neighborhood effects and word frequency. Word rhyme counts performed more strongly in predicting text processing versus word processing.

Neural representation of phonological wordform in temporal cortex.

While the neural bases of the earliest stages of speech categorization have been widely explored using neural decoding methods, there is still a lack of consensus on questions as basic as how wordforms are represented and in what way this word-level representation influences downstream processing in the brain. Isolating and localizing the neural representations of wordform is challenging because spoken words activate a variety of representations (e.g., segmental, semantic, articulatory) in addition to form-based representations. We addressed these challenges through a novel integrated neural decoding and effective connectivity design using region of interest (ROI)-based, source-reconstructed magnetoencephalography/electroencephalography (MEG/EEG) data collected during a lexical decision task. To identify wordform representations, we trained classifiers on words and nonwords from different phonological neighborhoods and then tested the classifiers' ability to discriminate between untrained target words that overlapped phonologically with the trained items. Training with word neighbors supported significantly better decoding than training with nonword neighbors in the period immediately following target presentation. Decoding regions included mostly right hemisphere regions in the posterior temporal lobe implicated in phonetic and lexical representation. Additionally, neighbors that aligned with target word beginnings (critical for word recognition) supported decoding, but equivalent phonological overlap with word codas did not, suggesting lexical mediation. Effective connectivity analyses showed a rich pattern of interaction between ROIs that support decoding based on training with lexical neighbors, especially driven by right posterior middle temporal gyrus. Collectively, these results evidence functional representation of wordforms in temporal lobes isolated from phonemic or semantic representations.

Processing difficulty while reading words with neighbors is not due to increased foveal load: Evidence from eye movements.

Words with high orthographic relatedness are termed "word neighbors" (angle/angel; birch/birth). Activation-based models of word recognition assume that lateral inhibition occurs between words and their activated neighbors. However, studies of eye movements during reading have not found inhibitory effects in early measures assumed to reflect lexical access (e.g., gaze duration). Instead, inhibition in eye-movement studies has been found in later measures of processing (e.g., total time, regressions in). We conducted an eye-movement boundary change study (Rayner, Cognitive Psychology, 7(1), 65-81, 1975) that manipulated the parafoveal preview of the word following the neighbor word (word N+1). In this way, we explored whether the late inhibitory effects seen with transposed letter words and words with higher-frequency neighbors result from reduced parafoveal preview due to increased foveal load and/or interference during late stages of lexical processing (the L2 stage within the E-Z Reader framework). For word N+1, while there were clear preview effects, there was not an effect of the neighborhood status of word N, nor a significant interaction. This suggests that the late inhibitory effects of earlier eye-movement studies are driven by misidentification of neighbor words rather than being due to increased foveal load.

Semisupervised Learning-Based Word-Sense Disambiguation Using Word Embedding for Afaan Oromoo Language

Natural language is a type of language that human beings use to communicate with each other. However, it is very difficult to communicate with a machine-understandable language. Finding context meaning is challenging the activity of automatically identifying machine translation, indexing engines, and predicting neighbor words in natural language. Many researchers around the world investigated word-sense disambiguation in different languages, including the Afaan Oromo language, to solve this challenge. Nevertheless, the amount of effort for Afaan Oromo is very little in terms of finding context meaning and predicting neighbor words to solve the word ambiguity problem. Since the Afaan Oromo language is one of the languages developed in Ethiopia, it needs the latest technology to enhance communication and overcome ambiguity challenges. So far, this work aims to design and develop a vector space model for the Afaan Oromo language that can provide the application of word-sense disambiguation to increase the performance of information retrieval. In this work, the study has used the Afaan Oromo word embedding method to disambiguate a contextual meaning of words by applying the semisupervised technique. To conduct the study, 456,300 Afaan Oromo words were taken from different sources and preprocessed for experimentation by the Natural Language Toolkit and Anaconda tool. The K-means machine learning algorithm was used to cluster similar word vocabulary. Experimental results show that using word embedding for the proposed language’s corpus improves the performance of the system by a total accuracy of 98.89% and outperforms the existing similar systems.

Spoken word recognition in French

Different linguistic factors can influence the recognition of spoken words in French. We are interested in the impact of the linguistic factor of phonological density, which refers to the number of phonological neighbours of words and which is related to the recognition process according to a principle of inhibition and lexical competition. By using a lexical decision task, where half of the words have a low phonological density, and the other half a high phonological density, we aim to measure the reaction times of a group of young French-speaking participants (18-35 years old). The results of the young French-speaking group did not show a phonological density effect, however, these results did show a lexicality effect, demonstrating that the word and pseudo-word recognition task worked well. Thus, from the error analyses, the involvement of another factor, word familiarity, was induced. The post-experimental survey on this new factor showed an imbalance between the high and low density groups. By removing the low familiarity items from the analysis, a significant phonological density effect was obtained at this time. It is essential to discuss the role of word familiarity on spoken word recognition; as well as the material development for spoken word recognition tasks.

Document Classification with Contextually Enriched Word Embeddings

The text classification task has a wide range of application domains for distinct purposes, such as the classification of articles, social media posts, and sentiments. As a natural language processing application, machine learning and deep learning techniques are intensively utilized in solving such challenges. One common approach is employing the discriminative word features comprising Bag-of-Words and n-grams to conduct text classification experiments. The other powerful approach is exploiting neural network-based (specifically deep learning models) through either sentence, word, or character levels. In this study, we proposed a novel approach to classify documents with contextually enriched word embeddings powered by the neighbor words accessible through the trigram word series. In the experiments, a well-known web of science dataset is exploited to demonstrate the novelty of the models. Consequently, we built various models constructed with and without the proposed approach to monitor the models' performances. The experimental models showed that the proposed neighborhood-based word embedding enrichment has decent potential to use in further studies.

Segmentation of Spoken Overlapping Ambiguity Strings in Chinese: An Eye-Tracking Study.

Using the printed-word paradigm with eye tracking, this study conducted three experiments to examine (a) how multiple words in spoken overlapping ambiguity strings (OASs) are activated, (b) how word frequency influences the word segmentation of spoken OASs, and (c) whether the multiple words in spoken OASs are activated competitively or independently. In this study, participants listened to a four-character spoken OAS (ABCD) and were presented with a visual display composed of a semantic associate of the "middle word" (BC; Experiments 1 and 2) or the "left word" (AB; Experiment 3) and two distractors. In Experiment 1, the word frequency of the middle words was manipulated to be higher than that of the neighbor words. In Experiment 2, the word frequency of the middle words was manipulated to be either higher or lower than that of the neighbor words. In Experiment 3, participants listened to either spoken OASs (ABCD) or spoken unambiguous strings (ABEF). In Experiment 1, we observed a significant semantic competition effect; that is, more fixations fell on the semantic competitors than on distractors, suggesting that the semantic information of the middle words in the spoken OASs was activated. In Experiment 2, the semantic competition effect was only observed in the high-frequency condition and was absent in the low-frequency condition. In Experiment 3, the results showed significant semantic competition effects for the left words under both conditions, and the observed effect was similar between the ambiguity condition and the control condition. These findings show that multiple words in spoken OASs are all activated and the activation level is modulated by word frequency. In addition, multiple words in the spoken OASs may be processed independently during spoken comprehension.

Lexical competition in the flankers task revisited.

We investigated the impact of flanking stimuli that are orthographic neighbors of central target words in the reading version of the flankers task. Experiment 1 provided a replication of the finding that flanking words that are orthographic neighbors of central target words (e.g., BLUE BLUR BLUE) facilitate lexical decisions relative to unrelated word flankers (e.g., STEP BLUR STEP). Experiment 2 tested the hypothesis that this facilitatory effect might be due to the task that was used in Experiment 1 and in prior research-the lexical decision task. In Experiment 2 the task was perceptual identification, and here we observed that orthographic neighbor flankers interfered with target word identification. Experiment 2 also included a bigram flanker condition (e.g., BL BLUR UE), and here the related bigram flankers facilitated target word identification. We conclude that when the task requires identification of a specific word, effects of lexical competition emerge over and above the facilitatory effects driven by the sublexical spatial pooling of orthographic information across target and flankers, and that the inhibitory influence of lexical competition has an even stronger impact when flankers are whole words.

Generating survey draft based on closeness of position distributions of key words

Automatically generating a survey draft is a challenge to text summarization research because it needs to select important sentences from important references in a large set of candidate papers for composing sections that are in line with section titles and different sections discuss the most relevant reference papers of different number, which are beyond the capability of previous text summarization approaches as they assume that all candidate papers should be included into one summary. This paper proposes an approach to generating survey draft according to a pattern consisting of sections with titles given by the user who requests the survey. The problem of generating each section can be divided into the following sub-problems: (1) rank the input scientific documents (in short documents) according to the title of a section, (2) determine the number of documents that are most relevant to the title, and (3) rank and select sentences from the selected documents according to the title. A position closeness distance of key word is proposed to rank a set of documents by measuring how closely two key words within section title are distributed within each document, which is used to rank the documents. The rationale is that the positions of the neighboring key words of a section title should be closer in more relevant documents than other words. As different sections have different number of selected documents, a method is proposed to determine the number of documents to be included into the current section based on the slope shape of the sorted rank curve of documents according to the section title. Based on the duality property of the closeness, ranks of sentences within a document can be directly obtained when the document is ranked according to the title of section, and both the importance and coherence of selected sentences can be reflected without extra calculation for ranking sentences. Experiments and manual evaluation show that the proposed methods achieve significant improvements compared with other approaches. The proposed approach is significant in applications as different surveys can be generated according to different patterns given by different users.

Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.

Prosodic words are the domain of emphasis spread: evidence from Northern Rural Jordanian Arabic

Abstract This article, which reports on an experimental study based on data obtained from 86 native speakers of Northern Rural Jordanian Arabic (NRJA), offers empirical evidence that emphasis spread can apply bidirectionally across word edges when they are prosodically word internal. In other words, emphasis can spread from one word to a neighboring (plain) word when the two words form one prosodic word (ω). In order to support this hypothesis, we examine simple construct-state nominals (i.e., nominals consisting of two members), which are widely assumed to behave prosodically as one word in Arabic grammar (see, e.g., Borer, Hagit. 1999. Deconstructing the construct. In Kyle Johnson & Ian Roberts (eds.), Beyond principles and parameters, 3–89. Dordrecht: Kluwer). A simple construct state nominal is mapped onto one ω, as proposed in the current article. Additionally, this article examines instances of construct state nominals in which two words could be members of a ω by cliticization. In this case, a weak form (e.g., a reduced functional word such as an auxiliary verb) is attached to the ω of a lexical word, as an enclitic. The article shows that emphasis spreads across word edges within the ω mapped from a simple construct state nominal or a word along with its enclitics. In so doing, it provides evidence that the domain of emphasis spread is larger than the syllable, morpheme, or single-word form, as proposed by other studies for other Arabic varieties.

Masked orthographic neighbor priming effects in Chinese two-character words

AbstractIn masked priming lexical decision tasks in alphabetic or syllabic script languages, latencies are longer when a word target is primed by a higher frequency neighbor (e.g., blue–BLUR) than when primed by an unrelated word of equivalent frequency (e.g., care–BLUR) – an “inhibitory neighbor priming effect.” In contrast, Zhou et al. (1999) demonstrated facilitatory orthographic neighbor priming for two-character Chinese words (e.g., 华丽–华贵). However, Zhou et al. did not control for relative prime-target frequency, which has been shown to be important in experiments when examining languages with other scripts. In the present Experiment 1 word neighbor primes (e.g., 容易-容貌) produced an inhibitory neighbor priming effect when the prime was higher frequency than the target, paralleling effects in other script languages. In further experiments, paralleling those in other script languages, two-character targets primed by nonword neighbors (容待-容貌) or single constituent characters matching the target in either position (容-容貌) showed significant facilitation. These results suggest that lexical activation/competition processes for two-character Chinese words are reasonably similar to those for words in alphabetic/syllabic script languages.

Orthographic priming from unrelated primes: Heterogeneous feedforward inhibition predicted by associative learning

A common assumption among models of orthographic processing is that letter-word inhibitory relationships all share the same strength: activity in the letter B has the same impact on a word like RACE as does equivalent activity in the letter F. However, basic associative learning mechanisms imply that the existence of the neighbor word FACE gives more opportunity to learn a negative weight from the neighbor letter F than from the non-neighbor letter B, leading to stronger negative letter-word weights for neighbor than non-neighbor letters. In masked primed lexical decision, therefore, fity, a neighborly prime formed using neighbor letters, should be a more inhibitory prime for RACE than bund (vice versa for LARK). We present simulations of weight learning using Rescorla and Wagner’s (1972) equations and three experiments consistent with this prediction. Further simulations show heterogeneous feedforward connections from letters to words could contribute to phenomena previously attributed to lexical competition.

Weighted Joint Sentiment-Topic Model for Sentiment Analysis Compared to ALGA: Adaptive Lexicon Learning Using Genetic Algorithm.

Latent Dirichlet Allocation (LDA) is an approach to unsupervised learning that aims to investigate the semantics among words in a document as well as the influence of a subject on a word. As an LDA-based model, Joint Sentiment-Topic (JST) examines the impact of topics and emotions on words. The emotion parameter is insufficient, and additional parameters may play valuable roles in achieving better performance. In this study, two new topic models, Weighted Joint Sentiment-Topic (WJST) and Weighted Joint Sentiment-Topic 1 (WJST1), have been presented to extend and improve JST through two new parameters that can generate a sentiment dictionary. In the proposed methods, each word in a document affects its neighbors, and different words in the document may be affected simultaneously by several neighbor words. Therefore, proposed models consider the effect of words on each other, which, from our view, is an important factor and can increase the performance of baseline methods. Regarding evaluation results, the new parameters have an immense effect on model accuracy. While not requiring labeled data, the proposed methods are more accurate than discriminative models such as SVM and logistic regression in accordance with evaluation results. The proposed methods are simple with a low number of parameters. While providing a broad perception of connections between different words in documents of a single collection (single-domain) or multiple collections (multidomain), the proposed methods have prepared solutions for two different situations (single-domain and multidomain). WJST is suitable for multidomain datasets, and WJST1 is a version of WJST which is suitable for single-domain datasets. While being able to detect emotion at the level of the document, the proposed models improve the evaluation outcomes of the baseline approaches. Thirteen datasets with different sizes have been used in implementations. In this study, perplexity, opinion mining at the level of the document, and topic_coherency are employed for assessment. Also, a statistical test called Friedman test is used to check whether the results of the proposed models are statistically different from the results of other algorithms. As can be seen from results, the accuracy of proposed methods is above 80% for most of the datasets. WJST1 achieves the highest accuracy on Movie dataset with 97 percent, and WJST achieves the highest accuracy on Electronic dataset with 86 percent. The proposed models obtain better results compared to Adaptive Lexicon learning using Genetic Algorithm (ALGA), which employs an evolutionary approach to make an emotion dictionary. Results show that the proposed methods perform better with different topic number settings, especially for WJST1 with 97% accuracy at |Z| = 5 on the Movie dataset.

Task-specific dependency-based word embedding methods

While most traditional word embedding methods target generic tasks, two task-specific dependency-based word embedding methods are proposed for better performance in text classification tasks in this work. First, we exploit the dependency parsing tree structure to capture the structural information of a sentence, and develop a method called dependency-based word embedding (DWE). It finds keywords and neighbor words of a target word as contexts via dependency parsing. Next, we leverage the word-class co-occurrence statistics to model the class distributional information and incorporate it into the embedding learning process. This leads to the class-enhanced dependency-based word embedding (CEDWE) method. Task-specific corpora and the matrix-factorization-based framework are used to train DWE and CEDWE. Seven text classification datasets are used to evaluate the performance of DWE and CEDWE, and experimental results show that they outperform several state-of-the-art word embedding methods.

Learning, Inside and Out: Prior Linguistic Knowledge and Learning Environment Impact Word Learning in Bilingual Individuals

Although several studies have focused on novel word learning and lexicalization in (presumably) monolingual speakers, less is known about how bilinguals add novel words to their mental lexicon. In this study we trained 33 English–French bilinguals on novel word-forms that were neighbors to English words with no existing neighbors. The number of novel neighbors to each English word varied, as did the cross-linguistic orthographic overlap between the English word and its French translation. We assessed episodic memory and lexicalization of the novel words before and after a consolidation period. Cross-linguistic similarity enhanced episodic memory of novel neighbors only when neighborhood density among the novel neighbors was low. We also found evidence that novel neighbors of English words with high cross-linguistic similarity became lexicalized after a consolidation period. Overall, the results suggest that similarity to preexisting lexical representations crucially impacted lexicalization of novel words by bilingual individuals.

The Use of Clitics In Novel “Murder on The Orient Express” By Agatha Christee

Clitics are words or parts of words that are structurally depend on neighboring words (it is the host) and cannot stand alone. The objectives of the research are 1) to investigate the types of clitics in the Novel “Murder on the Orient Express” by Agatha Crhistee. 2) to investigate the dominant types of clitics in the novel “Murder on the Orient Express” by Agatha Crhistee. This research used qualitative descriptive method and the instrument used was the researchers. The data of this research is taken from the novel “Murder on the Orient Express” by Agatha Christee. The data analysis technique used is Miles and Huberman technique, the steps are: data reduction, data display, and drawing conclusion and verification. From the research results obtained that the researchers found 699 clitics. There are 22 proclitics and 677 enclitics. Proclitic only has two kinds (o') and (d'). While enclitic has eight kinds, namely ('m), ('re), ('ll), ('ve), ('d), ('s), (n't), and genetive ('s) and the percentage shows that enclitic is 96.85% greater than the proclitic is 3.15%. So it can be concluded that enclitic is the most dominant used in novel Murder on the Orient Express by Agatha Christee.

Semantic Relation Extraction from Cultural Heritage Archives

Digital preservation technologies are now being increasingly adopted by cultural heritage organizations. This cultural heritage data is often disseminated in the form of digital text through a variety of channels such as Wikipedia, cultural heritage archives, etc. To acquire knowledge from digital data, the extraction technique becomes an important part. However, in the case of digital text, which has characteristics such as ambiguity, complex grammar structures such as the Thai language, and others, it makes it more challenging to extract information with a high level of accuracy. We thus propose a method for improving the performance of data extraction techniques based on word features, multiple instance learning, and unseen word mapping. Word features are used to improve the quality of word definition by concatenating parts of speech (POS) and word position is used to establish the accurate definition of a word and convert all of this into a vector. In addition, we use multiple instance learning to solve issues where words do not fully express the meaning of the triple. We also cluster the particular word to find the predicate word by removing words that are irrelevant between the subject and the object. The difficulty of having a new set of words that have never been trained before can be overcome by using unseen word mapping with sub-word and nearest neighbor word mapping. We conducted several experiments on a cultural heritage knowledge graph to show the efficacy of the proposed method. The results demonstrated that our proposed technique outperforms existing models currently utilized in relation to extraction systems. It can achieve excellent accuracy since its precision, recall, and F1 score are 0.89, 0.88, and 0.89, respectively. Furthermore, it also performed well in terms of unseen word prediction, precision, recall, and F1 score, which were 0.81, 0.87, and 0.84, respectively.

Investigation and Modeling of Z-Interference in Poly-Si Channel-Based 3-D NAND Flash Memories

In this article, the Z-interference in 3-D charge trap nitride (CTN) NAND flash memory is investigated using technology computer-aided design (TCAD) simulation. In 3-D CTN NAND flash memory, Z-interference is caused by the neighbor word line (WL) programming. When a neighbor WL is programed, nitride layer-induced barrier enhancement (NIBE) and charge spreading effects in the nitride layer cause a threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{t}$ </tex-math></inline-formula>) shift in the victim WL. Also, the victim WL program state dependency of Z-interference caused by the charge spreading effect is investigated. In monocrystalline Si channel, there is a program sequence dependency due to the drain bias-induced barrier lowering (DIBL) effect. However, poly-Si channel has different characteristics because of grain boundaries. Therefore, Z-interference due to poly-Si grain boundary (GB) trap position randomness and GB trap density variation is analyzed. Finally, Z-interference is modeled using Simulation Program with Integrated Circuit Emphasis (SPICE), and the <inline-formula> <tex-math notation="LaTeX">${V}_{t}$ </tex-math></inline-formula> distribution according to the Z-interference is modeled using a Monte Carlo simulation.

A probabilistic topic model based on short distance Co-occurrences

A limitation of many probabilistic topic models such as Latent Dirichlet Allocation (LDA) is their inflexibility to use local contexts. As a result, these models cannot directly benefit from short-distance co-occurrences, which are more likely to be indicators of meaningful word relationships. Some models such as the Bigram Topic Model (BTM) consider local context by integrating language and topic models. However, due to taking the exact word order into account, such models suffer severely from sparseness. Some other models like Latent Dirichlet Co-Clustering (LDCC) try to solve the problem by adding another level of granularity assuming a document as a bag of segments, while ignoring the word order. In this paper, we introduce a new topic model which uses overlapping windows to encode local word relationships. In the proposed model, we assume a document is comprised of fixed-size overlapping windows, and formulate a new generative process accordingly. In the inference procedure, each word is sampled once in only a single window, while influencing the sampling of its other fellow co-occurring words in other windows. Word relationships are discovered in the document level, but the topic of each word is derived considering only its neighbor words in a window, to emphasize local word relationships. By using overlapping windows, without assuming an explicit dependency between adjacent words, we avoid ignoring the word order completely. The proposed model is straightforward, not severely prone to sparseness and as the experimental results show, produces more meaningful and more coherent topics compared to the three mentioned established models.